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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. Travis J. Berge & Andrew C. Chang & Nitish R. Sinha, 2019. "Evaluating the Conditionality of Judgmental Forecasts," Finance and Economics Discussion Series 2019-002, Board of Governors of the Federal Reserve System (U.S.).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.

  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. Áureo de Paula & Imran Rasul & Pedro CL Souza, 2023. "Identifying network ties from panel data: Theory and an application to tax competition," CeMMAP working papers 21/23, Institute for Fiscal Studies.
    3. Kloodt, Nick, 2021. "Identification in a fully nonparametric transformation model with heteroscedasticity," Statistics & Probability Letters, Elsevier, vol. 170(C).
    4. 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.
    5. Hoderlein, Stefan & Su, Liangjun & White, Halbert & Yang, Thomas Tao, 2016. "Testing for monotonicity in unobservables under unconfoundedness," Journal of Econometrics, Elsevier, vol. 193(1), pages 183-202.
    6. 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.
    7. Liyu Dou & Jakub Kastl & John Lazarev, 2020. "Quantifying Delay Externalities in Airline Networks," Working Papers 2020-65, Princeton University. Economics Department..
    8. Timo Kuosmanen & Sheng Dai, 2023. "Modeling economies of scope in joint production: Convex regression of input distance function," Papers 2311.11637, arXiv.org.
    9. 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.
    10. 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.
    11. Christoph Breunig, 2016. "Specification Testing in Nonparametric Instrumental Quantile Regression," SFB 649 Discussion Papers SFB649DP2016-032, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    12. Áureo de Paula & Imran Rasul & Pedro CL Souza, 2018. "Recovering social networks from panel data: identification, simulations and an application," CeMMAP working papers CWP58/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Lewbel, Arthur & Lu, Xun & Su, Liangjun, 2015. "Specification testing for transformation models with an application to generalized accelerated failure-time models," Journal of Econometrics, Elsevier, vol. 184(1), pages 81-96.
    14. Christoph Breunig, 2019. "Specification Testing in Nonparametric Instrumental Quantile Regression," Papers 1909.10129, arXiv.org.
    15. Lin, Yingqian & Tu, Yundong & Yao, Qiwei, 2020. "Estimation for double-nonlinear cointegration," Journal of Econometrics, Elsevier, vol. 216(1), pages 175-191.
    16. Krief, Jerome M., 2017. "Direct instrumental nonparametric estimation of inverse regression functions," Journal of Econometrics, Elsevier, vol. 201(1), pages 95-107.
    17. 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.
    18. 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.
    19. 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.
    20. Christoph Breunig & Stephan Martin, 2020. "Nonclassical Measurement Error in the Outcome Variable," Papers 2009.12665, arXiv.org, revised May 2021.
    21. 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.
    22. Joachim Freyberger, 2021. "Normalizations and misspecification in skill formation models," Papers 2104.00473, arXiv.org, revised Jul 2022.
    23. 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.
    24. 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.
    25. 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.
    26. Irene Botosaru & Chris Muris & Krishna Pendakur, 2020. "Intertemporal Collective Household Models: Identification in Short Panels with Unobserved Heterogeneity in Resource Shares," Department of Economics Working Papers 2020-09, McMaster University.
    27. 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.

  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. Larch, Mario & Luckstead, Jeff & Yotov, Yoto, 2021. "Economic Sanctions and Agricultural Trade," School of Economics Working Paper Series 2021-16, LeBow College of Business, Drexel University.
    2. Gabriel J. Felbermayr & Jasmin Katrin Gröschl & Benedikt Heid, 2020. "Quantifying the Supply and Demand Effects of Natural Disasters Using Monthly Trade Data," CESifo Working Paper Series 8798, CESifo.
    3. French, Scott, 2016. "The composition of trade flows and the aggregate effects of trade barriers," Journal of International Economics, Elsevier, vol. 98(C), pages 114-137.
    4. Nan Li & Ana Maria Santacreu & Jie Cai, 2016. "Knowledge Diffusion and Trade Across Countries and Sectors," 2016 Meeting Papers 650, Society for Economic Dynamics.
    5. 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.
    6. Céline CARRERE & Marco FUGAZZA & Marcelo OLARREAGA & Frédéric ROBERT-NICOUD, 2016. "On the heterogeneous effect of trade on unemployment," Working Papers P180, FERDI.
    7. 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.
    8. Gordeev, Roman, 2020. "Comparative advantages of Russian forest products on the global market," Forest Policy and Economics, Elsevier, vol. 119(C).
    9. Bahar, Dany & Rosenow, Samuel & Stein, Ernesto & Wagner, Rodrigo, 2019. "Export take-offs and acceleration: Unpacking cross-sector linkages in the evolution of comparative advantage," World Development, Elsevier, vol. 117(C), pages 48-60.
    10. Konstantin Kucheryavyy & Gary Lyn & Andrés Rodríguez-Clare, 2023. "Grounded by Gravity: A Well-Behaved Trade Model with Industry-Level Economies of Scale," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(2), pages 372-412, April.
    11. 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.
    12. Egger, Peter H. & Nigai, Sergey, 2015. "Structural gravity with dummies only: Constrained ANOVA-type estimation of gravity models," Journal of International Economics, Elsevier, vol. 97(1), pages 86-99.
    13. Boehm, Johannes, 2015. "The impact of contract enforcement costs onoutsourcing and aggregate productivity," LSE Research Online Documents on Economics 64997, London School of Economics and Political Science, LSE Library.
    14. 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.
    15. 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.
    16. Alcalá, Francisco, 2016. "Specialization across goods and export quality," Journal of International Economics, Elsevier, vol. 98(C), pages 216-232.
