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Ivana Komunjer

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Arnaud Costinot & Dave Donaldson & Ivana Komunjer, 2010. "What Goods Do Countries Trade? A Quantitative Exploration of Ricardo's Ideas," NBER Working Papers 16262, National Bureau of Economic Research, Inc.

    Mentioned in:

    1. Whither Ricardian comparative advantage?
      by jdingel in Trade Diversion on 2011-06-08 22:40:23

Working papers

  1. Nikolay Gospodinov & Ivana Komunjer & Serena Ng, 2014. "Minimum Distance Estimation of Dynamic Models with Errors-In-Variables," FRB Atlanta Working Paper 2014-11, Federal Reserve Bank of Atlanta.

    Cited by:

    1. Meijer, Erik & Spierdijk, Laura & Wansbeek, Tom, 2017. "Consistent estimation of linear panel data models with measurement error," Journal of Econometrics, Elsevier, vol. 200(2), pages 169-180.
    2. Gospodinov, Nikolay & Komunjer, Ivana & Ng, Serena, 2017. "Simulated minimum distance estimation of dynamic models with errors-in-variables," Journal of Econometrics, Elsevier, vol. 200(2), pages 181-193.

  2. Julieta Caunedo & Riccardo DiCecio & Ivana Komunjer & Michael T. Owyang, 2013. "Asymmetry, Complementarities, and State Dependence in Federal Reserve Forecasts," Working Papers 2013-012, Federal Reserve Bank of St. Louis, revised 29 Dec 2017.

    Cited by:

    1. Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2015. "Central banks’ inflation forecasts under asymmetric loss: Evidence from four Latin-American countries," Economics Letters, Elsevier, vol. 129(C), pages 66-70.
    2. Siddhartha S. Bora & Ani L. Katchova & Todd H. Kuethe, 2021. "The Rationality of USDA Forecasts under Multivariate Asymmetric Loss," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1006-1033, May.
    3. Sinclair, Tara M. & Stekler, H.O. & Carnow, Warren, 2015. "Evaluating a vector of the Fed’s forecasts," International Journal of Forecasting, Elsevier, vol. 31(1), pages 157-164.
    4. Garratt, Anthony & Petrella, Ivan & Zhang, Yunyi, 2022. "Asymmetry and Interdependence when Evaluating U.S. Energy Information Administration Forecasts," MPRA Paper 115559, University Library of Munich, Germany.
    5. Berge, Travis J. & Chang, Andrew C. & Sinha, Nitish R., 2019. "Evaluating the conditionality of judgmental forecasts," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1627-1635.
    6. Mihaela SIMIONESCU, 2015. "The Evaluation of Global Accuracy of Romanian Inflation Rate Predictions Using Mahalanobis Distance," Management Dynamics in the Knowledge Economy, College of Management, National University of Political Studies and Public Administration, vol. 3(1), pages 133-149, March.
    7. Yoichi Tsuchiya, 2022. "Evaluating plant managers’ production plans over business cycles: asymmetric loss and rationality," SN Business & Economics, Springer, vol. 2(8), pages 1-29, August.

  3. Pierre-Andre Chiappori & Ivana Komunjer & Dennis Kristensen, 2011. "Nonparametric Identification and Estimation of Transformation Models," CAM Working Papers 2011-01, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.

    Cited by:

    1. Senay Sokullu, 2012. "Nonparametric Estimation of Semiparametric Transformation Models," Bristol Economics Discussion Papers 12/625, School of Economics, University of Bristol, UK.
    2. Botosaru, Irene & Muris, Chris & Pendakur, Krishna, 2023. "Identification of time-varying transformation models with fixed effects, with an application to unobserved heterogeneity in resource shares," Journal of Econometrics, Elsevier, vol. 232(2), pages 576-597.
    3. Ruixuan Liu, 2020. "A competing risks model with time‐varying heterogeneity and simultaneous failure," Quantitative Economics, Econometric Society, vol. 11(2), pages 535-577, May.
    4. Liyu Dou & Jakub Kastl & John Lazarev, 2020. "Quantifying Delay Externalities in Airline Networks," Working Papers 2020-65, Princeton University. Economics Department..
    5. Aureo de Paula & Imran Rasul & Pedro Souza, 2019. "Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition," Papers 1910.07452, arXiv.org, revised Oct 2023.
    6. Andrew Chesher & Adam Rosen & Konrad Smolinski, 2011. "An instrumental variable model of multiple discrete choice," CeMMAP working papers CWP39/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Bonev, Petyo, 2020. "Nonparametric identification in nonseparable duration models with unobserved heterogeneity," Economics Working Paper Series 2005, University of St. Gallen, School of Economics and Political Science.
    8. Stefan Hoderlein & Liangjun Su & Halbert White & Thomas Tao Yang, 2015. "Testing for Monotonicity in Unobservables under Unconfoundedness," Boston College Working Papers in Economics 899, Boston College Department of Economics.
    9. Centorrino, Samuele & Parmeter, Christopher F., 2024. "Nonparametric estimation of stochastic frontier models with weak separability," Journal of Econometrics, Elsevier, vol. 238(2).
    10. Christoph Breunig, 2019. "Specification Testing in Nonparametric Instrumental Quantile Regression," Papers 1909.10129, arXiv.org.
    11. Aureo de Paula & Imran Rasul & Pedro CL Souza, 2018. "Recovering social networks from panel data: Identification, simulations and an application," Documentos de Trabajo 16173, The Latin American and Caribbean Economic Association (LACEA).
    12. Lin, Yingqian & Tu, Yundong & Yao, Qiwei, 2020. "Estimation for double-nonlinear cointegration," Journal of Econometrics, Elsevier, vol. 216(1), pages 175-191.
    13. Krief, Jerome M., 2017. "Direct instrumental nonparametric estimation of inverse regression functions," Journal of Econometrics, Elsevier, vol. 201(1), pages 95-107.
    14. Gouriéroux, Christian & Monfort, Alain & Zakoian, Jean-Michel, 2017. "Pseudo-Maximum Likelihood and Lie Groups of Linear Transformations," MPRA Paper 79623, University Library of Munich, Germany.
    15. Irene Botosaru & Chris Muris, 2017. "Binarization for panel models with fixed effects," CeMMAP working papers CWP31/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Effraimidis, Georgios, 2016. "Nonparametric Identification of a Time-Varying Frailty Model," DaCHE discussion papers 2016:6, University of Southern Denmark, Dache - Danish Centre for Health Economics.
    17. Christoph Breunig & Stephan Martin, 2020. "Nonclassical Measurement Error in the Outcome Variable," Papers 2009.12665, arXiv.org, revised May 2021.
    18. Lin, Yingqian & Tu, Yundong & Yao, Qiwei, 2020. "Estimation for double-nonlinear cointegration," LSE Research Online Documents on Economics 103830, London School of Economics and Political Science, LSE Library.
    19. Arthur Lewbel & Xun Lu & Liangjun Su, 2012. "Specification Testing for Transformation Models with an Application to Generalized Accelerated Failure-time Models," Boston College Working Papers in Economics 817, Boston College Department of Economics, revised 01 May 2013.
    20. Senay Sokullu & Irene Botosaru & Chris Muris, 2022. "Time-Varying Linear Transformation Models with Fixed Effects and Endogeneity for Short Panels," Bristol Economics Discussion Papers 22/756, School of Economics, University of Bristol, UK.
    21. Nick Kloodt & Natalie Neumeyer & Ingrid Keilegom, 2021. "Specification testing in semi-parametric transformation models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 980-1003, December.
    22. Irene Botosaru & Chris Muris & Krishna Pendakur, 2020. "Intertemporal Collective Household Models: Identification in Short Panels with Unobserved Heterogeneity in Resource Shares," CeMMAP working papers CWP26/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    23. Hubner, Stefan, 2023. "Identification of unobserved distribution factors and preferences in the collective household model," Journal of Econometrics, Elsevier, vol. 234(1), pages 301-326.
    24. Kloodt, Nick, 2021. "Identification in a fully nonparametric transformation model with heteroscedasticity," Statistics & Probability Letters, Elsevier, vol. 170(C).
    25. Timo Kuosmanen & Sheng Dai, 2023. "Modeling economies of scope in joint production: Convex regression of input distance function," Papers 2311.11637, arXiv.org.
    26. Lin, Yingqian & Tu, Yundong, 2024. "Functional coefficient cointegration models with Box–Cox transformation," Economics Letters, Elsevier, vol. 234(C).
    27. Chalak, Karim, 2024. "Nonparametric Gini-Frisch bounds," Journal of Econometrics, Elsevier, vol. 238(1).
    28. Joachim Freyberger, 2021. "Normalizations and misspecification in skill formation models," Papers 2104.00473, arXiv.org, revised Jul 2022.
    29. Shosei Sakaguchi, 2024. "Partial identification and inference in duration models with endogenous censoring," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 308-326, March.
    30. Breunig, Christoph, 2016. "Specification testing in nonparametric instrumental quantile regression," SFB 649 Discussion Papers 2016-032, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

  4. Arnaud Costinot & Dave Donaldson & Ivana Komunjer, 2010. "What Goods Do Countries Trade? A Quantitative Exploration of Ricardo's Ideas," NBER Working Papers 16262, National Bureau of Economic Research, Inc.

    Cited by:

    1. Sandra Poncet & Felipe Starosta de Waldemar, 2014. "Product relatedness and firm exports in China," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01044982, HAL.
    2. Jerónimo Carballo & Ignacio Marra de Artiñano & Christian Volpe Martincus, 2021. "Information Frictions, Investment Promotion, and Multinational Production: Firm-Level Evidence," CESifo Working Paper Series 9043, CESifo.
    3. Gordeev, Roman, 2020. "Comparative advantages of Russian forest products on the global market," Forest Policy and Economics, Elsevier, vol. 119(C).
    4. Schetter, Ulrich & Diodato, Dario & Protzer, Eric & Neffke, Frank & Hausmann, Ricardo, 2024. "From Products to Capabilities: Constructing a Genotypic Product Space," CEPR Discussion Papers 19369, C.E.P.R. Discussion Papers.
    5. Stephen J. Redding & David E. Weinstein, 2018. "Aggregating From Micro to Macro Patterns of Trade," Working Papers 18-10, Center for Economic Studies, U.S. Census Bureau.
    6. Rodrigo Adao & Arnaud Costinot & Dave Donaldson, 2017. "Nonparametric Counterfactual Predictions in Neoclassical Models of International Trade," American Economic Review, American Economic Association, vol. 107(3), pages 633-689, March.
    7. Andrei A Levchenko & Jing Zhang, 2013. "The Global Labor Market Impact of Emerging Giants: A Quantitative Assessment," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 61(3), pages 479-519, August.
    8. Bahar, Dany & Rosenow, Samuel & Stein, Ernesto & Wagner, Rodrigo Andres, 2018. "Export Take-Offs and Acceleration: Unpacking Cross-Sector Linkages in the Evolution of Comparative Advantage," IZA Discussion Papers 12061, Institute of Labor Economics (IZA).
    9. Sam Kortum & John Romalis & Brent Neiman & Jonathan Eaton, 2010. "Trade and the Global Recession," 2010 Meeting Papers 1340, Society for Economic Dynamics.
    10. Ulrich Schetter, 2019. "A Structural Ranking of Economic Complexity," Growth Lab Working Papers 148, Harvard's Growth Lab.
    11. Pierre-Daniel Sarte & Fernando Parro & Esteban Rossi-Hansberg & Lorenzo Caliendo, 2014. "The Impact of Regional and Sectoral Productivity Changes on the U.S. Economy," 2014 Meeting Papers 426, Society for Economic Dynamics.
    12. William R. Kerr, 2013. "Heterogeneous Technology Diffusion and Ricardian Trade Patterns," Harvard Business School Working Papers 14-039, Harvard Business School.
    13. Scott French, 2017. "Comparative Advantage and Biased Gravity," Discussion Papers 2017-03, School of Economics, The University of New South Wales.
    14. Juyoung Cheong & Shino Takayama & Terence Yeo, 2013. "Preferential Trade Agreements and Welfare: General Equilibrium Analysis," Discussion Papers Series 482, School of Economics, University of Queensland, Australia.
    15. Redding, Stephen & Weinstein, David, 2019. "Aggregation and the Gravity Equation," CEPR Discussion Papers 13459, C.E.P.R. Discussion Papers.
    16. Markus Brueckner & Ngo Van Long & Joaquin Vespignani, 2020. "Non-gravity trade," CAMA Working Papers 2020-52, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    17. Rudik, Ivan & Lyn, Gary & Tan, Weiliang & Ortiz-Bobea, Ariel, 2021. "Heterogeneity and Market Adaptation to Climate Change in Dynamic-Spatial Equilibrium," ISU General Staff Papers 202106020700001127, Iowa State University, Department of Economics.
    18. Mayer, Thierry & Bas, Maria, 2015. "From Micro to Macro: Demand, Supply, and Heterogeneity in the Trade Elasticity," CEPR Discussion Papers 10637, C.E.P.R. Discussion Papers.
    19. Andrianarimanana, Mihasina Harinaivo & Yongjian, Pu & Rabezanahary Tanteliniaina, Mirindra Finaritra, 2023. "Assessment of the importance of climate, land, and soil on the global supply for agricultural products and global food security: Evidence from Madagascar," Food Policy, Elsevier, vol. 115(C).
    20. Costinot, Arnaud & Rodríguez-Clare, Andrés, 2014. "Trade Theory with Numbers: Quantifying the Consequences of Globalization," Handbook of International Economics, in: Gopinath, G. & Helpman, . & Rogoff, K. (ed.), Handbook of International Economics, edition 1, volume 4, chapter 0, pages 197-261, Elsevier.
    21. Stephen J. Redding & David E. Weinstein, 2018. "Accounting for Trade Patterns," Working Papers 2018-10, Princeton University. Economics Department..
    22. Benny Kleinman & Ernest Liu & Stephen J. Redding, 2023. "Dynamic Spatial General Equilibrium," Econometrica, Econometric Society, vol. 91(2), pages 385-424, March.
    23. Nelson Lind & Natalia Ramondo, 2018. "Trade with Correlation," NBER Working Papers 24380, National Bureau of Economic Research, Inc.
    24. Pessoa, João Paulo, 2016. "International competition and labor market adjustment," LSE Research Online Documents on Economics 66426, London School of Economics and Political Science, LSE Library.
    25. Larch, Mario & Tan, Shawn & Yotov, Yoto, 2021. "A simple method to quantify the ex-ante effects of “deep” trade liberalization and “hard” trade protection," School of Economics Working Paper Series 2021-14, LeBow College of Business, Drexel University.
    26. Redding, Stephen J., 2016. "Goods trade, factor mobility and welfare," Journal of International Economics, Elsevier, vol. 101(C), pages 148-167.
    27. Mariano A. Somale, 2017. "Comparative Advantage in Innovation and Production," International Finance Discussion Papers 1206, Board of Governors of the Federal Reserve System (U.S.).
    28. Clémence Lenoir & Julien Martin & Isabelle Mejean, 2023. "Search Frictions in International Goods Markets," Journal of the European Economic Association, European Economic Association, vol. 21(1), pages 326-366.
    29. Theresa Greaney & Kozo Kiyota, 2020. "The Gravity Model and Trade in Intermediate Inputs," Keio-IES Discussion Paper Series 2020-002, Institute for Economics Studies, Keio University.
    30. Benedikt Heid & Frank Stähler, 2024. "Disentangling Frictions Across the World: Markups Versus Trade Costs," CESifo Working Paper Series 11420, CESifo.
    31. Bolatto, Stefano & Sbracia, Massimo, 2014. "Deconstructing the Gains from Trade: Selection of Industries vs. Reallocation of Workers," MPRA Paper 56638, University Library of Munich, Germany.
    32. Scott L. Baier & Amanda Kerr & Yoto V. Yotov, 2018. "Gravity, distance, and international trade," Chapters, in: Bruce A. Blonigen & Wesley W. Wilson (ed.), Handbook of International Trade and Transportation, chapter 2, pages 15-78, Edward Elgar Publishing.
    33. Amit Khandelwal & Pablo Fajgelbaum, 2013. "Measuring the Unequal Gains From Trade," 2013 Meeting Papers 456, Society for Economic Dynamics.
    34. Markus Brueckner & Ngo Van Long & Joaquin Vespignani & Ngo Van Long, 2020. "Trade, Education, and Income Inequality," CESifo Working Paper Series 8370, CESifo.
    35. Keith Head & Thierry Mayer, 2013. "Gravity Equations: Workhorse,Toolkit, and Cookbook," Working Papers 2013-27, CEPII research center.
    36. Julian di Giovanni & Andrei A. Levchenko, 2011. "The Risk Content of Exports: A Portfolio View of International Trade," NBER Chapters, in: NBER International Seminar on Macroeconomics 2011, pages 97-151, National Bureau of Economic Research, Inc.
    37. Rebecca Freeman & Mario Larch & Angelos Theodorakopoulos & Yoto V. Yotov, 2021. "Unlocking New Methods to Estimate Country-Specific Trade Costs and Trade Elasticities," CESifo Working Paper Series 9432, CESifo.
    38. Ariu, Andrea & Müller, Tobias & Nguyen, Tuan, 2023. "Immigration and the Slope of the Labor Demand Curve: The Role of Firm Heterogeneity in a Model of Regional Labor Markets," CEPR Discussion Papers 18091, C.E.P.R. Discussion Papers.
    39. Redding, Stephen & Kleinman, Benny & Liu, Ernest, 2020. "International Friends and Enemies," CEPR Discussion Papers 15068, C.E.P.R. Discussion Papers.
    40. Yotov, Yoto, 2024. "The Evolution of Structural Gravity: The Workhorse Model of Trade," School of Economics Working Paper Series 2024-10, LeBow College of Business, Drexel University.
    41. Elizaveta Archanskaia & Guillaume Daudin, 2017. "Heterogeneity and the distance puzzle," SciencePo Working papers Main hal-01496258, HAL.
    42. Mario Larch & Jeff Luckstead & Yoto V. Yotov, 2021. "Economic Sanctions and Agricultural Trade," CESifo Working Paper Series 9410, CESifo.
    43. Robert-Nicoud, Frédéric & Olarreaga, Marcelo & Carrère, Céline & Fugazza, Marco, 2016. "On the heterogeneous effect of trade on unemployment," CEPR Discussion Papers 11540, C.E.P.R. Discussion Papers.
    44. Caron, Justin & Fally, Thibault & Markusen, James, 2020. "Per capita income and the demand for skills," Journal of International Economics, Elsevier, vol. 123(C).
    45. Christian Hepenstrick, 2010. "Per-capita incomes and the extensive margin of bilateral trade," IEW - Working Papers 519, Institute for Empirical Research in Economics - University of Zurich.
    46. Artuc, Erhan & Porto, Guido & Rijkers, Bob, 2019. "Trading off the income gains and the inequality costs of trade policy," Journal of International Economics, Elsevier, vol. 120(C), pages 1-45.
    47. Ferdinando Monte, 2014. "Local Transmission of Trade Shocks," Working Papers 2014-001, Human Capital and Economic Opportunity Working Group.
    48. Bombardini, Matilde & Li, Bingjing, 2020. "Trade, pollution and mortality in China," Journal of International Economics, Elsevier, vol. 125(C).
    49. Jian Han & Yanzhi Shen, 2016. "Exchange Rate Pass-through to China's Export Price: A Product-level Investigation," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 24(2), pages 48-67, March.
    50. Giorgia Giovannetti & Mauro Lanati, 2017. "Do High-Skill Immigrants trigger High-Quality Trade?," The World Economy, Wiley Blackwell, vol. 40(7), pages 1345-1380, July.
    51. Ariel Burstein & Javier Cravino & Jonathan Vogel, 2013. "Importing Skill-Biased Technology," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 32-71, April.
    52. Jerónimo Carballo & Alejandro Graziano & Georg Schaur & Christian Volpe Martincus, 2021. "Import Processing and Trade Costs," CESifo Working Paper Series 9170, CESifo.
    53. F. Langot & L. Patureau & T. Sopraseuth, 2014. "Fiscal Devaluation and Structural Gaps," Working papers 508, Banque de France.
    54. Johannes Boehm, 2017. "The Impact of Contract Enforcement Costs on Outsourcing and Aggregate Productivity," 2017 Meeting Papers 801, Society for Economic Dynamics.
    55. Bolatto, Stefano & Moramarco, Graziano, 2023. "Gains from trade and their quantification: Does sectoral disaggregation matter?," International Economics, Elsevier, vol. 174(C), pages 44-68.
    56. Heitor Pellegrina & Sebastian Sotelo, 2019. "Migration, Specialization and Trade: Evidence from the Brazilian March to the West," 2019 Meeting Papers 863, Society for Economic Dynamics.
    57. Eddy Bekkers & Joseph F. Francois & Hugo Rojas†Romagosa, 2018. "Melting Ice Caps and the Economic Impact of Opening the Northern Sea Route," Economic Journal, Royal Economic Society, vol. 128(610), pages 1095-1127, May.
    58. Larch, Mario & Yotov, Yoto, 2016. "General Equilibrium Trade Policy Analysis with Structural Gravity," School of Economics Working Paper Series 2016-9, LeBow College of Business, Drexel University.
    59. Thomas Sampson, 2019. "Technology Gaps, Trade and Income," CESifo Working Paper Series 7714, CESifo.
    60. Laurence Wicht, 2020. "The margin of importing sectors in the gains from trade," Working Papers 2020-07, Swiss National Bank.
    61. Eric O'N. Fisher & John Gilbert & Kathryn G. Marshall & Reza Oladi, 2015. "A New Measure of Economic Distance," CESifo Working Paper Series 5362, CESifo.
    62. Benedikt Heid & Frank Stähler, 2020. "Structural Gravity and the Gains from Trade under Imperfect Competition: Quantifying the Effects of the European Single Market," CESifo Working Paper Series 8121, CESifo.
    63. Rodrigo Adao & Costas Arkolakis & Sharat Ganapati, 2020. "Aggregate Implications of Firm Heterogeneity: A Nonparametric Analysis of Monopolistic Competition Trade Models," Cowles Foundation Discussion Papers 2265, Cowles Foundation for Research in Economics, Yale University.
    64. Lionel Fontagné & Philippe Martin & Gianluca Orefice, 2018. "The International Elasticity Puzzle Is Worse Than You Think," SciencePo Working papers Main hal-01870896, HAL.
    65. Stefano Bolatto, "undated". "Trade across Countries and Manufacturing Sectors with Heterogeneous Trade Elasticities," Development Working Papers 360, Centro Studi Luca d'Agliano, University of Milano.
    66. Dany Bahar & Andreas Hauptmann & Cem Özgüzel & Hillel Rapoport, 2019. "Migration and Post-conflict Reconstruction: The Effect of Returning Refugees on Export Performance in the Former Yugoslavia," Growth Lab Working Papers 149, Harvard's Growth Lab.
    67. Trevor Tombe, 2010. "The Missing Food Problem: How Low Agricultural Imports Contribute to International Income and Productivity Differences," Working Papers tecipa-416, University of Toronto, Department of Economics.
    68. Mario Larch & Yoto Yotov, 2017. "On the impact of TTIP in Southeastern and Eastern Europe: A quantitative analysis," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 3, pages 54-73,74-92.
    69. Xuebing Yang, 2015. "Estimating Distribution Costs with the Eaton–Kortum Model," Review of Development Economics, Wiley Blackwell, vol. 19(3), pages 653-665, August.
    70. Dany Bahar & Rodrigo Wagner & Ernesto Stein & Samuel Rosenow, 2017. "The Birth and Growth of New Export Clusters: Which Mechanisms Drive Diversification?," Growth Lab Working Papers 103, Harvard's Growth Lab.
    71. Martin Grančay & Tomáš Dudáš, 2019. "Bilateral trade flows and comparative advantage: does the size matter?," Society and Economy, Akadémiai Kiadó, Hungary, vol. 41(4), pages 397-413, December.
    72. Jie Cai & Nan Li & Ana Maria Santacreu, 2022. "Knowledge Diffusion, Trade, and Innovation across Countries and Sectors," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(1), pages 104-145, January.
    73. Mario Larch & Serge Shikher & Constantinos Syropoulos & Yoto V. Yotov, 2022. "Quantifying the impact of economic sanctions on international trade in the energy and mining sectors," Economic Inquiry, Western Economic Association International, vol. 60(3), pages 1038-1063, July.
    74. Levchenko, Andrei & Bartelme, Dominick & Lan, Ting, 2020. "Specialization, Market Access and Real Income," CEPR Discussion Papers 15604, C.E.P.R. Discussion Papers.
    75. Lirong Liu & Hiranya K. Nath & Kiril Tochkov, 2015. "Comparative Advantages in U. S. Bilateral Services Trade with China and India," Working Papers 1501, Sam Houston State University, Department of Economics and International Business.
    76. Lind, Nelson & Ramondo, Natalia, 2022. "Global Innovation and Knowledge Diffusion," CEPR Discussion Papers 17385, C.E.P.R. Discussion Papers.
    77. James E. Anderson & Penglong Zhang, 2020. "Latent Exports: Almost Ideal Gravity and Zeros," Boston College Working Papers in Economics 1020, Boston College Department of Economics.
    78. Georg Schneider & Frank Stähler & Georg U. Thunecke, 2022. "The (Non-)Neutrality of Value-Added Taxation," CESifo Working Paper Series 9663, CESifo.
    79. Baldwin, Richard & Dingel, Jonathan, 2021. "Telemigration and development: On the offshorability of teleworkable jobs," CEPR Discussion Papers 16641, C.E.P.R. Discussion Papers.
    80. Valeriano Martínez-San Román & Marta Bengoa & Blanca Sánchez-Robles, 2016. "Foreign direct investment, trade integration and the home bias: evidence from the European Union," Empirical Economics, Springer, vol. 50(1), pages 197-229, February.
    81. Guy Michaels & Ferdinand Rauch & Stephen J Redding, 2019. "Task Specialization in U.S. Cities from 1880 to 2000," Journal of the European Economic Association, European Economic Association, vol. 17(3), pages 754-798.
    82. Juan de Lucio & Raúl Mínguez & Asier Minondo & Francisco Requena, 2017. "The granularity of Spanish exports," Working Papers 1701, Department of Applied Economics II, Universidad de Valencia.
    83. Ding Nan & Pomi Shahbaz & Shamsheer ul Haq & Muhammad Nadeem & Muhammad Imran, 2023. "The Economies’ Ability to Produce Diversified and Complex Goods to Meet the Global Competition: Role of Gross Value Chain, Institutional Quality, and Human Capital," Sustainability, MDPI, vol. 15(8), pages 1-17, April.
    84. de Sousa, J. & Mayer, T. & Zignago, S., 2011. "Market access in global and regional trade," Working papers 358, Banque de France.
    85. Vasilii Erokhin & Gao Tianming & Anna Ivolga, 2021. "Cross-Country Potentials and Advantages in Trade in Fish and Seafood Products in the RCEP Member States," Sustainability, MDPI, vol. 13(7), pages 1-40, March.
    86. Caron, Justin & Fally, Thibault, 2018. "Per Capita Income, Consumption Patterns, and CO2 Emissions," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt0n98j4z7, Department of Agricultural & Resource Economics, UC Berkeley.
    87. Sebastian Sotelo, 2020. "Domestic Trade Frictions and Agriculture," Journal of Political Economy, University of Chicago Press, vol. 128(7), pages 2690-2738.
    88. Laurence Wicht, 2020. "A multi-sector analysis of Switzerland's gains from trade," Working Papers 2020-20, Swiss National Bank.
    89. Lionel Fontagné & Gianluca Santoni, 2021. "GVCs and the endogenous geography of RTAs," Post-Print halshs-03956355, HAL.
    90. Larch, Mario & Tan, Shawn W. & Yotov, Yoto V., 2023. "A simple method to ex-ante quantify the unobservable effects of trade liberalization and trade protection," Journal of Comparative Economics, Elsevier, vol. 51(4), pages 1200-1213.
    91. Dongin Kim & Sandro Steinbach, 2024. "The Linder hypothesis for foreign direct investment revisited," Review of International Economics, Wiley Blackwell, vol. 32(4), pages 1901-1928, September.
    92. Scott French, 2014. "The Composition of Trade Flows and the Aggregate Effects of Trade Barriers," Discussion Papers 2014-36, School of Economics, The University of New South Wales.
    93. Gnidchenko, A., 2014. "Improving the Methods for Estimating the Structure and the Basis of Export Potential through Export Diversification," Journal of the New Economic Association, New Economic Association, vol. 21(1), pages 83-109.
    94. Konstantakopoulou, Ioanna & Tsionas, Mike G., 2019. "Measuring comparative advantages in the Euro Area," Economic Modelling, Elsevier, vol. 76(C), pages 260-269.
    95. Konstantin Kucheryavyy & Gary Lyn & Andrés Rodríguez-Clare, 2016. "Grounded by Gravity: A Well-Behaved Trade Model with Industry-Level Economies of Scale," NBER Working Papers 22484, National Bureau of Economic Research, Inc.
    96. Gianluca Orefice & Hillel Rapoport & Gianluca Santoni, 2021. "How Do Immigrants Promote Exports?," Working Papers 2021-06, CEPII research center.
    97. Orefice, Gianluca & Rapoport, Hillel & Santoni, Gianluca, 2022. "How Do Immigrants Promote Exports? Networks, Knowledge, Diversity," IZA Discussion Papers 15722, Institute of Labor Economics (IZA).
    98. Federico Esposito, 2017. "Entrepreneurial Risk and Diversification through Trade," Working Papers w201714, Banco de Portugal, Economics and Research Department.
    99. Kebede, Hundanol A., 2024. "Gains from market integration: Welfare effects of new rural roads in Ethiopia," Journal of Development Economics, Elsevier, vol. 168(C).
    100. Scott French, 2017. "Revealed Comparative Advantage: What Is it Good For?," Discussion Papers 2014-39B, School of Economics, The University of New South Wales.
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    Cited by:

    1. Giacomini, Raffaella & Ragusa, Giuseppe, 2014. "Theory-coherent forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 145-155.

  6. Komunjer, Ivana & Santos, Andres, 2009. "Semiparametric Estimation of Nonseparable Models: A Minimum Distance from Independence Approach," University of California at San Diego, Economics Working Paper Series qt32k957bp, Department of Economics, UC San Diego.

    Cited by:

    1. Poirier, Alexandre, 2017. "Efficient estimation in models with independence restrictions," Journal of Econometrics, Elsevier, vol. 196(1), pages 1-22.
    2. Torgovitsky, Alexander, 2017. "Minimum distance from independence estimation of nonseparable instrumental variables models," Journal of Econometrics, Elsevier, vol. 199(1), pages 35-48.
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    4. Florian Gunsilius, 2018. "Point-identification in multivariate nonseparable triangular models," Papers 1806.09680, arXiv.org.
    5. Liangjun Su & Stefan Hoderlein & Halbert White, 2013. "Testing Monotonicity in Unobservables with Panel Data," Boston College Working Papers in Economics 892, Boston College Department of Economics, revised 01 Feb 2016.

  7. Komunjer, Ivana, 2008. "Global Identification In Nonlinear Semiparametric Models," University of California at San Diego, Economics Working Paper Series qt2r59d87f, Department of Economics, UC San Diego.

    Cited by:

    1. Komunjer, Ivana, 2008. "Global Identification of the Semiparametric Box-Cox Model," University of California at San Diego, Economics Working Paper Series qt97s197d4, Department of Economics, UC San Diego.
    2. Chiappori, Pierre-Andre & Komunjer, Ivana, 2008. "Correct Specification and Identification of Nonparametric Transformation Models," University of California at San Diego, Economics Working Paper Series qt4v12m2rg, Department of Economics, UC San Diego.