    17. Sam Kortum & John Romalis & Brent Neiman & Jonathan Eaton, 2010. "Trade and the Global Recession," 2010 Meeting Papers 1340, Society for Economic Dynamics.
    18. Sampson, Thomas, 2023. "Technology gaps, trade and income," LSE Research Online Documents on Economics 117370, London School of Economics and Political Science, LSE Library.
    19. 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.
    20. William R. Kerr, 2013. "Heterogeneous Technology Diffusion and Ricardian Trade Patterns," Harvard Business School Working Papers 14-039, Harvard Business School.
    21. Fally, Thibault & Caron, Justin, 2018. "Per Capita Income, Consumption Patterns, and CO2 Emissions," CEPR Discussion Papers 13092, C.E.P.R. Discussion Papers.
    22. Scott French, 2017. "Comparative Advantage and Biased Gravity," Discussion Papers 2017-03, School of Economics, The University of New South Wales.
    23. 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.
    24. Joao Paulo Pessoa, 2016. "International competition and labor market adjustment," CEP Discussion Papers dp1411, Centre for Economic Performance, LSE.
    25. Redding, Stephen & Weinstein, David, 2019. "Aggregation and the Gravity Equation," CEPR Discussion Papers 13459, C.E.P.R. Discussion Papers.
    26. 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.
    27. 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.
    28. Eum, Jihyun & Sheldon, Ian & Thompson, Stanley, 2017. "Asymmetric Trade Costs: Agricultural Trade among Developing and Developed Countries," 2017: Globalization Adrift, December 3-5, 2017, Washington, D.C. 266814, International Agricultural Trade Research Consortium.
    29. Lionel Fontagné & Philippe Martin & Gianluca Orefice, 2017. "The International Elasticity Puzzle Is Worse Than You Think," Working Papers 2017-03, CEPII research center.
    30. Maria Bas & Thierry Mayer & Mathias Thoenig, 2017. "From micro to macro: Demand, supply, and heterogeneity in the trade elasticity," Sciences Po publications info:hdl:2441/nki2gcedn93, Sciences Po.
    31. Fontagné, Lionel & Santoni, Gianluca, 2021. "GVCs and the endogenous geography of RTAs," European Economic Review, Elsevier, vol. 132(C).
    32. Blank, Sven & Egger, Peter H. & Merlo, Valeria & Wamser, Georg, 2022. "A structural quantitative analysis of services trade de-liberalization," Journal of International Economics, Elsevier, vol. 137(C).
    33. Morrow, Peter M. & Trefler, Daniel, 2022. "How do endowments determine trade? quantifying the output mix, factor price, and skill-biased technology channels," Journal of International Economics, Elsevier, vol. 137(C).
    34. Costa, Francisco Junqueira Moreira da & Pessoa, João Paulo, 2019. "Winners and losers from China’s ascension in international trade: a structural approach," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 809, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    35. Papageorgiou, Chris & Perez-Sebastian, Fidel & Spatafora, Nikola, 2016. "Quality Upgrading and the Stages of Diversification," MPRA Paper 79400, University Library of Munich, Germany.
    36. 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).
    37. Keith Head & Thierry Mayer, 2013. "Gravity Equations: Workhorse, Toolkit, and Cookbook," SciencePo Working papers Main hal-00973067, HAL.
    38. French, Scott, 2017. "Revealed comparative advantage: What is it good for?," Journal of International Economics, Elsevier, vol. 106(C), pages 83-103.
    39. Binoy Goswami & Hiranya K. Nath, 2020. "India's Revealed Comparative Advantages in Merchandise Trade with Country Groups at Different Levels of Development," Working Papers 2001, Sam Houston State University, Department of Economics and International Business.
    40. Costas Arkolakis & Arnaud Costinot & Dave Donaldson & Andrés Rodríguez-Clare, 2015. "The Elusive Pro-Competitive Effects of Trade," NBER Working Papers 21370, National Bureau of Economic Research, Inc.
    41. 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.
    42. Stephen J. Redding & David E. Weinstein, 2018. "Accounting for Trade Patterns," Working Papers 2018-10, Princeton University. Economics Department..
    43. Benny Kleinman & Ernest Liu & Stephen J. Redding, 2023. "Dynamic Spatial General Equilibrium," Econometrica, Econometric Society, vol. 91(2), pages 385-424, March.
    44. Nelson Lind & Natalia Ramondo, 2018. "Trade with Correlation," NBER Working Papers 24380, National Bureau of Economic Research, Inc.
    45. 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.
    46. Soledad Zignago & Thierry Mayer, 2005. "Market Access in Global and Regional Trade," Working Papers hal-03588689, HAL.
    47. Benny Kleinman & Ernest Liu & Stephen J. Redding, 2022. "International Friends and Enemies," Working Papers 292, Princeton University, Department of Economics, Center for Economic Policy Studies..
    48. Redding, Stephen J., 2016. "Goods trade, factor mobility and welfare," Journal of International Economics, Elsevier, vol. 101(C), pages 148-167.
    49. 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.).
    50. 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.
    51. 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.
    52. 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.
    53. Hugo Rojas-Romagosa & Eddy Bekkers & Joseph F. Francois, 2015. "Melting Ice Caps and the Economic Impact of Opening the Northern Sea Route," CPB Discussion Paper 307, CPB Netherlands Bureau for Economic Policy Analysis.
    54. 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.
    55. 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," Working Papers 2019-12, CEPII research center.
    56. Amit Khandelwal & Pablo Fajgelbaum, 2013. "Measuring the Unequal Gains From Trade," 2013 Meeting Papers 456, Society for Economic Dynamics.
    57. Markus Brueckner & Ngo Van Long & Joaquin Vespignani & Ngo Van Long, 2020. "Trade, Education, and Income Inequality," CESifo Working Paper Series 8370, CESifo.
    58. 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.
    59. 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.
    60. 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.
    61. Sandra Poncet & Felipe Starosta de Waldemar, 2014. "Product relatedness and firm exports in China," PSE - Labex "OSE-Ouvrir la Science Economique" hal-01044982, HAL.
    62. Schetter, Ulrich, 2021. "A Structural Ranking of Economic Complexity," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242451, Verein für Socialpolitik / German Economic Association.
    63. Elizaveta Archanskaia & Guillaume Daudin, 2017. "Heterogeneity and the distance puzzle," SciencePo Working papers Main hal-01496258, HAL.
    64. Eddy Bekkers & Michael Landesmann & Indre Macskasi, 2017. "Trade in Services versus Trade in Manufactures: The Relation between the Role of Tacit Knowledge, the Scope for Catch up, and Income Elasticity," wiiw Working Papers 139, The Vienna Institute for International Economic Studies, wiiw.
    65. Minwook Kang, 2018. "Comparative advantage and strategic specialization," Review of International Economics, Wiley Blackwell, vol. 26(1), pages 1-19, February.
    66. Caron, Justin & Fally, Thibault & Markusen, James, 2020. "Per capita income and the demand for skills," Journal of International Economics, Elsevier, vol. 123(C).
    67. 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.
    68. 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.
    69. Ferdinando Monte, 2014. "Local Transmission of Trade Shocks," Working Papers 2014-001, Human Capital and Economic Opportunity Working Group.
    70. Peter Egger & Sergey K. Nigai, 2016. "World-Trade Growth Accounting," CESifo Working Paper Series 5831, CESifo.
    71. Michaels, Guy & Rauch, Ferdinand & Redding, Stephen, 2019. "Task specialization in U.S. cities from 1880-2000," LSE Research Online Documents on Economics 85163, London School of Economics and Political Science, LSE Library.
    72. Costas Arkolakis & Treb Allen, 2015. "Universal Gravity," 2015 Meeting Papers 28, Society for Economic Dynamics.
    73. Bombardini, Matilde & Li, Bingjing, 2020. "Trade, pollution and mortality in China," Journal of International Economics, Elsevier, vol. 125(C).
    74. Patrizio Bifulco & Jochen Gluck & Oliver Krebs & Bohdan Kukharskyy, 2022. "Single and Attractive: Uniqueness and Stability of Economic Equilibria under Monotonicity Assumptions," Papers 2209.02635, arXiv.org.
    75. 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.
    76. Park, Soonchan, 2020. "Quality of transport infrastructure and logistics as source of comparative advantage," Transport Policy, Elsevier, vol. 99(C), pages 54-62.
    77. 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.
    78. Mauro Lanati, 2013. "Estimating the elasticity of trade: the trade share approach," LEM Papers Series 2013/10, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    79. 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.
    80. Jerónimo Carballo & Alejandro Graziano & Georg Schaur & Christian Volpe Martincus, 2021. "Import Processing and Trade Costs," CESifo Working Paper Series 9170, CESifo.
    81. F. Langot & L. Patureau & T. Sopraseuth, 2014. "Fiscal Devaluation and Structural Gaps," Working papers 508, Banque de France.
    82. Heitor S. Pellegrina & Sebastian Sotelo, 2021. "Migration, Specialization, and Trade: Evidence from the Brazilian March to the West," Working Papers 681, Research Seminar in International Economics, University of Michigan.
    83. Larch, Mario & Yotov, Yoto V., 2016. "General equilibrium trade policy analysis with structural gravity," WTO Staff Working Papers ERSD-2016-08, World Trade Organization (WTO), Economic Research and Statistics Division.
    84. Cecilia Bellora & Jean-Marc Bourgeon, 2014. "Agricultural Trade, Biodiversity Effects and Food Price Volatility," Working Papers hal-01052971, HAL.
    85. Bolatto, Stefano & Moramarco, Graziano, 2023. "Gains from trade and their quantification: Does sectoral disaggregation matter?," International Economics, Elsevier, vol. 174(C), pages 44-68.
    86. Cecilia Bellora & Jean-Marc Bourgeon, 2017. "Food Trade, Biodiversity Effects and Price Volatility," Working Papers hal-01669332, HAL.
    87. Dr. Laurence Wicht, 2020. "The margin of importing sectors in the gains from trade," Working Papers 2020-07, Swiss National Bank.
    88. Eric O'N. Fisher & John Gilbert & Kathryn G. Marshall & Reza Oladi, 2015. "A New Measure of Economic Distance," CESifo Working Paper Series 5362, CESifo.
    89. 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.
    90. Nelson Lind & Natalia Ramondo, 2018. "Innovation, Knowledge Diffusion, and Globalization," NBER Working Papers 25071, National Bureau of Economic Research, Inc.
    91. 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.
    92. 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.
    93. Stefan BOJNEC & Imre FERTO, 2016. "Export competitiveness of the European Union in fruit and vegetable products in the global markets," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 62(7), pages 299-310.
    94. Schetter, Ulrich, 2016. "Comparative Advantages with Product Complexity and Product Quality," VfS Annual Conference 2016 (Augsburg): Demographic Change 145933, Verein für Socialpolitik / German Economic Association.
    95. Taylor Jaworski & Carl Kitchens & Sergey Nigai, 2023. "Highways And Globalization," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(4), pages 1615-1648, November.