  8. Komunjer, Ivana & OWYANG, MICHAEL, 2007. "Multivariate Forecast Evaluation And Rationality Testing," University of California at San Diego, Economics Working Paper Series qt81w8m5sf, Department of Economics, UC San Diego.

    Cited by:

    1. Michael P. Clements, 2022. "Forecaster Efficiency, Accuracy, and Disagreement: Evidence Using Individual‐Level Survey Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(2-3), pages 537-568, March.
    2. Jens J. Krüger, 2014. "A multivariate evaluation of German output growth and inflation forecasts," Economics Bulletin, AccessEcon, vol. 34(3), pages 1410-1418.
    3. Andrew Martinez, 2017. "Testing for Differences in Path Forecast Accuracy: Forecast-Error Dynamics Matter," Working Papers (Old Series) 1717, Federal Reserve Bank of Cleveland.
    4. Ouysse, Rachida, 2016. "Bayesian model averaging and principal component regression forecasts in a data rich environment," International Journal of Forecasting, Elsevier, vol. 32(3), pages 763-787.
    5. Mamatzakis, Emmanuel & Tsionas, Mike G., 2015. "How are market preferences shaped? The case of sovereign debt of stressed euro-area countries," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 106-116.
    6. Fabiana Gomez & David Pacini, 2015. "Counting Biased Forecasters: An Application of Multiple Testing Techniques," Bristol Economics Discussion Papers 15/661, School of Economics, University of Bristol, UK.
    7. Jingwei Pan, 0000. "Evaluating Correlation Forecasts Under Asymmetric Loss," Proceedings of Economics and Finance Conferences 11413234, International Institute of Social and Economic Sciences.
    8. Andrea BASTIANIN & Marzio GALEOTTI & Matteo MANERA, 2011. "Forecast evaluation in call centers: combined forecasts, flexible loss functions and economic criteria," Departmental Working Papers 2011-08, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    9. An, Zidong & Zheng, Xinye, 2023. "Diligent forecasters can make accurate predictions despite disagreeing with the consensus," Economic Modelling, Elsevier, vol. 125(C).
    10. Siddhartha S. Bora & Ani L. Katchova & Todd H. Kuethe, 2021. "The Rationality of USDA Forecasts under Multivariate Asymmetric Loss," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1006-1033, May.
    11. Gloria Gonzalez-Rivera & Yun Luo & Esther Ruiz, 2019. "Prediction Regions for Interval-valued Time Series," Working Papers 201921, University of California at Riverside, Department of Economics.
    12. Andrea Bastianin & Marzio Galeotti & Matteo Manera, 2017. "Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers," Working Papers 2017.06, Fondazione Eni Enrico Mattei.
    13. Sinclair, Tara M. & Stekler, H.O. & Carnow, Warren, 2015. "Evaluating a vector of the Fed’s forecasts," International Journal of Forecasting, Elsevier, vol. 31(1), pages 157-164.
    14. Garratt, Anthony & Petrella, Ivan & Zhang, Yunyi, 2022. "Asymmetry and Interdependence when Evaluating U.S. Energy Information Administration Forecasts," MPRA Paper 115559, University Library of Munich, Germany.
    15. Tara M. Sinclair & Hans Christian Müller-Dröge & Herman Stekler, 2014. "Evaluating Forecasts of a Vector of Variables: A German Forecasting Competition," Working Papers 2014-17, The George Washington University, Institute for International Economic Policy.
    16. Olga Isengildina‐Massa & Berna Karali & Todd H. Kuethe & Ani L. Katchova, 2021. "Joint Evaluation of the System of USDA's Farm Income Forecasts," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(3), pages 1140-1160, September.
    17. Todd E. Clark & Michael W. McCracken, 2013. "Evaluating the accuracy of forecasts from vector autoregressions," Working Papers 2013-010, Federal Reserve Bank of St. Louis.
    18. Emmanuel C. Mamatzakis & Mike G. Tsionas, 2020. "Revealing forecaster's preferences: A Bayesian multivariate loss function approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 412-437, April.
    19. Travis J. Berge, 2017. "Understanding Survey Based Inflation Expectations," Finance and Economics Discussion Series 2017-046, Board of Governors of the Federal Reserve System (U.S.).
    20. Wang, Yiyao & Lee, Tae-Hwy, 2014. "Asymmetric loss in the Greenbook and the Survey of Professional Forecasters," International Journal of Forecasting, Elsevier, vol. 30(2), pages 235-245.
    21. Lin, Wei & González-Rivera, Gloria, 2016. "Interval-valued time series models: Estimation based on order statistics exploring the Agriculture Marketing Service data," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 694-711.
    22. Gloria Gonzalez-Rivera & Wei Lin, 2014. "Interval-valued Time Series: Model Estimation based on Order Statistics," Working Papers 201429, University of California at Riverside, Department of Economics.
    23. Ulu, Yasemin, 2013. "Multivariate test for forecast rationality under asymmetric loss functions: Recent evidence from MMS survey of inflation–output forecasts," Economics Letters, Elsevier, vol. 119(2), pages 168-171.
    24. Berge, Travis J. & Chang, Andrew C. & Sinha, Nitish R., 2019. "Evaluating the conditionality of judgmental forecasts," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1627-1635.
    25. Sinclair, Tara M. & Gamber, Edward N. & Stekler, Herman & Reid, Elizabeth, 2012. "Jointly evaluating the Federal Reserve’s forecasts of GDP growth and inflation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 309-314.
    26. Wei Lin & Gloria González‐Rivera, 2019. "Extreme returns and intensity of trading," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1121-1140, November.
    27. Alp, Tansel & Demetrescu, Matei, 2010. "Joint forecasts of Dow Jones stocks under general multivariate loss function," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2360-2371, November.
    28. Tae-Hwy Lee & Tao Wang, 2023. "Estimation and Testing of Forecast Rationality with Many Moments," Working Papers 202307, University of California at Riverside, Department of Economics.
    29. Michael Clements, 2016. "Are Macro-Forecasters Essentially The Same? An Analysis of Disagreement, Accuracy and Efficiency," ICMA Centre Discussion Papers in Finance icma-dp2016-08, Henley Business School, University of Reading.
    30. Christodoulakis, George, 2020. "Estimating the term structure of commodity market preferences," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1146-1163.
    31. Yoichi Tsuchiya, 2022. "Evaluating plant managers’ production plans over business cycles: asymmetric loss and rationality," SN Business & Economics, Springer, vol. 2(8), pages 1-29, August.
    32. Julieta Caunedo & Riccardo DiCecio & Ivana Komunjer & Michael T. Owyang, 2013. "Asymmetry, Complementarities, and State Dependence in Federal Reserve Forecasts," Working Papers 2013-012, Federal Reserve Bank of St. Louis, revised 29 Dec 2017.
    33. Dovern, Jonas, 2015. "A multivariate analysis of forecast disagreement: Confronting models of disagreement with survey data," European Economic Review, Elsevier, vol. 80(C), pages 16-35.
    34. Fresoli, Diego & Ruiz, Esther & Pascual, Lorenzo, 2015. "Bootstrap multi-step forecasts of non-Gaussian VAR models," International Journal of Forecasting, Elsevier, vol. 31(3), pages 834-848.
    35. Boskabadi, Elahe, 2022. "Economic policy uncertainty and forecast bias in the survey of professional forecasters," MPRA Paper 115081, University Library of Munich, Germany.
    36. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    37. Kexin Ding & Ani L. Katchova, 2024. "Testing the optimality of USDA's WASDE forecasts under unknown loss," Agribusiness, John Wiley & Sons, Ltd., vol. 40(4), pages 846-865, October.

  9. Komunjer, Ivana & Vuong, Quang, 2006. "Efficientt Conditional Quantile Estimation: The Time Series Case," University of California at San Diego, Economics Working Paper Series qt78842570, Department of Economics, UC San Diego.

    Cited by:

    1. Marc Hallin & Catherine Vermandele & Bas J. M. Werker, 2008. "Semiparametrically efficient inference based on signs and ranks for median‐restricted models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 389-412, April.
    2. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    3. Manganelli, Simone & White, Halbert & Kim, Tae-Hwan, 2008. "Modeling autoregressive conditional skewness and kurtosis with multi-quantile CAViaR," Working Paper Series 957, European Central Bank.
    4. Habert white & Tae-Hwan Kim & Simone Manganelli, 2012. "VAR for VaR: Measuring Tail Dependence Using Multivariate Regression Quantiles," Working papers 2012rwp-45, Yonsei University, Yonsei Economics Research Institute.
    5. White, Halbert & Kim, Tae-Hwan & Manganelli, Simone, 2010. "VAR for VaR: measuring systemic risk using multivariate regression quantiles," MPRA Paper 35372, University Library of Munich, Germany.
    6. Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
    7. Komunjer, Ivana & Vuong, Quang, 2010. "Efficient estimation in dynamic conditional quantile models," Journal of Econometrics, Elsevier, vol. 157(2), pages 272-285, August.

  10. Costinot, Arnaud & Komunjer, Ivana, 2006. "What Goods Do Countries Trade? New Ricardian Predictions," University of California at San Diego, Economics Working Paper Series qt86n316hw, Department of Economics, UC San Diego.

    Cited by:

    1. Antti SIMOLA & Jouko KINNUNEN & Hannu TÖRMÄ & Jukka KOLA, 2010. "Bioenergy Production in Finland and its Effects on Regional Growth and Employment," EcoMod2010 259600157, EcoMod.
    2. Chen, Natalie & Novy, Dennis, 2008. "International Trade Integration: A Disaggregated Approach," CEPR Discussion Papers 7103, C.E.P.R. Discussion Papers.
    3. Morrow, Peter M., 2010. "Ricardian-Heckscher-Ohlin comparative advantage: Theory and evidence," Journal of International Economics, Elsevier, vol. 82(2), pages 137-151, November.
    4. Costas Arkolakis & Arnaud Costinot & Andres Rodriguez-Clare, 2012. "New Trade Models, Same Old Gains?," American Economic Review, American Economic Association, vol. 102(1), pages 94-130, February.
    5. Christian Hepenstrick, 2010. "Per-capita incomes and the extensive margin of bilateral trade," IEW - Working Papers 519, Institute for Empirical Research in Economics - University of Zurich.
    6. Cheng‐Te Lee & Shang‐Fen Wu, 2023. "Technology advantage, terms of trade, and pattern of trade," International Journal of Economic Theory, The International Society for Economic Theory, vol. 19(1), pages 166-174, March.
    7. Matilde Bombardini & Giovanni Gallipoli & Germán Pupato, 2009. "Skill Dispersion and Trade Flows," NBER Working Papers 15097, National Bureau of Economic Research, Inc.
    8. Costinot, Arnaud, 2009. "On the origins of comparative advantage," Journal of International Economics, Elsevier, vol. 77(2), pages 255-264, April.
    9. James E. Anderson, 2008. "Gravity, Productivity and the Pattern of Production and Trade," Boston College Working Papers in Economics 700, Boston College Department of Economics.
    10. James E. Anderson & Yoto V. Yotov, 2010. "Specialization: Pro- and Anti-globalizing, 1990-2002," NBER Working Papers 16301, National Bureau of Economic Research, Inc.
    11. Sergei Koulayev, 2009. "Estimating demand in search markets: the case of online hotel bookings," Working Papers 09-16, Federal Reserve Bank of Boston.
    12. Arnaud Costinot, 2009. "An Elementary Theory of Comparative Advantage," Econometrica, Econometric Society, vol. 77(4), pages 1165-1192, July.
    13. Chor, Davin, 2010. "Unpacking sources of comparative advantage: A quantitative approach," Journal of International Economics, Elsevier, vol. 82(2), pages 152-167, November.
    14. Amoroso, Nicolás & Chiquiar, Daniel & Ramos-Francia, Manuel, 2011. "Technology and endowments as determinants of comparative advantage: Evidence from Mexico," The North American Journal of Economics and Finance, Elsevier, vol. 22(2), pages 164-196, August.
    15. Shikher, Serge, 2011. "Capital, technology, and specialization in the neoclassical model," Journal of International Economics, Elsevier, vol. 83(2), pages 229-242, March.

  11. Ivana Komunjer, 2004. "Asymmetric Power Distribution: Theory and Applications to Risk Measurement," Econometric Society 2004 Latin American Meetings 44, Econometric Society.

    Cited by:

    1. Li, Xiao-Ming & Rose, Lawrence C., 2009. "The tail risk of emerging stock markets," Emerging Markets Review, Elsevier, vol. 10(4), pages 242-256, December.
    2. Bao, Te & Diks, Cees & Li, Hao, 2018. "A generalized CAPM model with asymmetric power distributed errors with an application to portfolio construction," Economic Modelling, Elsevier, vol. 68(C), pages 611-621.
    3. J. Miguel Marin & Genaro Sucarrat, 2015. "Financial density selection," The European Journal of Finance, Taylor & Francis Journals, vol. 21(13-14), pages 1195-1213, November.
    4. ZHU, Dongming & ZINDE-WALSH, Victoria, 2007. "Properties and Estimation of Asymmetric Exponential Power Distribution," Cahiers de recherche 13-2007, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    5. Yong Bao & Aman Ullah, 2009. "Expectation of Quadratic Forms in Normal and Nonnormal Variables with Econometric Applications," Working Papers 200907, University of California at Riverside, Department of Economics, revised Jun 2009.
    6. Fischer, Matthias, 2012. "A skew and leptokurtic distribution with polynomial tails and characterizing functions in closed form," FAU Discussion Papers in Economics 03/2012, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    7. Lu Ou & Zhibiao Zhao, 2021. "Value‐at‐risk forecasting via dynamic asymmetric exponential power distributions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 291-300, March.
    8. Ruediger Bachmann & Kai Carstensen & Stefan Lautenbacher & Martin Schneider, 2021. "Uncertainty and Change: Survey Evidence of Firms' Subjective Beliefs," NBER Working Papers 29430, National Bureau of Economic Research, Inc.
    9. Vijverberg, Wim P. & Hasebe, Takuya, 2015. "GTL Regression: A Linear Model with Skewed and Thick-Tailed Disturbances," IZA Discussion Papers 8898, Institute of Labor Economics (IZA).
    10. Lee, Dong Jin & Kim, Tae-Hwan & Mizen, Paul, 2021. "Impulse response analysis in conditional quantile models with an application to monetary policy," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    11. Jin-Ray Lu & Chiang-Chang Hwang & Yi-Chun Chen & Chu-Ting Wen, 2013. "Including More Information Content to Enhance the Value at Risk Estimation for Real Estate Investment Trusts," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 4(3), pages 25-34, July.
    12. Yu, Dalei & Bai, Peng & Ding, Chang, 2015. "Adjusted quasi-maximum likelihood estimator for mixed regressive, spatial autoregressive model and its small sample bias," Computational Statistics & Data Analysis, Elsevier, vol. 87(C), pages 116-135.
    13. Demetrescu, Matei & Hacioglu Hoke, Sinem, 2018. "Predictive regressions under asymmetric loss: factor augmentation and model selection," Bank of England working papers 723, Bank of England.
    14. Bonato, Matteo & Gupta, Rangan & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Moments-based spillovers across gold and oil markets," Energy Economics, Elsevier, vol. 89(C).
    15. Liu, Xiaochun, 2019. "On tail fatness of macroeconomic dynamics," Journal of Macroeconomics, Elsevier, vol. 62(C).
    16. Mendoza, Alfonso. & Galvanovskis, Evalds., 2014. "La cópula GED bivariada. Una aplicación en entornos de crisis," El Trimestre Económico, Fondo de Cultura Económica, vol. 0(323), pages .721-746, julio-sep.
    17. Rombouts Jeroen V. K. & Bouaddi Mohammed, 2009. "Mixed Exponential Power Asymmetric Conditional Heteroskedasticity," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(3), pages 1-32, May.
    18. Bera Anil K. & Galvao Antonio F. & Montes-Rojas Gabriel V. & Park Sung Y., 2016. "Asymmetric Laplace Regression: Maximum Likelihood, Maximum Entropy and Quantile Regression," Journal of Econometric Methods, De Gruyter, vol. 5(1), pages 79-101, January.
    19. Mohammed Bouaddi & Khouzeima Moutanabbir, 2022. "Systematic extreme potential gain and loss spillover across countries," Risk Management, Palgrave Macmillan, vol. 24(4), pages 327-366, December.
    20. Kaiping Wang, 2014. "Modeling Stock Index Returns using Semi-Parametric Approach with Multiplicative Adjustment," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 65-75, December.
    21. Douch, Mohamed & Farooq, Omar & Bouaddi, Mohammed, 2015. "Stock price synchronicity and tails of return distribution," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 1-11.
    22. Yi-Ting Chen, 2016. "Testing for Granger Causality in Moments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(2), pages 265-288, April.
    23. Lafaye de Micheaux, Pierre & Ouimet, Frédéric, 2018. "A uniform L1 law of large numbers for functions of i.i.d. random variables that are translated by a consistent estimator," Statistics & Probability Letters, Elsevier, vol. 142(C), pages 109-117.
    24. Vijverberg, Chu-Ping C. & Vijverberg, Wim P.M. & Taşpınar, Süleyman, 2016. "Linking Tukey’s legacy to financial risk measurement," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 595-615.
    25. León, Ángel & Ñíguez, Trino-Manuel, 2020. "Modeling asset returns under time-varying semi-nonparametric distributions," Journal of Banking & Finance, Elsevier, vol. 118(C).
    26. Lukas Arnroth, 2025. "Bayesian composite $$L^p$$ L p -quantile regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 88(1), pages 83-97, January.
    27. Mahdi Teimouri & Saralees Nadarajah, 2022. "Maximum Likelihood Estimation for the Asymmetric Exponential Power Distribution," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 665-692, August.
    28. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    29. Halvarsson, Daniel, 2019. "Asymmetric Double Pareto Distributions: Maximum Likelihood Estimation with Application to the Growth Rate Distribution of Firms," Ratio Working Papers 327, The Ratio Institute.
    30. Yong Bao, 2013. "On Sample Skewness and Kurtosis," Econometric Reviews, Taylor & Francis Journals, vol. 32(4), pages 415-448, December.
    31. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
    32. Mendoza-Velázquez, Alfonso & Galvanovskis, Evalds, 2009. "Introducing the GED-Copula with an application to Financial Contagion in Latin America," MPRA Paper 46669, University Library of Munich, Germany, revised 01 Feb 2010.
    33. Huber, Peter & Oberhofer, Harald & Pfaffermayr, Michael, 2015. "Who Creates Jobs? Econometric Modeling and Evidence for Austrian Firm Level Data," Department of Economics Working Paper Series 205, WU Vienna University of Economics and Business.
    34. Tae-Hwan Kim & Dong Jin Lee & Paul Mizen, 2020. "Impulse Response Analysis in Conditional Quantile Models and an Application to Monetary Policy," Working papers 2020rwp-164, Yonsei University, Yonsei Economics Research Institute.
    35. Komunjer, Ivana & Vuong, Quang, 2010. "Efficient estimation in dynamic conditional quantile models," Journal of Econometrics, Elsevier, vol. 157(2), pages 272-285, August.
    36. Petrella, Ivan & Santoro, Emiliano & Simonsen, Lasse de la Porte, 2018. "Time-varying Price Flexibility and Inflation Dynamics," CEPR Discussion Papers 13027, C.E.P.R. Discussion Papers.
    37. A. T. Soyinka & A. A. Olosunde, 2021. "Inferences from Asymmetric Multivariate Exponential Power Distribution," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 350-370, November.
    38. de Castro, Luciano & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2020. "Quantile selection in non-linear GMM quantile models," Economics Letters, Elsevier, vol. 195(C).
    39. Fabrizio Leisen & Luca Rossini & Cristiano Villa, 2020. "Loss-based approach to two-piece location-scale distributions with applications to dependent data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 309-333, June.
    40. Zhu, Dongming & Galbraith, John W., 2011. "Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 765-778, September.

  12. Ivana Komunjer & Federico Echenique, 2004. "Testing Models with Multiple Equilibria by Quantile Methods," Econometric Society 2004 North American Summer Meetings 447, Econometric Society.

    Cited by:

    1. Timmermann, Allan & Wermers, Russ, 2014. "Runs on Money Market Funds," CEPR Discussion Papers 9906, C.E.P.R. Discussion Papers.
    2. Taisuke Otsu & Yoshiyasu Rai, 2013. "On Testability Of Complementarity In Models With Multiple Equilibria," STICERD - Econometrics Paper Series 560, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    3. Rabah Amir & Natalia Lazzati, 2009. "Network Effects, Market Structure and Industry Performance," Working Papers 09-27, NET Institute.
    4. Natalia Lazzati & John K.-H. Quah & Koji Shirai, 2016. "A revealed preference theory of monotone choice and strategic complementarity," Discussion Paper Series 147, School of Economics, Kwansei Gakuin University, revised Oct 2016.
    5. Komunjer, Ivana, 2008. "Global Identification In Nonlinear Semiparametric Models," University of California at San Diego, Economics Working Paper Series qt2r59d87f, Department of Economics, UC San Diego.
    6. Ron N. Borkovsky & Paul B. Ellickson & Brett R. Gordon & Victor Aguirregabiria & Gardete Pedro, 2014. "Multiplicity of Equilibria and Information Structures in Empirical Games: Challenges and Prospects," Working Papers tecipa-510, University of Toronto, Department of Economics.
    7. Alberto Bisin & Andrea Moro & Giorgio Topa, 2011. "The empirical content of models with multiple equilibria in economies with social interactions," Staff Reports 504, Federal Reserve Bank of New York.
    8. Vincent P. Crawford & Miguel A. Costa-Gomes & Nagore Iriberri, 2010. "Strategic Thinking," Levine's Working Paper Archive 661465000000001148, David K. Levine.
    9. Giovanni Cespa & Xavier Vives, 2011. "Expectations, Liquidity, and Short-term Trading," CESifo Working Paper Series 3390, CESifo.
    10. Lukasz Balbusy & Pawel Dziewulski & Kevin Reffett & Lukasz Wozny, 2020. "Markov distributional equilibrium dynamics in games with complementarities and no aggregate risk," Working Paper Series 1320, Department of Economics, University of Sussex Business School.
    11. Giovanni Cespa & Xavier Vives, 2015. "The Beauty Contest and Short-Term Trading," Journal of Finance, American Finance Association, vol. 70(5), pages 2099-2154, October.
    12. Natalia Lazzati & John K.-H. Quah & Koji Shirai, 2018. "Nonparametric analysis of monotone choice," Discussion Paper Series 184, School of Economics, Kwansei Gakuin University.
    13. Wermers, Russ, 2012. "Runs on money market mutual funds," CFR Working Papers 12-05, University of Cologne, Centre for Financial Research (CFR).
    14. Áureo de Paula, 2012. "Econometric analysis of games with multiple equilibria," CeMMAP working papers CWP29/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Yuichi Kitamura & Louise Laage, 2018. "Nonparametric Analysis of Finite Mixtures," Papers 1811.02727, arXiv.org.
    16. Vives, Xavier & Vravosinos, Orestis, 2024. "Strategic complementarity in games," Journal of Mathematical Economics, Elsevier, vol. 113(C).
    17. Miyauchi, Yuhei, 2016. "Structural estimation of pairwise stable networks with nonnegative externality," Journal of Econometrics, Elsevier, vol. 195(2), pages 224-235.
    18. Camargo, Bráz Ministério de & Pastorino, Elena, 2012. "Career concerns: a human capital perspective," Textos para discussão 288, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).

  13. Allan Timmermann & Graham Elliott & Ivana Komunjer, 2004. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Econometric Society 2004 North American Summer Meetings 601, Econometric Society.

    Cited by:

    1. Clements, Michael P., 2008. "Explanations of the inconsistencies in survey respondents'forecasts," The Warwick Economics Research Paper Series (TWERPS) 870, University of Warwick, Department of Economics.
    2. Stan Hurn & Jing Tian & Lina Xu, 2021. "Assessing the Informational Content of Official Australian Bureau of Meteorology Forecasts of Wind Speed," The Economic Record, The Economic Society of Australia, vol. 97(319), pages 525-547, December.
    3. Benjamin Born & Zeno Enders & Manuel Menkhoff & Gernot J. Müller & Knut Niemann, 2023. "Firm Expectations and News: Micro v Macro," ifo Working Paper Series 400, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    4. Reitz, Stefan & Rülke, Jan & Stadtmann, Georg, 2012. "Nonlinear Expectations in Speculative Markets," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62045, Verein für Socialpolitik / German Economic Association.
    5. Aretz, Kevin & Bartram, Söhnke M. & Pope, Peter F., 2011. "Asymmetric Loss Functions and the Rationality of Expected Stock Returns," MPRA Paper 47343, University Library of Munich, Germany.
    6. Reitz, Stefan & Rülke, Jan-Christoph & Stadtmann, Georg, 2012. "Nonlinear expectations in speculative markets: Evidence from the ECB survey of professional forecasters," Discussion Papers 311, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
    7. Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2015. "Central banks’ inflation forecasts under asymmetric loss: Evidence from four Latin-American countries," Economics Letters, Elsevier, vol. 129(C), pages 66-70.
    8. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Oil-Price Volatility: The Role of Financial Stress and Asymmetric Loss," Working Papers 201903, University of Pretoria, Department of Economics.
    9. Issler, João Victor & Soares, Ana Flávia, 2019. "Central Bank credibility and inflation expectations: a microfounded forecasting approach," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 812, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    10. Valentina Corradi & Norman Swanson, 2013. "A Survey of Recent Advances in Forecast Accuracy Comparison Testing, with an Extension to Stochastic Dominance," Departmental Working Papers 201309, Rutgers University, Department of Economics.
    11. Christian Pierdzioch & Jan-Christoph Rülke & Georg Stadtmann, 2013. "Oil price forecasting under asymmetric loss," Applied Economics, Taylor & Francis Journals, vol. 45(17), pages 2371-2379, June.
    12. Carlos Capistrán & Allan Timmermann, 2008. "Forecast Combination With Entry and Exit of Experts," CREATES Research Papers 2008-55, Department of Economics and Business Economics, Aarhus University.
    13. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    14. Charles F. Manski, 2017. "Survey Measurement of Probabilistic Macroeconomic Expectations: Progress and Promise," NBER Working Papers 23418, National Bureau of Economic Research, Inc.
    15. Michael P. Clements, 2022. "Forecaster Efficiency, Accuracy, and Disagreement: Evidence Using Individual‐Level Survey Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(2-3), pages 537-568, March.
    16. Massenot, Baptiste & Pettinicchi, Yuri, 2018. "Can households see into the future? Survey evidence from the Netherlands," SAFE Working Paper Series 233, Leibniz Institute for Financial Research SAFE.
    17. Born, Benjamin & Enders, Zeno & Müller, Gernot, 2023. "On FIRE, news, and expectations," CEPR Discussion Papers 18259, C.E.P.R. Discussion Papers.
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    131. Guanghui Cai & Zhimin Wu & Lei Peng, 2021. "Forecasting volatility with outliers in Realized GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 667-685, July.
    132. Trung H. Le, 2024. "Forecasting VaR and ES in emerging markets: The role of time‐varying higher moments," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 402-414, March.
    133. Matteo Iacopini & Aubrey Poon & Luca Rossini & Dan Zhu, 2024. "A Quantile Nelson-Siegel model," Papers 2401.09874, arXiv.org.
    134. Le, Trung H., 2020. "Forecasting value at risk and expected shortfall with mixed data sampling," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1362-1379.
    135. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    136. Szymon Lis & Marcin Chlebus, 2021. "Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts," Working Papers 2021-11, Faculty of Economic Sciences, University of Warsaw.
    137. Christian Brownlees & Giuseppe Cavaliere & Alice Monti, 2018. "Evaluating The Accuracy Of Tail Risk Forecasts For Systemic Risk Measurement," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-25, June.
    138. Bams, Dennis & Blanchard, Gildas & Lehnert, Thorsten, 2017. "Volatility measures and Value-at-Risk," International Journal of Forecasting, Elsevier, vol. 33(4), pages 848-863.
    139. Mauro Bernardi & Leopoldo Catania & Lea Petrella, 2014. "Are news important to predict large losses?," Papers 1410.6898, arXiv.org, revised Oct 2014.
    140. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    141. Sander Barendse, 2017. "Interquantile Expectation Regression," Tinbergen Institute Discussion Papers 17-034/III, Tinbergen Institute.
    142. Baruník, Jozef & Čech, František, 2021. "Measurement of common risks in tails: A panel quantile regression model for financial returns," Journal of Financial Markets, Elsevier, vol. 52(C).
    143. Jan G. De Gooijer, 2023. "Penalized Averaging of Quantile Forecasts from GARCH Models with Many Exogenous Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 407-424, June.
    144. De Gooijer Jan G. & Zerom Dawit, 2020. "Penalized Averaging of Parametric and Non-Parametric Quantile Forecasts," Journal of Time Series Econometrics, De Gruyter, vol. 12(1), pages 1-15, January.

  15. Timmermann, Allan & Elliott, Graham & Komunjer, Ivana, 2003. "Estimating Loss Function Parameters," CEPR Discussion Papers 3821, C.E.P.R. Discussion Papers.

    Cited by:

    1. Graham Elliott & Ivana Komunjer & Allan Timmermann, 2008. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 122-157, March.
    2. Stan Hurn & Ralf Becker, 2009. "Testing for Nonlinearity in Mean in the Presence of Heteroskedasticity," Economic Analysis and Policy, Elsevier, vol. 39(2), pages 311-326, September.
    3. Siliverstovs, Boriss & Engsted, Tom & Haldrup, Niels, 2002. "Long-Run Forecasting in Multicointegrated Systems," Finance Working Papers 02-14, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    4. Yang Yang & Tae-Hwy Lee, 2004. "Bagging Binary Predictors for Time Series," Econometric Society 2004 Far Eastern Meetings 512, Econometric Society.
    5. Adrian Pagan & Hashem Pesaran, 2007. "Econometric Analysis of Structural Systems with Permanent and Transitory Shocks. Working paper #7," NCER Working Paper Series 7, National Centre for Econometric Research.
    6. Timmermann, Allan & Patton, Andrew, 2003. "Properties of Optimal Forecasts," CEPR Discussion Papers 4037, C.E.P.R. Discussion Papers.
    7. Martin Skitmore & Franco K. T. Cheung, 2007. "Explorations in specifying construction price forecast loss functions," Construction Management and Economics, Taylor & Francis Journals, vol. 25(5), pages 449-465.
    8. Lee, Tae-Hwy & Yang, Yang, 2006. "Bagging binary and quantile predictors for time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 465-497.
    9. Pedro Henrique Melo Albuquerque & Yaohao Peng & João Pedro Fontoura da Silva, 2022. "Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1701-1724, December.
    10. Alexander, Marcus & Christakis, Nicholas A., 2008. "Bias and asymmetric loss in expert forecasts: A study of physician prognostic behavior with respect to patient survival," Journal of Health Economics, Elsevier, vol. 27(4), pages 1095-1108, July.
    11. Basu, Sudipta & Markov, Stanimir, 2004. "Loss function assumptions in rational expectations tests on financial analysts' earnings forecasts," Journal of Accounting and Economics, Elsevier, vol. 38(1), pages 171-203, December.

  16. Komunjer, Ivana, 2002. "Quasi-Maximum Likelihood Estimation for Conditional Quantiles," Working Papers 1139, California Institute of Technology, Division of the Humanities and Social Sciences.