    96. Shang-Jin Wei & Yinxi Xie, 2022. "On the Wedge Between the PPI and CPI Inflation Indicators," Staff Working Papers 22-5, Bank of Canada.
    97. Niko Korpar & Mario Larch & Roman Stöllinger, 2023. "The European carbon border adjustment mechanism: a small step in the right direction," International Economics and Economic Policy, Springer, vol. 20(1), pages 95-138, February.
    98. Li, Wenchao, 2019. "Time barrier to export for OECD countries," Economics Letters, Elsevier, vol. 175(C), pages 106-112.
    99. 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.
    100. Han QI & Haichao Fan & Edwin Lai, 2013. "Global Gains from Reduction of Trade Costs," 2013 Meeting Papers 1283, Society for Economic Dynamics.
    101. Nelson Lind & Natalia Ramondo, 2023. "Global Innovation and Knowledge Diffusion," American Economic Review: Insights, American Economic Association, vol. 5(4), pages 494-510, December.
    102. 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.
    103. Andrei A. Levchenko & Jing Zhang, 2011. "The Evolution of Comparative Advantage: Measurement and Welfare Implications," NBER Working Papers 16806, National Bureau of Economic Research, Inc.
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  5. 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.

    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.
    3. Junlong Feng & Sokbae Lee, 2023. "Individual Welfare Analysis: Random Quasilinear Utility, Independence, and Confidence Bounds," Papers 2304.01921, arXiv.org, revised Aug 2023.
    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. 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.
    2. 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.
    3. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2017. "Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers," ETA: Economic Theory and Applications 253725, Fondazione Eni Enrico Mattei (FEEM).
    4. 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.
    5. Jens J. Krüger, 2014. "A multivariate evaluation of German output growth and inflation forecasts," Economics Bulletin, AccessEcon, vol. 34(3), pages 1410-1418.
    6. Travis J. Berge & Andrew C. Chang & Nitish R. Sinha, 2019. "Evaluating the Conditionality of Judgmental Forecasts," Finance and Economics Discussion Series 2019-002, Board of Governors of the Federal Reserve System (U.S.).
    7. Hans Christian Müller-Dröge & Tara M. Sinclair & Herman O. Stekler, 2014. "Evaluating Forecasts Of A Vector Of Variables: A German Forecasting Competition," Working Papers 2014-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    8. 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.
    9. Andrew Martinez, 2017. "Testing for Differences in Path Forecast Accuracy: Forecast-Error Dynamics Matter," Working Papers (Old Series) 1717, Federal Reserve Bank of Cleveland.
    10. 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.
    11. 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.
    12. 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.
    13. Jingwei Pan, 0000. "Evaluating Correlation Forecasts Under Asymmetric Loss," Proceedings of Economics and Finance Conferences 11413234, International Institute of Social and Economic Sciences.
    14. 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.
    15. 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.
    16. An, Zidong & Zheng, Xinye, 2023. "Diligent forecasters can make accurate predictions despite disagreeing with the consensus," Economic Modelling, Elsevier, vol. 125(C).
    17. 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.
    18. 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.
    19. Christodoulakis, George, 2020. "Estimating the term structure of commodity market preferences," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1146-1163.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. Gloria Gonzalez-Rivera & Wei Lin, 2015. "Interval-valued Time Series Models: Estimation based on Order Statistics. Exploring the Agriculture Marketing Service Data," Working Papers 201505, University of California at Riverside, Department of Economics.
    28. 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.
    29. 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.).
    30. 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.
    31. 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.
    32. 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.
    33. Boskabadi, Elahe, 2022. "Economic policy uncertainty and forecast bias in the survey of professional forecasters," MPRA Paper 115081, University Library of Munich, Germany.
    34. 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.
    35. 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.
    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.

  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. 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.
    2. 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.
    3. 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.
    4. Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
    5. Komunjer, Ivana & Vuong, Quang, 2010. "Efficient estimation in dynamic conditional quantile models," Journal of Econometrics, Elsevier, vol. 157(2), pages 272-285, August.
    6. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    7. 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.

  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. James E. Anderson & Yoto V. Yotov, 2010. "Specialization: Pro- and Anti-globalizing, 1990-2002," NBER Working Papers 16301, National Bureau of Economic Research, Inc.
    3. Chen, Natalie & Novy, Dennis, 2008. "International Trade Integration: A Disaggregated Approach," CEPR Discussion Papers 7103, C.E.P.R. Discussion Papers.
    4. Morrow, Peter M., 2010. "Ricardian-Heckscher-Ohlin comparative advantage: Theory and evidence," Journal of International Economics, Elsevier, vol. 82(2), pages 137-151, November.
    5. 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.
    6. Sergei Koulayev, 2009. "Estimating demand in search markets: the case of online hotel bookings," Working Papers 09-16, Federal Reserve Bank of Boston.
    7. 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.
    8. Arnaud Costinot, 2009. "An Elementary Theory of Comparative Advantage," Econometrica, Econometric Society, vol. 77(4), pages 1165-1192, July.
    9. Chor, Davin, 2010. "Unpacking sources of comparative advantage: A quantitative approach," Journal of International Economics, Elsevier, vol. 82(2), pages 152-167, November.
    10. 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.
    11. Matilde Bombardini & Giovanni Gallipoli & Germán Pupato, 2009. "Skill Dispersion and Trade Flows," NBER Working Papers 15097, National Bureau of Economic Research, Inc.
    12. Costinot, Arnaud, 2007. "On the Origins of Comparative Advantage," University of California at San Diego, Economics Working Paper Series qt07g7g8h8, Department of Economics, UC San Diego.
    13. James E. Anderson, 2009. "Gravity, Productivity and the Pattern of Production and Trade," NBER Working Papers 14642, National Bureau of Economic Research, Inc.
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Demetrescu, Matei & Hacıoğlu Hoke, Sinem, 2019. "Predictive regressions under asymmetric loss: Factor augmentation and model selection," International Journal of Forecasting, Elsevier, vol. 35(1), pages 80-99.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. León, Ángel & Ñíguez, Trino-Manuel, 2020. "Modeling asset returns under time-varying semi-nonparametric distributions," Journal of Banking & Finance, Elsevier, vol. 118(C).
    12. 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.
    13. 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.
    14. 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.
    15. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    16. 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.
    17. 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.
    18. 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).
    19. 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).
    20. Yong Bao, 2013. "On Sample Skewness and Kurtosis," Econometric Reviews, Taylor & Francis Journals, vol. 32(4), pages 415-448, December.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. BOUADDI, Mohammed & ROMBOUTS, Jeroen V.K., 2007. "Mixed exponential power asymmetric conditional heteroskedasticity," LIDAM Discussion Papers CORE 2007097, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    26. Matteo Bonato & Rangan Gupta & Chi Keung Marco Lau & Shixuan Wang, 2019. "Moments-Based Spillovers across Gold and Oil Markets," Working Papers 201966, University of Pretoria, Department of Economics.
    27. 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.
    28. 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.
    29. Liu, Xiaochun, 2019. "On tail fatness of macroeconomic dynamics," Journal of Macroeconomics, Elsevier, vol. 62(C).
    30. Komunjer, Ivana & Vuong, Quang, 2010. "Efficient estimation in dynamic conditional quantile models," Journal of Econometrics, Elsevier, vol. 157(2), pages 272-285, August.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    35. 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.
    36. de Castro, Luciano & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2020. "Quantile selection in non-linear GMM quantile models," Economics Letters, Elsevier, vol. 195(C).
    37. 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.
    38. 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.
    39. 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. Yuichi Kitamura & Louise Laage, 2018. "Nonparametric Analysis of Finite Mixtures," Papers 1811.02727, arXiv.org.
    5. 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.
    6. Komunjer, Ivana, 2007. "Global Identification In Nonlinear Semiparametric Models," University of California at San Diego, Economics Working Paper Series qt8dk0n386, Department of Economics, UC San Diego.
    7. Lukasz Balbus & Pawel Dziewulski & Kevin Reffett & Lukasz Wozny, 2020. "Markov distributional equilibrium dynamics in games with complementarities and no aggregate risk," KAE Working Papers 2020-052, Warsaw School of Economics, Collegium of Economic Analysis.
    8. 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.
    9. Miyauchi, Yuhei, 2016. "Structural estimation of pairwise stable networks with nonnegative externality," Journal of Econometrics, Elsevier, vol. 195(2), pages 224-235.
    10. Lawrence Schmidt & Allan Timmermann & Russ Wermers, 2016. "Runs on Money Market Mutual Funds," American Economic Review, American Economic Association, vol. 106(9), pages 2625-2657, September.
    11. 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).
    12. Áureo de Paula, 2013. "Econometric Analysis of Games with Multiple Equilibria," Annual Review of Economics, Annual Reviews, vol. 5(1), pages 107-131, May.
    13. 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.
    14. Vincent P. Crawford & Miguel A. Costa-Gomes & Nagore Iriberri, 2010. "Strategic Thinking," Levine's Working Paper Archive 661465000000001148, David K. Levine.
    15. Giovanni Cespa & Xavier Vives, 2011. "Expectations, Liquidity, and Short-term Trading," CESifo Working Paper Series 3390, CESifo.
    16. Giovanni Cespa & Xavier Vives, 2014. "The Beauty Contest and Short-Term Trading," CSEF Working Papers 383, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    17. 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. 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. 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.
    6. 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.
    7. Atsushi Inoue & Lutz Kilian & Fatma Burcu Kiraz, 2009. "Do Actions Speak Louder Than Words? Household Expectations of Inflation Based on Micro Consumption Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(7), pages 1331-1363, October.
    8. 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.
    9. 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.
    10. 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.
    11. Demirer, Riza & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2019. "Time-varying risk aversion and realized gold volatility," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    12. 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).
    13. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2020. "Proper scoring rules for evaluating asymmetry in density forecasting," Working Papers No 06/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    14. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2017. "Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers," ETA: Economic Theory and Applications 253725, Fondazione Eni Enrico Mattei (FEEM).
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. Charles F. Manski, 2017. "Survey Measurement of Probabilistic Macroeconomic Expectations: Progress and Promise," NBER Working Papers 23418, National Bureau of Economic Research, Inc.
    20. 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.
    21. 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.
    22. Born, Benjamin & Enders, Zeno & Müller, Gernot, 2023. "On FIRE, news, and expectations," CEPR Discussion Papers 18259, C.E.P.R. Discussion Papers.
      • Born, Benjamin & Enders, Zeno & Müller, Gernot J., 2023. "On FIRE, news, and expectations," Working Papers 42, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    23. Brissimis, Sophocles & Migiakis, Petros, 2010. "Inflation persistence and the rationality of inflation expectations," MPRA Paper 29052, University Library of Munich, Germany.
    24. Jens J. Krüger, 2014. "A multivariate evaluation of German output growth and inflation forecasts," Economics Bulletin, AccessEcon, vol. 34(3), pages 1410-1418.
    25. 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.
    26. Anatolyev, Stanislav, 2009. "Dynamic modeling under linear-exponential loss," Economic Modelling, Elsevier, vol. 26(1), pages 82-89, January.