    Cited by:

    1. Tae-Hwy Lee & Weiping Yang, 2014. "Granger-Causality in Quantiles between Financial Markets: Using Copula Approach," Working Papers 201406, University of California at Riverside, Department of Economics.
    2. Lee, Tae-Hwy & Ullah, Aman & Wang, He, 2018. "The second-order bias of quantile estimators," Economics Letters, Elsevier, vol. 173(C), pages 143-147.
    3. Tae-Hwy Lee & Weiping Yang, 2012. "Money–Income Granger-Causality in Quantiles," Advances in Econometrics, in: 30th Anniversary Edition, pages 385-409, Emerald Group Publishing Limited.
    4. Marc Hallin & Catherine Vermandele & Bas J. M. Werker, 2008. "Semiparametrically efficient inference based on signs and ranks for median‐restricted models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 389-412, April.
    5. Oliver Linton & Dajing Shang & Yang Yan, 2012. "Efficient estimation of conditional risk measures in a semiparametric GARCH model," CeMMAP working papers 25/12, Institute for Fiscal Studies.
    6. Hartwig, Benny & Meinerding, Christoph & Schüler, Yves S., 2021. "Identifying indicators of systemic risk," Journal of International Economics, Elsevier, vol. 132(C).
    7. Alhamzawi, Rahim, 2016. "Bayesian model selection in ordinal quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 68-78.
    8. J. Carlos Escanciano & Carlos Velasco, 2010. "Specification tests of parametric dynamic conditional quantiles," Post-Print hal-00732534, HAL.
    9. Yang Yang & Tae-Hwy Lee, 2004. "Bagging Binary Predictors for Time Series," Econometric Society 2004 Far Eastern Meetings 512, Econometric Society.
    10. Patton, Andrew J. & Ziegel, Johanna F. & Chen, Rui, 2019. "Dynamic semiparametric models for expected shortfall (and Value-at-Risk)," Journal of Econometrics, Elsevier, vol. 211(2), pages 388-413.
    11. Jungsik Noh & Sangyeol Lee, 2016. "Quantile Regression for Location-Scale Time Series Models with Conditional Heteroscedasticity," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 700-720, September.
    12. Ying-Ying Lee, 2015. "Interpretation and Semiparametric Efficiency in Quantile Regression under Misspecification," Econometrics, MDPI, vol. 4(1), pages 1-14, December.
    13. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2020. "The Efficiency Gap," Papers 2010.14146, arXiv.org, revised Sep 2022.
    14. Ivana Komunjer, 2004. "Asymmetric Power Distribution: Theory and Applications to Risk Measurement," Econometric Society 2004 Latin American Meetings 44, Econometric Society.
    15. Meligkotsidou, Loukia & Vrontos, Ioannis D. & Vrontos, Spyridon D., 2009. "Quantile regression analysis of hedge fund strategies," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 264-279, March.
    16. Taylor, James W., 2022. "Forecasting Value at Risk and expected shortfall using a model with a dynamic omega ratio," Journal of Banking & Finance, Elsevier, vol. 140(C).
    17. Dong Jin Lee, 2009. "Testing Parameter Stability in Quantile Models: An Application to the U.S. Inflation Process," Working papers 2009-26, University of Connecticut, Department of Economics.
    18. Santos, Douglas G. & Candido, Osvaldo & Tófoli, Paula V., 2022. "Forecasting risk measures using intraday and overnight information," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    19. Ji, Yonggang & Lin, Nan & Zhang, Baoxue, 2012. "Model selection in binary and tobit quantile regression using the Gibbs sampler," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 827-839.
    20. Susana Faria & Maria Conceição Portela, 2016. "Student Performance in Mathematics using PISA-2009 data for Portugal," Working Papers de Gestão (Management Working Papers) 01, Católica Porto Business School, Universidade Católica Portuguesa.
    21. Zongwu Cai & Meng Shi & Yue Zhao & Wuqing Wu, 2020. "Testing Financial Hierarchy Based on A PDQ-CRE Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202011, University of Kansas, Department of Economics, revised Jul 2020.
    22. Sebastian Bayer & Timo Dimitriadis, 2022. "Regression-Based Expected Shortfall Backtesting [Backtesting Expected Shortfall]," Journal of Financial Econometrics, Oxford University Press, vol. 20(3), pages 437-471.
    23. Yunmi Kim & Lijuan Huo & Tae-Hwan Kim, 2020. "Dealing with Markov-Switching Parameters in Quantile Regression Models," Working papers 2020rwp-166, Yonsei University, Yonsei Economics Research Institute.
    24. Gourieroux, C. & Jasiak, J., 2008. "Dynamic quantile models," Journal of Econometrics, Elsevier, vol. 147(1), pages 198-205, November.
    25. Sessi Tokpavi & Christophe Boucher, 2018. "Stocks and Bonds: Flight-to-Safety for Ever?," EconomiX Working Papers 2018-39, University of Paris Nanterre, EconomiX.
    26. Bera Anil K. & Galvao Antonio F. & Montes-Rojas Gabriel V. & Park Sung Y., 2016. "Asymmetric Laplace Regression: Maximum Likelihood, Maximum Entropy and Quantile Regression," Journal of Econometric Methods, De Gruyter, vol. 5(1), pages 79-101, January.
    27. Lee, Tae-Hwy & Yang, Yang, 2006. "Bagging binary and quantile predictors for time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 465-497.
    28. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2022. "Characterizing M-estimators," Papers 2208.08108, arXiv.org.
    29. Cai, Zongwu & Chen, Linna & Fang, Ying, 2018. "A semiparametric quantile panel data model with an application to estimating the growth effect of FDI," Journal of Econometrics, Elsevier, vol. 206(2), pages 531-553.
    30. Manganelli, Simone & White, Halbert & Kim, Tae-Hwan, 2008. "Modeling autoregressive conditional skewness and kurtosis with multi-quantile CAViaR," Working Paper Series 957, European Central Bank.
    31. Rahim Alhamzawi, 2016. "Bayesian Analysis of Composite Quantile Regression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(2), pages 358-373, October.
    32. Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Yoldas, Emre, 2007. "Optimality of the RiskMetrics VaR model," Finance Research Letters, Elsevier, vol. 4(3), pages 137-145, September.
    33. Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, vol. 6(2), pages 1-16, April.
    34. Großmaß Lidan, 2014. "Liquidity and the Value at Risk," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 234(5), pages 572-602, October.
    35. Bernstein, David H. & Parmeter, Christopher F. & Tsionas, Mike G., 2023. "On the performance of the United States nuclear power sector: A Bayesian approach," Energy Economics, Elsevier, vol. 125(C).
    36. Catania, Leopoldo & Luati, Alessandra, 2023. "Semiparametric modeling of multiple quantiles," Journal of Econometrics, Elsevier, vol. 237(2).
    37. Habert white & Tae-Hwan Kim & Simone Manganelli, 2012. "VAR for VaR: Measuring Tail Dependence Using Multivariate Regression Quantiles," Working papers 2012rwp-45, Yonsei University, Yonsei Economics Research Institute.
    38. Gabriel Montes-Rojas & Zacharias Psaradakis & Martín Sola, 2024. "On Regime Separation in Markov-Switching Quantile Regressions," Department of Economics Working Papers 2024_05, Universidad Torcuato Di Tella.
    39. Alhamzawi, Rahim & Yu, Keming, 2013. "Conjugate priors and variable selection for Bayesian quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 209-219.
    40. Tianshun Yan & Yanyong Zhao & Wentao Wang, 2020. "Likelihood-based estimation of a semiparametric time-dependent jump diffusion model of the short-term interest rate," Computational Statistics, Springer, vol. 35(2), pages 539-557, June.
    41. Christophe Boucher & Sessi Tokpavi, 2018. "Stocks and Bonds: Flight-to-Safety for Ever?," Working Papers hal-04141705, HAL.
    42. Lieli, Robert P. & Stinchcombe, Maxwell B. & Grolmusz, Viola M., 2019. "Unrestricted and controlled identification of loss functions: Possibility and impossibility results," International Journal of Forecasting, Elsevier, vol. 35(3), pages 878-890.
    43. Lijuan Huo & Tae-Hwan Kim & Yunmi Kim, 2013. "Testing for Autocorrelation in Quantile Regression Models," Working papers 2013rwp-54, Yonsei University, Yonsei Economics Research Institute.
    44. De Rezende, Rafael B., 2015. "Risks in macroeconomic fundamentals and excess bond returns predictability," Working Paper Series 295, Sveriges Riksbank (Central Bank of Sweden).
    45. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    46. Huang, Dashan & Yu, Baimin & Fabozzi, Frank J. & Fukushima, Masao, 2009. "CAViaR-based forecast for oil price risk," Energy Economics, Elsevier, vol. 31(4), pages 511-518, July.
    47. Fabian Krüger & Ingmar Nolte, 2011. "Disagreement, Uncertainty and the True Predictive Density," Working Paper Series of the Department of Economics, University of Konstanz 2011-43, Department of Economics, University of Konstanz.
    48. White, Halbert & Kim, Tae-Hwan & Manganelli, Simone, 2010. "VAR for VaR: measuring systemic risk using multivariate regression quantiles," MPRA Paper 35372, University Library of Munich, Germany.
    49. Reiss Philip T. & Huang Lei, 2012. "Smoothness Selection for Penalized Quantile Regression Splines," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-27, May.
    50. Anil K. Bera & Antonio F. Galvao Jr. & Gabriel V. Montes-Rojas & Sung Y. Park, 2014. "Which Quantile is the Most Informative? Maximum Likelihood, Maximum Entropy and Quantile Regression," World Scientific Book Chapters, in: Kaddour Hadri & William Mikhail (ed.), Econometric Methods and Their Applications in Finance, Macro and Related Fields, chapter 7, pages 167-199, World Scientific Publishing Co. Pte. Ltd..
    51. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    52. Zongwu Cai & Ying Fang & Dingshi Tian, 2024. "CAViaR Model Selection Via Adaptive Lasso," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202403, University of Kansas, Department of Economics, revised Jan 2024.
    53. Xiaochun Meng & James W. Taylor & Souhaib Ben Taieb & Siran Li, 2020. "Scores for Multivariate Distributions and Level Sets," Papers 2002.09578, arXiv.org, revised Jun 2023.
    54. Haowen Bao & Zongwu Cai & Yuying Sun & Shouyang Wang, 2023. "Penalized Model Averaging for High Dimensional Quantile Regressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202302, University of Kansas, Department of Economics, revised Jan 2023.
    55. Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
    56. DeRossi, G. & Harvey, A., 2006. "Time-Varying Quantiles," Cambridge Working Papers in Economics 0649, Faculty of Economics, University of Cambridge.
    57. Akanksha Negi, 2020. "Doubly weighted M-estimation for nonrandom assignment and missing outcomes," Papers 2011.11485, arXiv.org.
    58. Tae-Hwy Lee & Aman Ullah & He Wang, 2024. "The second-order bias and mean squared error of quantile regression estimators," Indian Economic Review, Springer, vol. 59(1), pages 11-68, October.
    59. Christophe Boucher & Sessi Tokpavi, 2019. "Stocks and Bonds: Flight-to-Safety for Ever?," Post-Print hal-02067096, HAL.
    60. Komunjer, Ivana & Vuong, Quang, 2010. "Efficient estimation in dynamic conditional quantile models," Journal of Econometrics, Elsevier, vol. 157(2), pages 272-285, August.
    61. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 197-207, April.
    62. Timo Dimitriadis & Julie Schnaitmann, 2019. "Forecast Encompassing Tests for the Expected Shortfall," Papers 1908.04569, arXiv.org, revised Aug 2020.
    63. Dong Jin Lee, 2020. "Optimal tests for parameter breaking process in conditional quantile models," The Japanese Economic Review, Springer, vol. 71(3), pages 479-510, July.
    64. Christian Brownlees & Giuseppe Cavaliere & Alice Monti, 2018. "Evaluating The Accuracy Of Tail Risk Forecasts For Systemic Risk Measurement," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-25, June.
    65. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 197-207.
    66. Tae-Hwy Lee & Aman Ullah & He Wang, 2023. "The Second-order Bias and Mean Squared Error of Quantile Regression Estimators," Working Papers 202313, University of California at Riverside, Department of Economics.
    67. El Ghouch, Anouar & Genton, Marc G., 2009. "Local Polynomial Quantile Regression With Parametric Features," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1416-1429.
    68. de Castro, Luciano & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2020. "Quantile selection in non-linear GMM quantile models," Economics Letters, Elsevier, vol. 195(C).
    69. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    70. Dong Jin Lee & Jai Hyung Yoon, 2012. "The New Keynesian Phillips Curves in Multiple Quantiles and the Asymmetry of Monetary Policy," Working papers 2012-03, University of Connecticut, Department of Economics.
    71. Alecos Papadopoulos, 2024. "Some notes on the asymmetry of the regression error," Journal of Productivity Analysis, Springer, vol. 61(1), pages 37-42, February.

  17. Komunjer, Ivana, 2001. "Consistent Estimation for Aggregated GARCH," University of California at San Diego, Economics Working Paper Series qt1fp2v3q7, Department of Economics, UC San Diego.

    Cited by:

    1. Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2017. "A suggestion for constructing a large time-varying conditional covariance matrix," Economics Letters, Elsevier, vol. 156(C), pages 110-113.
    2. Jondeau, Eric, 2015. "The dynamics of squared returns under contemporaneous aggregation of GARCH models," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 80-93.
    3. Eric Jondeau, 2008. "Contemporaneous Aggregation of GARCH Models and Evaluation of the Aggregation Bias," Swiss Finance Institute Research Paper Series 08-06, Swiss Finance Institute.

Articles

  1. Komunjer, Ivana & Zhu, Yinchu, 2020. "Likelihood ratio testing in linear state space models: An application to dynamic stochastic general equilibrium models," Journal of Econometrics, Elsevier, vol. 218(2), pages 561-586.

    Cited by:

    1. Andrzej Kocięcki & Marcin Kolasa, 2022. "A solution to the global identification problem in DSGE models," Working Papers 2022-01, Faculty of Economic Sciences, University of Warsaw.
    2. Ke Sun & Linglong Kong & Hongtu Zhu & Chengchun Shi, 2024. "ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments," Papers 2408.05342, arXiv.org, revised Jan 2025.

  2. Julieta Caunedo & Riccardo Dicecio & Ivana Komunjer & Michael T. Owyang, 2020. "Asymmetry, Complementarities, and State Dependence in Federal Reserve Forecasts," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(1), pages 205-228, February.
    See citations under working paper version above.
  3. Gospodinov, Nikolay & Komunjer, Ivana & Ng, Serena, 2017. "Simulated minimum distance estimation of dynamic models with errors-in-variables," Journal of Econometrics, Elsevier, vol. 200(2), pages 181-193.

    Cited by:

    1. Yong Bao & Xiaotian Liu & Lihong Yang, 2020. "Indirect Inference Estimation of Spatial Autoregressions," Econometrics, MDPI, vol. 8(3), pages 1-26, September.
    2. Worapree Maneesoonthorn & David T. Frazier & Gael M. Martin, 2024. "Probabilistic Predictions of Option Prices Using Multiple Sources of Data," Papers 2412.00658, arXiv.org.
    3. Bao, Yong & Yu, Xuewen, 2023. "Indirect inference estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1027-1053.
    4. Veronika Czellar & David T. Frazier & Eric Renault, 2020. "Approximate Maximum Likelihood for Complex Structural Models," Papers 2006.10245, arXiv.org.
    5. Czellar, Veronika & Frazier, David T. & Renault, Eric, 2021. "Approximate Maximum Likelihood for Complex Structural Models," The Warwick Economics Research Paper Series (TWERPS) 1337, University of Warwick, Department of Economics.
    6. Sönksen, Jantje & Grammig, Joachim, 2020. "Empirical Asset Pricing with Multi-Period Disaster Risk: A Simulation-Based Approach," CFR Working Papers 14-06, University of Cologne, Centre for Financial Research (CFR), revised 2020.
    7. Jean-Jacques Forneron, 2019. "A Sieve-SMM Estimator for Dynamic Models," Papers 1902.01456, arXiv.org, revised Jan 2023.
    8. Shuowen Chen, 2022. "Indirect Inference for Nonlinear Panel Models with Fixed Effects," Papers 2203.10683, arXiv.org, revised Apr 2022.
    9. Czellar, Veronika & Frazier, David T. & Renault, Eric, 2022. "Approximate maximum likelihood for complex structural models," Journal of Econometrics, Elsevier, vol. 231(2), pages 432-456.
    10. Yong Bao, 2021. "Indirect Inference Estimation of a First-Order Dynamic Panel Data Model," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 79-98, December.