    27. Christian Pierdzioch & Monique B. Reid & Rangan Gupta, 2014. "Forecasting the South African Inflation Rate: On Asymmetric Loss and Forecast Rationality," Working Papers 201475, University of Pretoria, Department of Economics.
    28. Hakan Kara & Hande Kucuk-Tuğer, 2010. "Inflation expectations in Turkey: learning to be rational," Applied Economics, Taylor & Francis Journals, vol. 42(21), pages 2725-2742.
    29. Rybacki Jakub, 2020. "Macroeconomic forecasting in Poland: The role of forecasting competitions," Central European Economic Journal, Sciendo, vol. 7(54), pages 1-11, January.
    30. Lutz Kilian & Simone Manganelli, 2008. "The Central Banker as a Risk Manager: Estimating the Federal Reserve's Preferences under Greenspan," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(6), pages 1103-1129, September.
    31. 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.
    32. Avino, Davide & Nneji, Ogonna, 2012. "Are CDS spreads predictable? An analysis of linear and non-linear forecasting models," MPRA Paper 42848, University Library of Munich, Germany.
    33. 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.
    34. 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.
    35. Isiklar, Gultekin, 2005. "On aggregation bias in fixed-event forecast efficiency tests," Economics Letters, Elsevier, vol. 89(3), pages 312-316, December.
    36. Behrens, Christoph & Pierdzioch, Christian & Risse, Marian, 2018. "Testing the optimality of inflation forecasts under flexible loss with random forests," Economic Modelling, Elsevier, vol. 72(C), pages 270-277.
    37. Clements, Michael P., 2012. "Do professional forecasters pay attention to data releases?," International Journal of Forecasting, Elsevier, vol. 28(2), pages 297-308.
    38. 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.
    39. 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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    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. McAleer, M.J. & Jiménez-Martín, J.A. & Pérez-Amaral, T., 2010. "GFC-Robust Risk Management Strategies under the Basel Accord," Econometric Institute Research Papers EI 2010-59, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    4. Ross Askanazi & Francis X. Diebold & Frank Schorfheide & Minchul Shin, 2018. "On the Comparison of Interval Forecasts," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 953-965, November.
    5. Thiele, Stephen, 2019. "Detecting underestimates of risk in VaR models," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 12-20.
    6. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    7. Filip Žikeš & Jozef Baruník, 2016. "Semi-parametric Conditional Quantile Models for Financial Returns and Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 185-226.
    8. Michael P. Clements & David I. Harvey, 2010. "Forecast encompassing tests and probability forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(6), pages 1028-1062.
    9. Cathy W. S. Chen & Richard Gerlach & Bruce B. K. Hwang & Michael McAleer, 2011. "Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range," KIER Working Papers 775, Kyoto University, Institute of Economic Research.
    10. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
    11. Lloyd, S. & Manuel, E. & Panchev, K., 2021. "Foreign Vulnerabilities, Domestic Risks: The Global Drivers of GDP-at-Risk," Cambridge Working Papers in Economics 2156, Faculty of Economics, University of Cambridge.
    12. Joshua Angrist & Victor Chernozhukov & Ivan Fernandez-Val, 2004. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," NBER Working Papers 10428, National Bureau of Economic Research, Inc.
    13. 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.
    14. Krenar AVDULAJ & Jozef BARUNIK, 2013. "Can We Still Benefit from International Diversification? The Case of the Czech and German Stock Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(5), pages 425-442, November.
    15. Jimenez-Martin, Juan-Angel & McAleer, Michael & Pérez-Amaral, Teodosio & Santos, Paulo Araújo, 2013. "GFC-robust risk management under the Basel Accord using extreme value methodologies," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 223-237.
    16. Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea, 2021. "Nowcasting Tail Risk to Economic Activity at a Weekly Frequency," CEPR Discussion Papers 16496, C.E.P.R. Discussion Papers.
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  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, 2006. "Testing for nonlinearity in mean in the presence of heteroskedasticity," Stan Hurn Discussion Papers 2006-02, School of Economics and Finance, Queensland University of Technology.
    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. 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.
    5. Yang Yang & Tae-Hwy Lee, 2004. "Bagging Binary Predictors for Time Series," Econometric Society 2004 Far Eastern Meetings 512, Econometric Society.
    6. Allan Timmermann & Andrew J. Patton, 2004. "Properties of Optimal Forecasts," Econometric Society 2004 North American Winter Meetings 234, Econometric Society.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.

  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. 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.
    5. 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).
    6. 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.
    7. 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.
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    11. 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.
    12. Christophe Boucher & Sessi Tokpavi, 2018. "Stocks and Bonds: Flight-to-Safety for Ever?," Working Papers hal-04141705, HAL.
    13. Alhamzawi, Rahim, 2016. "Bayesian model selection in ordinal quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 68-78.
    14. 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.
    15. J. Carlos Escanciano & Carlos Velasco, 2010. "Specification tests of parametric dynamic conditional quantiles," Post-Print hal-00732534, HAL.
    16. 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.
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    19. 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.
    20. 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.
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    25. Ying-Ying Lee, 2015. "Interpretation and Semiparametric Efficiency in Quantile Regression under Misspecification," Econometrics, MDPI, vol. 4(1), pages 1-14, December.
    26. 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.
    27. 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.
    28. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2020. "The Efficiency Gap," Papers 2010.14146, arXiv.org, revised Sep 2022.
    29. 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..
    30. Ivana Komunjer, 2004. "Asymmetric Power Distribution: Theory and Applications to Risk Measurement," Econometric Society 2004 Latin American Meetings 44, Econometric Society.
    31. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
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    33. 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.
    34. 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.
    35. 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.
    36. 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).
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    38. Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
    39. DeRossi, G. & Harvey, A., 2006. "Time-Varying Quantiles," Cambridge Working Papers in Economics 0649, Faculty of Economics, University of Cambridge.
    40. 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.
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    42. Akanksha Negi, 2020. "Doubly weighted M-estimation for nonrandom assignment and missing outcomes," Papers 2011.11485, arXiv.org.
    43. Christophe Boucher & Sessi Tokpavi, 2019. "Stocks and Bonds: Flight-to-Safety for Ever?," Post-Print hal-02067096, HAL.
    44. 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.
    45. 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.
    46. Komunjer, Ivana & Vuong, Quang, 2010. "Efficient estimation in dynamic conditional quantile models," Journal of Econometrics, Elsevier, vol. 157(2), pages 272-285, August.
    47. 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.
    48. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 197-207, April.
    49. Timo Dimitriadis & Julie Schnaitmann, 2019. "Forecast Encompassing Tests for the Expected Shortfall," Papers 1908.04569, arXiv.org, revised Aug 2020.
    50. 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.
    51. 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.
    52. 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.
    53. Sessi Tokpavi & Christophe Boucher, 2018. "Stocks and Bonds: Flight-to-Safety for Ever?," EconomiX Working Papers 2018-39, University of Paris Nanterre, EconomiX.
    54. 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.
    55. 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.
    56. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 197-207.
    57. 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.
    58. 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.
    59. 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.
    60. de Castro, Luciano & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2020. "Quantile selection in non-linear GMM quantile models," Economics Letters, Elsevier, vol. 195(C).
    61. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2022. "Characterizing M-estimators," Papers 2208.08108, arXiv.org.
    62. 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.
    63. 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.
    64. 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.
    65. Rahim Alhamzawi, 2016. "Bayesian Analysis of Composite Quantile Regression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(2), pages 358-373, October.
    66. 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.
    67. 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.
    68. Alecos Papadopoulos, 2024. "Some notes on the asymmetry of the regression error," Journal of Productivity Analysis, Springer, vol. 61(1), pages 37-42, February.
    69. 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.

  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. 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. Sönksen, Jantje & Grammig, Joachim, 2021. "Empirical asset pricing with multi-period disaster risk: A simulation-based approach," Journal of Econometrics, Elsevier, vol. 222(1), pages 805-832.
    3. Jean-Jacques Forneron, 2019. "A Sieve-SMM Estimator for Dynamic Models," Papers 1902.01456, arXiv.org, revised Jan 2023.
    4. Shuowen Chen, 2022. "Indirect Inference for Nonlinear Panel Models with Fixed Effects," Papers 2203.10683, arXiv.org, revised Apr 2022.
    5. Bao, Yong & Yu, Xuewen, 2023. "Indirect inference estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1027-1053.
    6. 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.
    7. Veronika Czellar & David T. Frazier & Eric Renault, 2020. "Approximate Maximum Likelihood for Complex Structural Models," Papers 2006.10245, arXiv.org.
    8. 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.
    9. 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. Lee Tae-Hwy & Ullah Aman & Mao Millie Yi, 2021. "Maximum Entropy Analysis of Consumption-based Capital Asset Pricing Model and Volatility," Journal of Econometric Methods, De Gruyter, vol. 10(1), pages 1-19, January.
    2. Timothy Christensen & Benjamin Connault, 2019. "Counterfactual Sensitivity and Robustness," Papers 1904.00989, arXiv.org, revised May 2022.
    3. 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.
    4. Andreas Tryphonides, 2017. "Conditional moment restrictions and the role of density information in estimated structural models," SFB 649 Discussion Papers SFB649DP2017-016, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Andreas Tryphonides, 2018. "Tilting Approximate Models," Papers 1805.10869, arXiv.org, revised Mar 2024.
    6. 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.
    7. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.
    8. Naoya Sueishi, 2022. "A Misuse of Specification Tests," Papers 2211.11915, arXiv.org.
    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. Tim Bollerslev & Andrew J. Patton & Rogier Quaedvlieg, 2016. "Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions," CREATES Research Papers 2016-10, Department of Economics and Business Economics, Aarhus University.
    2. Daniel Kaufmann, 2017. "Is Deflation Costly After All? The Perils of Erroneous Historical Classifications," IRENE Working Papers 17-09, IRENE Institute of Economic Research.
    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.
    4. 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.
    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. 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," TSE Working Papers 12-295, Toulouse School of Economics (TSE).
    2. 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.
    3. 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.
    4. Nishimura, Hiroki, 2021. "Revealed preferences of individual players in sequential games," Journal of Mathematical Economics, Elsevier, vol. 96(C).
    5. , P. & ,, 2014. "On the consistency of data with bargaining theories," Theoretical Economics, Econometric Society, vol. 9(1), January.
    6. Laurens Cherchye & Thomas Demuynck & Bram De Rock, 2013. "The empirical content of Cournot competition," ULB Institutional Repository 2013/151678, ULB -- Universite Libre de Bruxelles.
    7. Hubner, Stefan, 2016. "Topics in nonparametric identification and estimation," Other publications TiSEM 08fce56b-3193-46e0-871b-0, Tilburg University, School of Economics and Management.
    8. Jean-paul Chavas & Martina Menon & Federico Perali, 2014. "The Sharing Rule: Where Is It?," Working Papers 27/2014, University of Verona, Department of Economics.
    9. 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).
    10. 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.
    11. 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.
    12. Jean-Paul Chavas & Eleonora Matteazzi & Martina Menon & Federico Perali, 2022. "(In)Efficient Bargaining in the Family," Working Papers 2, SITES.