  4. Komunjer, Ivana & Ragusa, Giuseppe, 2016. "Existence And Characterization Of Conditional Density Projections," Econometric Theory, Cambridge University Press, vol. 32(4), pages 947-987, August.

    Cited by:

    1. Timothy Christensen & Benjamin Connault, 2019. "Counterfactual Sensitivity and Robustness," Papers 1904.00989, arXiv.org, revised May 2022.
    2. Tae-Hwy Lee & Millie Yi Mao & Aman Ullah, 2020. "Maximum Entropy Analysis of Consumption-based Capital Asset Pricing Model and Volatility," Working Papers 202015, University of California at Riverside, Department of Economics.
    3. Andreas Tryphonides, 2018. "Tilting Approximate Models," Papers 1805.10869, arXiv.org, revised Mar 2024.
    4. Qiu, Chen & Otsu, Taisuke, 2022. "Information theoretic approach to high dimensional multiplicative models: stochastic discount factor and treatment effect," LSE Research Online Documents on Economics 110494, London School of Economics and Political Science, LSE Library.
    5. Chen Qiu & Taisuke Otsu, 2022. "Information theoretic approach to high‐dimensional multiplicative models: Stochastic discount factor and treatment effect," Quantitative Economics, Econometric Society, vol. 13(1), pages 63-94, January.
    6. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.
    7. Naoya Sueishi, 2022. "A Misuse of Specification Tests," Papers 2211.11915, arXiv.org.
    8. Tryphonides, Andreas, 2017. "Conditional moment restrictions and the role of density information in estimated structural models," SFB 649 Discussion Papers 2017-016, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    9. Timothy Christensen & Benjamin Connault, 2023. "Counterfactual Sensitivity and Robustness," Econometrica, Econometric Society, vol. 91(1), pages 263-298, January.

  5. Chiappori, Pierre-André & Komunjer, Ivana & Kristensen, Dennis, 2015. "Nonparametric identification and estimation of transformation models," Journal of Econometrics, Elsevier, vol. 188(1), pages 22-39.
    See citations under working paper version above.
  6. Komunjer, Ivana & Ng, Serena, 2014. "Measurement Errors In Dynamic Models," Econometric Theory, Cambridge University Press, vol. 30(1), pages 150-175, February.

    Cited by:

    1. Daniel Kaufmann, 2017. "Is Deflation Costly After All? The Perils of Erroneous Historical Classifications," IRENE Working Papers 17-09, IRENE Institute of Economic Research.
    2. Hilbert, Martin, 2016. "The bad news is that the digital access divide is here to stay: Domestically installed bandwidths among 172 countries for 1986–2014," Telecommunications Policy, Elsevier, vol. 40(6), pages 567-581.
    3. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2018. "Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions," Journal of Econometrics, Elsevier, vol. 207(1), pages 71-91.
    4. Gospodinov, Nikolay & Komunjer, Ivana & Ng, Serena, 2017. "Simulated minimum distance estimation of dynamic models with errors-in-variables," Journal of Econometrics, Elsevier, vol. 200(2), pages 181-193.
    5. Thomas von Brasch & Diana-Cristina Iancu & Terje Skjerpen, 2017. "Productivity dispersion and measurement errors," Discussion Papers 869, Statistics Norway, Research Department.
    6. Edith Kitzmantel, 2016. "EU-Fiskalregeln - Anker oder Mühlstein der europäischen Wirtschaftspolitik?," Wirtschaft und Gesellschaft - WuG, Kammer für Arbeiter und Angestellte für Wien, Abteilung Wirtschaftswissenschaft und Statistik, vol. 42(3), pages 431-450.
    7. Nikolay Gospodinov & Ivana Komunjer & Serena Ng, 2014. "Minimum Distance Estimation of Dynamic Models with Errors-In-Variables," FRB Atlanta Working Paper 2014-11, Federal Reserve Bank of Atlanta.
    8. Alicia N. Rambaldi & Ryan R. J. McAllister & Cameron S. Fletcher, 2015. "Decoupling land values in residential property prices: smoothing methods for hedonic imputed price indices," Discussion Papers Series 549, School of Economics, University of Queensland, Australia.
    9. Jiahe Lin & George Michailidis, 2019. "Regularized Estimation of High-dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models," Papers 1912.04146, arXiv.org, revised May 2020.

  7. Pierre-André Chiappori & Olivier Donni & Ivana Komunjer, 2012. "Learning from a Piece of Pie," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(1), pages 162-195.

    Cited by:

    1. Bo Honoré & Áureo de Paula, 2011. "Interdependent Durations in Joint Retirement," Working Papers, Center for Retirement Research at Boston College wp2011-5, Center for Retirement Research, revised Feb 2011.
    2. Nishimura, Hiroki, 2021. "Revealed preferences of individual players in sequential games," Journal of Mathematical Economics, Elsevier, vol. 96(C).
    3. , P. & ,, 2014. "On the consistency of data with bargaining theories," Theoretical Economics, Econometric Society, vol. 9(1), January.
    4. Laurens Cherchye & Thomas Demuynck & Bram De Rock, 2013. "The empirical content of Cournot competition," ULB Institutional Repository 2013/151678, ULB -- Universite Libre de Bruxelles.
    5. Hubner, Stefan, 2016. "Topics in nonparametric identification and estimation," Other publications TiSEM 08fce56b-3193-46e0-871b-0, Tilburg University, School of Economics and Management.
    6. Donni, Olivier & Molina, José Alberto, 2018. "Household Collective Models: Three Decades of Theoretical Contributions and Empirical Evidence," IZA Discussion Papers 11915, Institute of Labor Economics (IZA).
    7. Bo E. Honoré & Áureo de Paula, 2016. "A new model for interdependent durations with an application to joint retirement," CeMMAP working papers 07/16, Institute for Fiscal Studies.
    8. Le Breton, Michel & Van Der Straeten, Karine, 2012. "Alliances Electorales entre Deux Tours de Scrutin : Le Point de Vue de la Théorie des Jeux Coopératifs et une Application aux Elections Régionales de Mars 2010," IDEI Working Papers 710, Institut d'Économie Industrielle (IDEI), Toulouse.
    9. Laurens CHERCHYE & Thomas DEMUYNCK & Bram DE ROCK, 2011. "Nash bargained consumption decisions: a revealed preference analysis," Working Papers of Department of Economics, Leuven ces11.07, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    10. Jean-Paul Chavas & Eleonora Matteazzi & Martina Menon & Federico Perali, 2022. "(In)Efficient Bargaining in the Family," Working Papers 2, SITES.
    11. David Boto-García & Petr Mariel, 2024. "How well do couples know their partners’ preferences? Experimental evidence from joint recreation," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 41(3), pages 657-686, October.
    12. Martina Menon & Federico Perali, 2012. "The Sharing Rule: Where Is It?," Working Papers 16/2012, University of Verona, Department of Economics.
    13. Hubner, Stefan, 2023. "Identification of unobserved distribution factors and preferences in the collective household model," Journal of Econometrics, Elsevier, vol. 234(1), pages 301-326.
    14. Jean-Paul Chavas & Eleonora Matteazzi & Martina Menon & Federico Perali, 2021. "Bargaining in the Family," CHILD Working Papers Series 88 JEL Classification: D1, Centre for Household, Income, Labour and Demographic Economics (CHILD) - CCA.
    15. Oliveira, Fernando S., 2023. "The emergence of social inequality: A Co-Evolutionary analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 192-206.
    16. Carvajal, Andrés, 2024. "Recent advances on testability in economic equilibrium models," Journal of Mathematical Economics, Elsevier, vol. 114(C).

  8. Komunjer, Ivana, 2012. "Global Identification In Nonlinear Models With Moment Restrictions," Econometric Theory, Cambridge University Press, vol. 28(4), pages 719-729, August.

    Cited by:

    1. Fabio A. Miessi Sanches & Daniel Silva Junior, Sorawoot Srisuma, 2015. "Minimum Distance Estimation of Search Costs using Price Distribution," Working Papers, Department of Economics 2015_31, University of São Paulo (FEA-USP).
    2. Mutschler, Willi, 2014. "Identification of DSGE Models - A Comparison of Methods and the Effect of Second Order Approximation," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100598, Verein für Socialpolitik / German Economic Association.
    3. Aureo de Paula & Imran Rasul & Pedro Souza, 2019. "Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition," Papers 1910.07452, arXiv.org, revised Oct 2023.
    4. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2020. "The Efficiency Gap," Papers 2010.14146, arXiv.org, revised Sep 2022.
    5. Aureo de Paula & Imran Rasul & Pedro CL Souza, 2018. "Recovering social networks from panel data: Identification, simulations and an application," Documentos de Trabajo 16173, The Latin American and Caribbean Economic Association (LACEA).
    6. Forneron, Jean-Jacques, 2024. "Detecting identification failure in moment condition models," Journal of Econometrics, Elsevier, vol. 238(1).
    7. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2022. "Characterizing M-estimators," Papers 2208.08108, arXiv.org.
    8. Nail Kashaev, 2022. "Estimation of Parametric Binary Outcome Models with Degenerate Pure Choice-Based Data with Application to COVID-19-Positive Tests from British Columbia," University of Western Ontario, Departmental Research Report Series 20225, University of Western Ontario, Department of Economics.
    9. Komarova, Tatiana & Sanches, Fábio Adriano & Silva Junior, Daniel & Srisuma, Sorawoot, 2018. "Joint analysis of the discount factor and payoff parameters in dynamic discrete choice games," LSE Research Online Documents on Economics 86858, London School of Economics and Political Science, LSE Library.
    10. Zhentao Shi & Huanhuan Zheng, 2018. "Structural estimation of behavioral heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 690-707, August.
    11. Arthur Lewbel, 2018. "The Identification Zoo - Meanings of Identification in Econometrics," Boston College Working Papers in Economics 957, Boston College Department of Economics, revised 14 Dec 2019.
    12. Iaria, Alessandro & ,, 2020. "Identification and Estimation of Demand for Bundles," CEPR Discussion Papers 14363, C.E.P.R. Discussion Papers.

  9. Ivana Komunjer & Michael T. Owyang, 2012. "Multivariate Forecast Evaluation and Rationality Testing," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1066-1080, November.
    See citations under working paper version above.
  10. Arnaud Costinot & Dave Donaldson & Ivana Komunjer, 2012. "What Goods Do Countries Trade? A Quantitative Exploration of Ricardo's Ideas," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(2), pages 581-608.
    See citations under working paper version above.
  11. Ivana Komunjer & Serena Ng, 2011. "Dynamic Identification of Dynamic Stochastic General Equilibrium Models," Econometrica, Econometric Society, vol. 79(6), pages 1995-2032, November.

    Cited by:

    1. Ríos-Rull, José-Víctor & Schorfheide, Frank & Fuentes-Albero, Cristina & Kryshko, Maxym & Santaeulàlia-Llopis, Raül, 2012. "Methods versus substance: Measuring the effects of technology shocks," Journal of Monetary Economics, Elsevier, vol. 59(8), pages 826-846.
    2. Andrzej Kocięcki & Marcin Kolasa, 2022. "A solution to the global identification problem in DSGE models," Working Papers 2022-01, Faculty of Economic Sciences, University of Warsaw.
    3. Chatelain, Jean-Bernard & Ralf, Kirsten, 2017. "Can we Identify the Fed's Preferences?," MPRA Paper 76831, University Library of Munich, Germany.
    4. Massimo Franchi & Paolo Paruolo, 2015. "Minimality of State Space Solutions of DSGE Models and Existence Conditions for Their VAR Representation," Computational Economics, Springer;Society for Computational Economics, vol. 46(4), pages 613-626, December.
    5. Atsushi Inoue & Chun-Hung Kuo & Barbara Rossi, 2015. "Identifying the sources of model misspecification," Economics Working Papers 1479, Department of Economics and Business, Universitat Pompeu Fabra, revised Jun 2018.
    6. Kyle Jurado, 2016. "Advance Information and Distorted Beliefs in Macroeconomic and Financial Fluctuations," 2016 Meeting Papers 154, Society for Economic Dynamics.
    7. Olkhov, Victor, 2019. "New Essentials of Economic Theory," MPRA Paper 95065, University Library of Munich, Germany.
    8. Dave, Chetan & Malik, Samreen, 2017. "A tale of fat tails," European Economic Review, Elsevier, vol. 100(C), pages 293-317.
    9. Yao, Wenying & Kam, Timothy & Vahid, Farshid, 2014. "VAR(MA), what is it good for? more bad news for reduced-form estimation and inference," Working Papers 2014-14, University of Tasmania, Tasmanian School of Business and Economics.
    10. Andrew Binning & Junior Maih, 2015. "Sigma Point Filters For Dynamic Nonlinear Regime Switching Models," Working Papers No 4/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    11. Peter A. Zadrozny, 2015. "Extended Yule-Walker Identification of Varma Models with Single- or Mixed- Frequency Data," Economic Working Papers 485, Bureau of Labor Statistics.
    12. Yunus Aksoy & Henrique S. Basso & Ron P. Smith & Tobias Grasl, 2016. "Demographic Structure and Macroeconomic Trends," CESifo Working Paper Series 5872, CESifo.
    13. Tenreyro, Silvana & Broadbent, Ben & Di Pace, Federico & Drechsel, Thomas & Harrison, Richard, 2019. "The Brexit Vote, Productivity Growth and Macroeconomic Adjustments in the United Kingdom," CEPR Discussion Papers 13993, C.E.P.R. Discussion Papers.
    14. Mutschler, Willi, 2014. "Identification of DSGE Models - A Comparison of Methods and the Effect of Second Order Approximation," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100598, Verein für Socialpolitik / German Economic Association.
    15. Härtl, Tilmann, 2022. "Identifying Proxy VARs with Restrictions on the Forecast Error Variance," VfS Annual Conference 2022 (Basel): Big Data in Economics 264071, Verein für Socialpolitik / German Economic Association.
    16. Haberis, Alex & Sokol, Andrej, 2014. "A procedure for combining zero and sign restrictions in aVAR-identification scheme," LSE Research Online Documents on Economics 58077, London School of Economics and Political Science, LSE Library.
    17. Chen, Xiaoshan & Kirsanova, Tatiana & Leith, Campbell, 2013. "How Optimal is US Monetary Policy?," Stirling Economics Discussion Papers 2013-05, University of Stirling, Division of Economics.
    18. Angelini, Giovanni, 2020. "Bootstrap lag selection in DSGE models with expectations correction," Econometrics and Statistics, Elsevier, vol. 14(C), pages 38-48.
    19. Luca Fanelli & Marco M. Sorge, 2015. "Indeterminacy, Misspecification and Forecastability: Good Luck in Bad Policy?," CSEF Working Papers 402, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    20. Raffaella Giacomini, 2013. "The relationship between DSGE and VAR models," CeMMAP working papers CWP21/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    21. Aruoba, S. Borağan & Diebold, Francis X. & Nalewaik, Jeremy & Schorfheide, Frank & Song, Dongho, 2016. "Improving GDP measurement: A measurement-error perspective," Journal of Econometrics, Elsevier, vol. 191(2), pages 384-397.
    22. Willi Mutschler, 2015. "Higher-order statistics for DSGE models," CQE Working Papers 4315, Center for Quantitative Economics (CQE), University of Muenster.
    23. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.
    24. Parra-Alvarez, Juan Carlos & Posch, Olaf & Wang, Mu-Chun, 2020. "Estimation of heterogeneous agent models: A likelihood approach," Discussion Papers 42/2020, Deutsche Bundesbank.
    25. Stéphane Adjemian & Houtan Bastani & Michel Juillard & Frédéric Karamé & Ferhat Mihoubi & Willi Mutschler & Johannes Pfeifer & Marco Ratto & Sébastien Villemot & Normann Rion, 2023. "Dynare: Reference Manual Version 5," PSE Working Papers hal-04219920, HAL.
      • Stéphane Adjemian & Houtan Bastani & Michel Juillard & Frédéric Karamé & Ferhat Mihoubi & Willi Mutschler & Johannes Pfeifer & Marco Ratto & Sébastien Villemot & Normann Rion, 2023. "Dynare: Reference Manual Version 5," Working Papers hal-04219920, HAL.
      • Adjemian, Stéphane & Bastani, Houtan & Juillard, Michel & Karamé, Fréderic & Mihoubi, Ferhat & Mutschler, Willi & Pfeifer, Johannes & Ratto, Marco & Rion, Normann & Villemot, Sébastien, 2022. "Dynare: Reference Manual Version 5," Dynare Working Papers 72, CEPREMAP, revised Mar 2023.
    26. Christis Katsouris, 2023. "Structural Analysis of Vector Autoregressive Models," Papers 2312.06402, arXiv.org, revised Feb 2024.
    27. Jonathan Benchimol & Lahcen Bounader, 2018. "Optimal Monetary Policy Under Bounded Rationality," Globalization Institute Working Papers 336, Federal Reserve Bank of Dallas.
    28. Hsiao, Cody Yu-Ling & Jin, Tao & Kwok, Simon & Wang, Xi & Zheng, Xin, 2023. "Entrepreneurial risk shocks and financial acceleration asymmetry in a two-country DSGE model," China Economic Review, Elsevier, vol. 81(C).
    29. Daniel Rees & David Lancaster & Richard Finlay, 2014. "A State-space Approach to Australian GDP Measurement," RBA Research Discussion Papers rdp2014-12, Reserve Bank of Australia.
    30. Fève, Patrick & Beaudry, Paul & Collard, Fabrice & Guay, Alain & Portier, Franck, 2022. "Dynamic Identification in VARs," TSE Working Papers 22-1384, Toulouse School of Economics (TSE).
    31. Willi Mutschler, 2014. "Identification of DSGE Models - the Effect of Higher-Order Approximation and Pruning," CQE Working Papers 3314, Center for Quantitative Economics (CQE), University of Muenster.
    32. Chatelain, Jean-Bernard & Ralf, Kirsten, 2018. "Publish and Perish: Creative Destruction and Macroeconomic Theory," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 46(2), pages 65-101.
    33. Nathan Bedock & Dalibor Stevanović, 2017. "An empirical study of credit shock transmission in a small open economy," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(2), pages 541-570, May.
    34. Pedro Brinca & Nikolay Iskrev & Francesca Loria, 2022. "On Identification Issues in Business Cycle Accounting Models," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 55-138, Emerald Group Publishing Limited.
    35. Jean-Bernard Chatelain & Kirsten Ralf, 2014. "Stability and Identification with Optimal Macroprudential Policy Rules," Papers 1404.3347, arXiv.org.
    36. Yasuo Hirose & Hirokuni Iiboshi & Mototsugu Shintani & Kozo Ueda, 2024. "Estimating a Behavioral New Keynesian Model with the Zero Lower Bound," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(8), pages 2185-2197, December.
    37. Den Haan, Wouter & Drechsel, Thomas, 2018. "Agnostic Structural Disturbances (ASDs): Detecting and Reducing Misspecification in Empirical Macroeconomic Models," CEPR Discussion Papers 13145, C.E.P.R. Discussion Papers.
    38. Adjemian, Stéphane & Juillard, Michel & Karamé, Fréderic & Mutschler, Willi & Pfeifer, Johannes & Ratto, Marco & Rion, Normann & Villemot, Sébastien, 2024. "Dynare: Reference Manual, Version 6," Dynare Working Papers 80, CEPREMAP, revised Feb 2025.
    39. Jan P. A. M. Jacobs & Samad Sarferaz & Jan-Egbert Sturm & Simon van Norden, 2022. "Can GDP Measurement Be Further Improved? Data Revision and Reconciliation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 423-431, January.
    40. Franchi, Massimo, 2018. "Testing for cointegration in I(1) state space systems via a finite order approximation," Economics Letters, Elsevier, vol. 165(C), pages 73-76.
    41. Olkhov, Victor, 2019. "New Essentials of Economic Theory I. Assumptions, Economic Space and Variables," MPRA Paper 94874, University Library of Munich, Germany.
    42. Jesús Fernández-Villaverde & Pablo A. Guerrón-Quintana, 2021. "Estimating DSGE Models: Recent Advances and Future Challenges," Annual Review of Economics, Annual Reviews, vol. 13(1), pages 229-252, August.
    43. Alessandro SACCAL, 2021. "A Note On Gensys’ Minimality," Theoretical and Practical Research in the Economic Fields, ASERS Publishing, vol. 12(1), pages 57-60.
    44. Claire A. Reicher, 2016. "A Note on the Identification of Dynamic Economic Models with Generalized Shock Processes," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(3), pages 412-423, June.
    45. Nikolay Gospodinov & Serena Ng, 2013. "Minimum distance estimation of possibly non-invertible moving average models," FRB Atlanta Working Paper 2013-11, Federal Reserve Bank of Atlanta.
    46. Varang Wiriyawit, 2014. "Trend Mis-specifications and Estimated Policy Implications in DSGE Models," ANU Working Papers in Economics and Econometrics 2014-615, Australian National University, College of Business and Economics, School of Economics.
    47. Martín Almuzara & Dante Amengual & Enrique Sentana, 2019. "Normality tests for latent variables," Quantitative Economics, Econometric Society, vol. 10(3), pages 981-1017, July.
    48. Deák, Szabolcs & Levine, Paul & Pham, Son T., 2024. "Simple mandates, monetary rules, and trend-inflation," Macroeconomic Dynamics, Cambridge University Press, vol. 28(4), pages 757-790, June.
    49. Ho, Paul, 2023. "Global robust Bayesian analysis in large models," Journal of Econometrics, Elsevier, vol. 235(2), pages 608-642.
    50. Peter A. Zadrozny, 2022. "Linear Identification of Linear Rational-Expectations Models by Exogenous Variables Reconciles Lucas and Sims," CESifo Working Paper Series 10078, CESifo.
    51. Enrique Martínez García, 2020. "A Matter of Perspective: Mapping Linear Rational Expectations Models into Finite-Order VAR Form," Globalization Institute Working Papers 389, Federal Reserve Bank of Dallas.
    52. Anderson, Brian D.O. & Deistler, Manfred & Felsenstein, Elisabeth & Koelbl, Lukas, 2016. "The structure of multivariate AR and ARMA systems: Regular and singular systems; the single and the mixed frequency case," Journal of Econometrics, Elsevier, vol. 192(2), pages 366-373.
    53. Reicher, Christopher Phillip, 2013. "A note on the identification of dynamic economic models with generalized shock processes," Kiel Working Papers 1821, Kiel Institute for the World Economy (IfW Kiel).
    54. K. Lawler & T. Vlasova & A. Moscardini, 2019. "Using System Dynamics in Macroeconomics," Вестник Киевского национального университета имени Тараса Шевченко. Экономика., Socionet;Киевский национальный университет имени Тараса Шевченко, vol. 3(204), pages 34-40.
    55. Elmar Mertens & James M Nason, 2015. "Inflation and Professional Forecast Dynamics: An Evaluation of Stickiness, Persistence, and Volatility," CAMA Working Papers 2015-06, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    56. Giovanni Angelini & Paolo Gorgi, 2018. "DSGE Models with Observation-Driven Time-Varying parameters," Tinbergen Institute Discussion Papers 18-030/III, Tinbergen Institute.
    57. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick & Wickens, Michael, 2017. "A Monte Carlo procedure for checking identification in DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 76(C), pages 202-210.
    58. Bernd Funovits, 2014. "Implications of Stochastic Singularity in Linear Multivariate Rational Expectations Models," Vienna Economics Papers vie1405, University of Vienna, Department of Economics.
    59. Linde, Jesper & LASEEN, PER & Ratto, Marco, 2019. "Identification Versus Misspecification in New Keynesian Monetary Policy Models," CEPR Discussion Papers 13492, C.E.P.R. Discussion Papers.
    60. Callum Jones & Mariano Kulish & Juan Pablo Nicolini, 2022. "Priors and the Slope of the Phillips Curve," Working Papers 165, Red Nacional de Investigadores en Economía (RedNIE).
    61. Tatiana Kirsanova & Celsa Machado & Ana Paula Ribeiro, 2020. "Tight and Loose, and Red and Blue: A 'Dance' of Macro Policies in the US," Working Papers 2020_14, Business School - Economics, University of Glasgow.
    62. Xiaoshan Chen & Eric M. Leeper & Campbell Leith, 2022. "Strategic interactions in U.S. monetary and fiscal policies," Quantitative Economics, Econometric Society, vol. 13(2), pages 593-628, May.
    63. McAdam, Peter & Warne, Anders, 2018. "Euro area real-time density forecasting with financial or labor market frictions," Working Paper Series 2140, European Central Bank.
    64. Sergey Ivashchenko & Willi Mutschler, 2019. "The effect of observables, functional specifications, model features and shocks on identification in linearized DSGE models," CQE Working Papers 8319, Center for Quantitative Economics (CQE), University of Muenster.
    65. Juan Carlos Parra-Alvarez & Olaf Posch & Mu-Chun Wang, 2017. "Identification and estimation of heterogeneous agent models: A likelihood approach," CREATES Research Papers 2017-35, Department of Economics and Business Economics, Aarhus University.
    66. Krogh, Tord S., 2015. "Macro frictions and theoretical identification of the New Keynesian Phillips curve," Journal of Macroeconomics, Elsevier, vol. 43(C), pages 191-204.
    67. Ho, Paul, 2024. "Estimating the effects of demographics on interest rates: A robust Bayesian perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 158(C).
    68. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    69. Majid Al-Sadoon & Piotr Zwiernik, 2019. "The identification problem for linear rational expectations models," Economics Working Papers 1669, Department of Economics and Business, Universitat Pompeu Fabra.
    70. D.S. Poskitt & Wenying Yao, 2012. "VAR Modeling and Business Cycle Analysis: A Taxonomy of Errors," Monash Econometrics and Business Statistics Working Papers 11/12, Monash University, Department of Econometrics and Business Statistics.
    71. Komunjer, Ivana & Zhu, Yinchu, 2020. "Likelihood ratio testing in linear state space models: An application to dynamic stochastic general equilibrium models," Journal of Econometrics, Elsevier, vol. 218(2), pages 561-586.
    72. Andrew Binning & Junior Maih, 2016. "Implementing the zero lower bound in an estimated regime-switching DSGE model," Working Paper 2016/3, Norges Bank.
    73. Prosper Dovonon & Alastair Hall, 2018. "The Asymptotic Properties of GMM and Indirect Inference under Second-order Identification," CIRANO Working Papers 2018s-37, CIRANO.
    74. Enrique Martínez García & Mark A. Wynne, 2014. "Assessing Bayesian model comparison in small samples," Globalization Institute Working Papers 189, Federal Reserve Bank of Dallas.
    75. Zhongjun Qu & Denis Tkachenko, 2023. "Using arbitrary precision arithmetic to sharpen identification analysis for DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 644-667, June.
    76. Angelini, Giovanni & Gorgi, Paolo, 2018. "DSGE Models with observation-driven time-varying volatility," Economics Letters, Elsevier, vol. 171(C), pages 169-171.
    77. Prosper Dovonon & Alastair R. Hall, 2017. "The Asymptotic Properties of GMM and Indirect Inference Under Second-Order Identification," Economics Discussion Paper Series 1705, Economics, The University of Manchester.
    78. Dovonon, Prosper & Hall, Alastair R., 2018. "The asymptotic properties of GMM and indirect inference under second-order identification," Journal of Econometrics, Elsevier, vol. 205(1), pages 76-111.
    79. Anna Mikusheva, 2014. "Estimation of dynamic stochastic general equilibrium models (in Russian)," Quantile, Quantile, issue 12, pages 1-21, February.
    80. Juan Carlos Parra‐Alvarez & Olaf Posch & Mu‐Chun Wang, 2023. "Estimation of Heterogeneous Agent Models: A Likelihood Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 304-330, April.
    81. Guglielminetti, Elisa & Pouraghdam, Meradj, 2018. "Time-varying job creation and macroeconomic shocks," Labour Economics, Elsevier, vol. 50(C), pages 156-179.
    82. Daniel O. Beltran & David Draper, 2016. "Estimating Dynamic Macroeconomic Models : How Informative Are the Data?," International Finance Discussion Papers 1175, Board of Governors of the Federal Reserve System (U.S.).
    83. Emanuele Bacchiocchi & Toru Kitagawa, 2020. "Locally- but not globally-identified SVARs," CeMMAP working papers CWP40/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    84. Massimo Franchi, 2013. "Comment on: Ravenna, F., 2007. Vector autoregressions and reduced form representations of DSGE models. Journal of Monetary Economics 54, 2048-2064," DSS Empirical Economics and Econometrics Working Papers Series 2013/2, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
    85. Stefano Grassi & Miguel Leon-Ledesma & Filippo Ferroni, 2016. "Fundamental shock selection in DSGE models," 2016 Meeting Papers 47, Society for Economic Dynamics.
    86. Fanelli, Luca & Sorge, Marco M., 2017. "Indeterminate forecast accuracy under indeterminacy," Journal of Macroeconomics, Elsevier, vol. 53(C), pages 57-70.
    87. Meradj Morteza Pouraghdam, 2016. "Three essays on the role of frictions in the economy [Trois essais sur le rôle du désaccord en économie]," SciencePo Working papers Main tel-03498781, HAL.
    88. Meenagh, David & Minford, Patrick & Wickens, Michael & Xu, Yongdeng, 2018. "Testing DSGE Models by indirect inference: a survey of recent findings," Cardiff Economics Working Papers E2018/14, Cardiff University, Cardiff Business School, Economics Section.
    89. Pedro Chaim & Márcio Poletti Laurini, 2022. "Data Cloning Estimation and Identification of a Medium-Scale DSGE Model," Stats, MDPI, vol. 6(1), pages 1-13, December.
    90. Deistler, Manfred & Wagner, Martin, 2017. "Cointegration in singular ARMA models," Economics Letters, Elsevier, vol. 155(C), pages 39-42.
    91. William Bednar & Nick Pretnar, 2019. "Home Production with Time to Consume," 2019 Meeting Papers 328, Society for Economic Dynamics.
    92. Olkhov, Victor, 2019. "Methods of Economic Theory: Variables, Transactions and Expectations as Functions of Risks," MPRA Paper 95628, University Library of Munich, Germany.
    93. Andreas Tryphonides, 2017. "Set Identified Dynamic Economies and Robustness to Misspecification," Papers 1712.03675, arXiv.org, revised Jan 2018.
    94. Saccal, Alessandro, 2020. "A note on minimality in Dynare," MPRA Paper 103656, University Library of Munich, Germany.
    95. Giovanni Angelini & Giuseppe Cavaliere & Luca Fanelli, 2022. "Bootstrap inference and diagnostics in state space models: With applications to dynamic macro models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 3-22, January.
    96. Dey, Jaya & Tsai, Yi-Chan, 2017. "Explaining the durable goods co-movement puzzle: A Bayesian approach," Journal of Macroeconomics, Elsevier, vol. 52(C), pages 75-99.
    97. Majid M. Al-Sadoon, 2020. "Regularized Solutions to Linear Rational Expectations Models," Papers 2009.05875, arXiv.org, revised Oct 2020.
    98. Filippo Ferroni & Stefano Grassi & Miguel A. León-Ledesma, 2017. "Selecting Primal Innovations in DSGE models," Working Paper Series WP-2017-20, Federal Reserve Bank of Chicago.
    99. Victor Olkhov, 2019. "Financial Variables, Market Transactions, and Expectations as Functions of Risk," IJFS, MDPI, vol. 7(4), pages 1-27, November.
    100. Funovits, Bernd, 2017. "The full set of solutions of linear rational expectations models," Economics Letters, Elsevier, vol. 161(C), pages 47-51.
    101. Majid M. Al-Sadoon, 2020. "The Spectral Approach to Linear Rational Expectations Models," Papers 2007.13804, arXiv.org, revised Aug 2024.
    102. Raffaella Giacomini, 2013. "The relationship between DSGE and VAR models," CeMMAP working papers 21/13, Institute for Fiscal Studies.
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  12. Komunjer, Ivana & Vuong, Quang, 2010. "Efficient estimation in dynamic conditional quantile models," Journal of Econometrics, Elsevier, vol. 157(2), pages 272-285, August.