    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.

  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. Áureo de Paula & Imran Rasul & Pedro CL Souza, 2023. "Identifying network ties from panel data: Theory and an application to tax competition," CeMMAP working papers 21/23, Institute for Fiscal Studies.
    2. 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).
    3. 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.
    4. 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.
    5. 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.
    6. Áureo de Paula & Imran Rasul & Pedro CL Souza, 2018. "Recovering social networks from panel data: identification, simulations and an application," CeMMAP working papers CWP58/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2020. "The Efficiency Gap," Papers 2010.14146, arXiv.org, revised Sep 2022.
    8. 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.
    9. 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.
    10. Iaria, Alessandro & ,, 2020. "Identification and Estimation of Demand for Bundles," CEPR Discussion Papers 14363, C.E.P.R. Discussion Papers.
    11. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2022. "Characterizing M-estimators," Papers 2208.08108, arXiv.org.

  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. 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).
    2. 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.
    3. 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.
    4. Chatelain, Jean-Bernard & Ralf, Kirsten, 2017. "Can we Identify the Fed's Preferences?," MPRA Paper 76831, University Library of Munich, Germany.
    5. 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.
    6. 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.
    7. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    8. Kyle Jurado, 2016. "Advance Information and Distorted Beliefs in Macroeconomic and Financial Fluctuations," 2016 Meeting Papers 154, Society for Economic Dynamics.
    9. Olkhov, Victor, 2019. "New Essentials of Economic Theory," MPRA Paper 95065, University Library of Munich, Germany.
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    60. Guglielminetti, Elisa & Pouraghdam, Meradj, 2018. "Time-varying job creation and macroeconomic shocks," Labour Economics, Elsevier, vol. 50(C), pages 156-179.
    61. Ivashchenko, Sergey & Mutschler, Willi, 2020. "The effect of observables, functional specifications, model features and shocks on identification in linearized DSGE models," Economic Modelling, Elsevier, vol. 88(C), pages 280-292.
    62. Yasuo Hirose & Hirokuni Iiboshi & Mototsugu Shintani & Kozo Ueda, 2022. "Estimating a Behavioral New Keynesian Model with the Zero Lower Bound," CARF F-Series CARF-F-535, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    63. Callum J. Jones & Mariano Kulish & Juan Pablo Nicolini, 2021. "Priors and the Slope of the Phillips Curve," Working Papers 778, Federal Reserve Bank of Minneapolis.
    64. 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.).
    65. 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.
    66. 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.
    67. 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.
    68. 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.
    69. 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.
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    72. Ho, Paul, 2023. "Global robust Bayesian analysis in large models," Journal of Econometrics, Elsevier, vol. 235(2), pages 608-642.
    73. Stefano Grassi & Miguel Leon-Ledesma & Filippo Ferroni, 2016. "Fundamental shock selection in DSGE models," 2016 Meeting Papers 47, Society for Economic Dynamics.
    74. Fanelli, Luca & Sorge, Marco M., 2017. "Indeterminate forecast accuracy under indeterminacy," Journal of Macroeconomics, Elsevier, vol. 53(C), pages 57-70.
    75. 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.
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    77. 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.
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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. De Rezende, Rafael B., 2015. "Risks in macroeconomic fundamentals and excess bond returns predictability," Working Paper Series 295, Sveriges Riksbank (Central Bank of Sweden).
    3. 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.
    4. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2020. "The Efficiency Gap," Papers 2010.14146, arXiv.org, revised Sep 2022.
    5. 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).
    6. 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.
    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. J. C. Escanciano & S. C. Goh, 2019. "Quantile-Regression Inference With Adaptive Control of Size," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1382-1393, July.
    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. Capistrán, Carlos & López-Moctezuma, Gabriel, 2014. "Forecast revisions of Mexican inflation and GDP growth," International Journal of Forecasting, Elsevier, vol. 30(2), pages 177-191.
    4. Lee, Tae-Hwy & Ullah, Aman & Wang, He, 2018. "The second-order bias of quantile estimators," Economics Letters, Elsevier, vol. 173(C), pages 143-147.
    5. Wieland, Volker & Wolters, Maik, 2013. "Forecasting and Policy Making," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 239-325, Elsevier.
    6. 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.
    7. Emilio Zanetti Chini, 2018. "Forecaster’s utility and forecasts coherence," CREATES Research Papers 2018-01, Department of Economics and Business Economics, Aarhus University.
    8. 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.
    9. 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.
    10. Francis X. Diebold & Minchul Shin, 2017. "Assessing point forecast accuracy by stochastic error distance," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 588-598, October.
    11. Pierdzioch, Christian & Rülke, Jan-Christoph, 2013. "Do inflation targets anchor inflation expectations?," Economic Modelling, Elsevier, vol. 35(C), pages 214-223.
    12. Demirer, Riza & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2019. "Time-varying risk aversion and realized gold volatility," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    13. 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.
    14. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2020. "Proper scoring rules for evaluating asymmetry in density forecasting," Working Papers No 06/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    15. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2017. "Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers," ETA: Economic Theory and Applications 253725, Fondazione Eni Enrico Mattei (FEEM).
    16. 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.
    17. 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.
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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. Kraus, Daniel & Czado, Claudia, 2017. "D-vine copula based quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 1-18.
    2. Bayer, Sebastian, 2018. "Combining Value-at-Risk forecasts using penalized quantile regressions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 56-77.
    3. 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.
    4. Bruzda, Joanna, 2019. "Quantile smoothing in supply chain and logistics forecasting," International Journal of Production Economics, Elsevier, vol. 208(C), pages 122-139.

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