    Cited by:

    1. Lavergne, Pascal & Patilea, Valentin, 2013. "Smooth minimum distance estimation and testing with conditional estimating equations: Uniform in bandwidth theory," Journal of Econometrics, Elsevier, vol. 177(1), pages 47-59.
    2. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2020. "The Efficiency Gap," Papers 2010.14146, arXiv.org, revised Sep 2022.
    3. Lee, Dong Jin & Kim, Tae-Hwan & Mizen, Paul, 2021. "Impulse response analysis in conditional quantile models with an application to monetary policy," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    4. Jungmo Yoon & Antonio F. Galvao, 2020. "Cluster robust covariance matrix estimation in panel quantile regression with individual fixed effects," Quantitative Economics, Econometric Society, vol. 11(2), pages 579-608, May.
    5. De Rezende, Rafael B., 2015. "Risks in macroeconomic fundamentals and excess bond returns predictability," Working Paper Series 295, Sveriges Riksbank (Central Bank of Sweden).
    6. White, Halbert & Kim, Tae-Hwan & Manganelli, Simone, 2010. "VAR for VaR: measuring systemic risk using multivariate regression quantiles," MPRA Paper 35372, University Library of Munich, Germany.
    7. Tae-Hwan Kim & Dong Jin Lee & Paul Mizen, 2020. "Impulse Response Analysis in Conditional Quantile Models and an Application to Monetary Policy," Working papers 2020rwp-164, Yonsei University, Yonsei Economics Research Institute.
    8. Dong Jin Lee, 2020. "Optimal tests for parameter breaking process in conditional quantile models," The Japanese Economic Review, Springer, vol. 71(3), pages 479-510, July.
    9. Antonio Galvao & Kengo Kato & Gabriel Montes-Rojas & Jose Olmo, 2014. "Testing linearity against threshold effects: uniform inference in quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 413-439, April.
    10. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.

  13. Komunjer, Ivana & Vuong, Quang, 2010. "Semiparametric Efficiency Bound In Time-Series Models For Conditional Quantiles," Econometric Theory, Cambridge University Press, vol. 26(2), pages 383-405, April.

    Cited by:

    1. Komunjer, Ivana & Ragusa, Giuseppe, 2009. "Existence and Uniqueness of Semiparametric Projections," University of California at San Diego, Economics Working Paper Series qt0wg3j51c, Department of Economics, UC San Diego.
    2. Ahnaf Rafi, 2023. "Efficient Semiparametric Estimation of Average Treatment Effects Under Covariate Adaptive Randomization," Papers 2305.08340, arXiv.org.
    3. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2020. "The Efficiency Gap," Papers 2010.14146, arXiv.org, revised Sep 2022.
    4. Juan Carlos Escanciano & Chuan Goh, 2018. "Quantile-Regression Inference With Adaptive Control of Size," Papers 1807.06977, arXiv.org, revised Sep 2019.
    5. Antonio Galvao & Kengo Kato & Gabriel Montes-Rojas & Jose Olmo, 2014. "Testing linearity against threshold effects: uniform inference in quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 413-439, April.
    6. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.

  14. Ivana Komunjer & Andres Santos, 2010. "Semi-parametric estimation of non-separable models: a minimum distance from independence approach," Econometrics Journal, Royal Economic Society, vol. 13(3), pages 28-55, October.
    See citations under working paper version above.
  15. Federico Echenique & Ivana Komunjer, 2009. "Testing Models With Multiple Equilibria by Quantile Methods," Econometrica, Econometric Society, vol. 77(4), pages 1281-1297, July.
    See citations under working paper version above.
  16. Graham Elliott & Ivana Komunjer & Allan Timmermann, 2008. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 122-157, March.
    See citations under working paper version above.
  17. Ivana Komunjer, 2007. "Asymmetric power distribution: Theory and applications to risk measurement," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 891-921.
    See citations under working paper version above.
  18. Graham Elliott & Allan Timmermann & Ivana Komunjer, 2005. "Estimation and Testing of Forecast Rationality under Flexible Loss," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(4), pages 1107-1125.

    Cited by:

    1. Clements, Michael P., 2008. "Explanations of the inconsistencies in survey respondents'forecasts," The Warwick Economics Research Paper Series (TWERPS) 870, University of Warwick, Department of Economics.
    2. Stan Hurn & Jing Tian & Lina Xu, 2021. "Assessing the Informational Content of Official Australian Bureau of Meteorology Forecasts of Wind Speed," The Economic Record, The Economic Society of Australia, vol. 97(319), pages 525-547, December.
    3. Lee, Tae-Hwy & Ullah, Aman & Wang, He, 2018. "The second-order bias of quantile estimators," Economics Letters, Elsevier, vol. 173(C), pages 143-147.
    4. Aretz, Kevin & Bartram, Söhnke M. & Pope, Peter F., 2011. "Asymmetric Loss Functions and the Rationality of Expected Stock Returns," MPRA Paper 47343, University Library of Munich, Germany.
    5. Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2015. "Central banks’ inflation forecasts under asymmetric loss: Evidence from four Latin-American countries," Economics Letters, Elsevier, vol. 129(C), pages 66-70.
    6. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Oil-Price Volatility: The Role of Financial Stress and Asymmetric Loss," Working Papers 201903, University of Pretoria, Department of Economics.
    7. Pierdzioch, Christian & Rülke, Jan-Christoph, 2013. "Do inflation targets anchor inflation expectations?," Economic Modelling, Elsevier, vol. 35(C), pages 214-223.
    8. Matei Demetrescu & Mu-Chun Wang, 2014. "Incorporating Asymmetric Preferences into Fan Charts and Path Forecasts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(2), pages 287-297, April.
    9. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Christian Pierdzioch, 2021. "El Nino, La Nina, and the Forecastability of the Realized Variance of Heating Oil Price Movements," Working Papers 202138, University of Pretoria, Department of Economics.
    10. Christian Pierdzioch & Jan-Christoph Rülke & Georg Stadtmann, 2013. "Oil price forecasting under asymmetric loss," Applied Economics, Taylor & Francis Journals, vol. 45(17), pages 2371-2379, June.
    11. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    12. Charles F. Manski, 2017. "Survey Measurement of Probabilistic Macroeconomic Expectations: Progress and Promise," NBER Working Papers 23418, National Bureau of Economic Research, Inc.
    13. Bonato, Matteo & Cepni, Oguzhan & Gupta, Rangan & Pierdzioch, Christian, 2023. "Climate risks and state-level stock market realized volatility," Journal of Financial Markets, Elsevier, vol. 66(C).
    14. Michael P. Clements, 2022. "Forecaster Efficiency, Accuracy, and Disagreement: Evidence Using Individual‐Level Survey Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(2-3), pages 537-568, March.
    15. Jens J. Krüger, 2014. "A multivariate evaluation of German output growth and inflation forecasts," Economics Bulletin, AccessEcon, vol. 34(3), pages 1410-1418.
    16. Rangan Gupta & Yuvana Jaichand & Christian Pierdzioch & Reneé van Eyden, 2023. "Realized Stock-Market Volatility of the United States and the Presidential Approval Rating," Mathematics, MDPI, vol. 11(13), pages 1-27, July.
    17. Timmermann, Allan & Elliott, Graham, 2007. "Economic Forecasting," CEPR Discussion Papers 6158, C.E.P.R. Discussion Papers.
    18. Robert Krol, 2014. "Forecast Bias of Government Agencies," Cato Journal, Cato Journal, Cato Institute, vol. 34(1), pages 99-112, Winter.
    19. mamatzakis, e & Christodoulakis, G, 2013. "Behavioural Asymmetries in the G7 Foreign Exchange Market," MPRA Paper 51615, University Library of Munich, Germany.
    20. Baghestani, Hamid & Marchon, Cassia, 2012. "An evaluation of private forecasts of interest rate targets in Brazil," Economics Letters, Elsevier, vol. 115(3), pages 352-355.
    21. Ron Alquist & Lutz Kilian, 2010. "What do we learn from the price of crude oil futures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 539-573.
    22. Michael P. Clements, 2014. "US Inflation Expectations and Heterogeneous Loss Functions, 1968–2010," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 1-14, January.
    23. Martin Ellison & Thomas J. Sargent, 2012. "A Defense Of The Fomc," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(4), pages 1047-1065, November.
    24. Hitesh Doshi & Kris Jacobs & Rui Liu, 2021. "Information in the Term Structure: A Forecasting Perspective," Management Science, INFORMS, vol. 67(8), pages 5255-5277, August.
    25. Fritsche, Ulrich & Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2015. "Forecasting the Brazilian real and the Mexican peso: Asymmetric loss, forecast rationality, and forecaster herding," International Journal of Forecasting, Elsevier, vol. 31(1), pages 130-139.
    26. Afees A. Salisu & Christian Pierdzioch & Rangan Gupta & Renee van Eyden, 2021. "Climate Risks and U.S. Stock-Market Tail Risks: A Forecasting Experiment Using over a Century of Data," Working Papers 202165, University of Pretoria, Department of Economics.
    27. Patrick Schmidt & Matthias Katzfuss & Tilmann Gneiting, 2021. "Interpretation of point forecasts with unknown directive," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 728-743, September.
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      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
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    38. Marcella Niglio, 2007. "Multi-step forecasts from threshold ARMA models using asymmetric loss functions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 16(3), pages 395-410, November.
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    42. Seth Pruitt, 2012. "Uncertainty Over Models and Data: The Rise and Fall of American Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44, pages 341-365, March.
    43. Novy, Dennis & Taylor, Alan, 2014. "Trade and Uncertainty," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100381, Verein für Socialpolitik / German Economic Association.
    44. Carmona, Carlos Capistran, 2005. "Bias in Federal Reserve Inflation Forecasts: Is the Federal Reserve Irrational or Just Cautious?," University of California at San Diego, Economics Working Paper Series qt6v28v0b6, Department of Economics, UC San Diego.
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    49. Hamid Baghestani, 2014. "On the loss structure of federal reserve forecasts of output growth," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 38(3), pages 518-527, July.
    50. Krüger, Jens J. & Hoss, Julian, 2012. "German business cycle forecasts, asymmetric loss and financial variables," Economics Letters, Elsevier, vol. 114(3), pages 284-287.
    51. Tae-Hwy Lee & Yiyao Wang, 2015. "Finding SPF Percentiles Closest to Greenbook," Working Papers 201503, University of California at Riverside, Department of Economics.
    52. Kontogeorgos, Georgios & Lambrias, Kyriacos, 2019. "An analysis of the Eurosystem/ECB projections," Working Paper Series 2291, European Central Bank.
    53. Conrad, Christian & Lahiri, Kajal, 2024. "Heterogeneous Expectations among Professional Forecasters," Working Papers 0754, University of Heidelberg, Department of Economics.
    54. Rangan Gupta & Christian Pierdzioch, 2021. "Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment," Energies, MDPI, vol. 14(23), pages 1-18, December.
    55. Tsuchiya, Yoichi, 2016. "Asymmetric loss and rationality of Chinese renminbi forecasts: An implication for the trade between China and the US," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 116-127.
    56. Salisu, Afees A. & Pierdzioch, Christian & Gupta, Rangan & Gabauer, David, 2022. "Forecasting stock-market tail risk and connectedness in advanced economies over a century: The role of gold-to-silver and gold-to-platinum price ratios," International Review of Financial Analysis, Elsevier, vol. 83(C).
    57. Frenkel, Michael & Rülke, Jan-Christoph & Zimmermann, Lilli, 2013. "Do private sector forecasters chase after IMF or OECD forecasts?," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 217-229.
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    65. Christian Pierdzioch & Jan C Rülke & Georg Stadtmann, 2012. "Forecasting the Dollar/British Pound Exchange Rate: Asymmetric Loss and Forecast Rationality," Economics Bulletin, AccessEcon, vol. 32(3), pages 213-213.
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    67. Qu, Ritong & Timmermann, Allan & Zhu, Yinchu, 2024. "Comparing forecasting performance with panel data," International Journal of Forecasting, Elsevier, vol. 40(3), pages 918-941.
    68. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A quantile-boosting approach to forecasting gold returns," The North American Journal of Economics and Finance, Elsevier, vol. 35(C), pages 38-55.
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    74. Auffhammer, Maximilian, 2005. "The Rationality of EIA Forecasts under Symmetric and Asymmetric Loss," CUDARE Working Papers 25017, University of California, Berkeley, Department of Agricultural and Resource Economics.
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    77. Wang, Yiyao & Lee, Tae-Hwy, 2014. "Asymmetric loss in the Greenbook and the Survey of Professional Forecasters," International Journal of Forecasting, Elsevier, vol. 30(2), pages 235-245.
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    80. Carlos Capistrán & Allan Timmermann, 2009. "Disagreement and Biases in Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(2‐3), pages 365-396, March.
    81. Krol, Robert, 2013. "Evaluating state revenue forecasting under a flexible loss function," International Journal of Forecasting, Elsevier, vol. 29(2), pages 282-289.
    82. E. Mamatzakis, 2014. "Revealing asymmetries in the loss function of WTI oil futures market," Empirical Economics, Springer, vol. 47(2), pages 411-426, September.
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    86. Joerg Doepke & Ulrich Fritsche & Boriss Siliverstovs, 2009. "Evaluating German Business Cycle Forecasts Under an Asymmetric Loss Function," Macroeconomics and Finance Series 200905, University of Hamburg, Department of Socioeconomics.
    87. Hamid Baghestani & Cassia Marchon, 2015. "On the accuracy of private forecasts of inflation and growth in Brazil," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(2), pages 370-381, April.
    88. Philip Hans Franses & Rianne Legerstee & Richard Paap, 2017. "Estimating loss functions of experts," Applied Economics, Taylor & Francis Journals, vol. 49(4), pages 386-396, January.
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  19. Komunjer, Ivana, 2005. "Quasi-maximum likelihood estimation for conditional quantiles," Journal of Econometrics, Elsevier, vol. 128(1), pages 137-164, September.
    See citations under working paper version above.
  20. Giacomini, Raffaella & Komunjer, Ivana, 2005. "Evaluation and Combination of Conditional Quantile Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 416-431, October.
    See citations under working paper version above.

Chapters

  1. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.

    Cited by:

    1. Patton, Andrew J. & Ziegel, Johanna F. & Chen, Rui, 2019. "Dynamic semiparametric models for expected shortfall (and Value-at-Risk)," Journal of Econometrics, Elsevier, vol. 211(2), pages 388-413.
    2. Oh, Dong Hwan & Patton, Andrew J., 2024. "Better the devil you know: Improved forecasts from imperfect models," Journal of Econometrics, Elsevier, vol. 242(1).
    3. Bruzda, Joanna, 2019. "Quantile smoothing in supply chain and logistics forecasting," International Journal of Production Economics, Elsevier, vol. 208(C), pages 122-139.
    4. Kraus, Daniel & Czado, Claudia, 2017. "D-vine copula based quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 1-18.
    5. Bayer, Sebastian, 2018. "Combining Value-at-Risk forecasts using penalized quantile regressions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 56-77.

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