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Peter A. Zadrozny

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.

Working papers

  1. Peter A. Zadrozny, 2022. "Linear Identification of Linear Rational-Expectations Models by Exogenous Variables Reconciles Lucas and Sims," CESifo Working Paper Series 10078, CESifo.

    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. Peter A. Zadrozny & Baoline Chen, 2019. "Weighted-Covariance Factor Decomposition Of Varma Models Applied To Forecasting Quarterly U.S. Real Gdp At Monthly Intervals," Economic Working Papers 516, Bureau of Labor Statistics.

    Cited by:

    1. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.

  3. Peter A. Zadrozny, 2016. "Real-Time State Space Method for Computing Smoothed Estimates of Future Revisions of U.S. Monthly Chained CPI," CESifo Working Paper Series 5897, CESifo.

    Cited by:

    1. Jan P.A.M. Jacobs & Samad Sarferaz & Simon van Norden & Jan-Egbert Sturm, 2013. "Modeling Multivariate Data Revisions," CIRANO Working Papers 2013s-44, CIRANO.

  4. Peter A. Zadrozny, 2015. "Extended Yule-Walker Identification of Varma Models with Single- or Mixed- Frequency Data," Economic Working Papers 485, Bureau of Labor Statistics.

    Cited by:

    1. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    2. Zadrozny, Peter A., 2022. "Linear identification of linear rational-expectations models by exogenous variables reconciles Lucas and Sims," CFS Working Paper Series 682, Center for Financial Studies (CFS).
    3. Deistler, Manfred & Koelbl, Lukas & Anderson, Brian D.O., 2017. "Non-identifiability of VMA and VARMA systems in the mixed frequency case," Econometrics and Statistics, Elsevier, vol. 4(C), pages 31-38.
    4. Michael Thornton & Marcus Chambers, 2016. "Continuous Time ARMA Processes: Discrete Time Representation and Likelihood Evaluation," Discussion Papers 16/10, Department of Economics, University of York.
    5. Morris, Stephen D., 2017. "DSGE pileups," Journal of Economic Dynamics and Control, Elsevier, vol. 74(C), pages 56-86.
    6. Celina Pestano-Gabino & Concepción González-Concepción & María Candelaria Gil-Fariña, 2024. "VARMA Models with Single- or Mixed-Frequency Data: New Conditions for Extended Yule–Walker Identification," Mathematics, MDPI, vol. 12(2), pages 1-15, January.

  5. Byeongchan Seong & Sung K. Ahn & Peter Zadrozny, 2007. "Cointegration Analysis with Mixed-Frequency Data," CESifo Working Paper Series 1939, CESifo.

    Cited by:

    1. Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
    2. J. Atsu Amegashie & Bazoumana Ouattara & Eric Strobl, 2007. "Moral Hazard and the Composition of Transfers: Theory with an Application to Foreign Aid," Working Papers 0702, University of Guelph, Department of Economics and Finance.
    3. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Working Papers 1403, Department of Economics, University of Missouri.
    4. J. Isaac Miller, 2010. "Cointegrating regressions with messy regressors and an application to mixed‐frequency series," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(4), pages 255-277, July.
    5. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.

  6. Peter A. Zadrozny, 2005. "Necessary and Sufficient Restrictions for Existence of a Unique Fourth Moment of a Univariate GARCH(p,q) Process," CESifo Working Paper Series 1505, CESifo.

    Cited by:

    1. Haas, Markus & Mittnik, Stefan, 2008. "Multivariate regimeswitching GARCH with an application to international stock markets," CFS Working Paper Series 2008/08, Center for Financial Studies (CFS).
    2. Todd, Prono, 2009. "Simple, Skewness-Based GMM Estimation of the Semi-Strong GARCH(1,1) Model," MPRA Paper 30994, University Library of Munich, Germany, revised 30 Jul 2011.
    3. Todd, Prono, 2010. "Simple GMM Estimation of the Semi-Strong GARCH(1,1) Model," MPRA Paper 20034, University Library of Munich, Germany.

  7. Baoline Chen & Peter A. Zadrozny, 2005. "Estimated U.S. Manufacturing Production Capital and Technology Based on an Estimated Dynamic Economic Model," CESifo Working Paper Series 1526, CESifo.

    Cited by:

    1. Konrad, Kai A. & Skaperdas, Stergios, 1999. "The Market for Protection and the Origin of the State," CEPR Discussion Papers 2173, C.E.P.R. Discussion Papers.
    2. Baoline Chen and Peter Zadrozny, 2001. "An Anticipative Feedback Solution for Infinite-Horizon Linear-Quadratic Dynamic Stackelberg Games," Computing in Economics and Finance 2001 110, Society for Computational Economics.
    3. Rodrigo Fuentes & Marco Morales, 2007. "Measuring TFP: A Latent Variable Approach," Working Papers Central Bank of Chile 419, Central Bank of Chile.

  8. Stefan Mittnik & Peter A. Zadrozny, 2004. "Forecasting Quarterly German GDP at Monthly Intervals Using Monthly IFO Business Conditions Data," CESifo Working Paper Series 1203, CESifo.

    Cited by:

    1. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    2. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should macroeconomic forecasters use daily financial data and how?," University of Cyprus Working Papers in Economics 09-2010, University of Cyprus Department of Economics.
    3. Cecilia Frale & Libero Monteforte, "undated". "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
    4. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    5. Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
    6. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    7. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2021. "Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-41, December.
    8. Klaus Abberger & Gebhard Flaig & Wolfgang Nierhaus, 2007. "ifo Konjunkturumfragen und Konjunkturanalyse : ausgewählte methodische Aufsätze aus dem ifo Schnelldienst," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 33, July.
    9. Konstantin A. Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2007. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder," Discussion Papers of DIW Berlin 664, DIW Berlin, German Institute for Economic Research.
    10. Klaus Abberger, 2007. "Forecasting Quarter-on-Quarter Changes of German GDP with Monthly Business Tendency Survey Results," ifo Working Paper Series 40, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    11. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.
    12. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "“Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming”," AQR Working Papers 201706, University of Barcelona, Regional Quantitative Analysis Group, revised May 2017.
    13. Seong, Byeongchan, 2020. "Smoothing and forecasting mixed-frequency time series with vector exponential smoothing models," Economic Modelling, Elsevier, vol. 91(C), pages 463-468.
    14. Byeongchan Seong & Sung K. Ahn & Peter Zadrozny, 2007. "Cointegration Analysis with Mixed-Frequency Data," CESifo Working Paper Series 1939, CESifo.
    15. Lenza, Michele & Cimadomo, Jacopo & Giannone, Domenico & Monti, Francesca & Sokol, Andrej, 2021. "Nowcasting with Large Bayesian Vector Autoregressions," CEPR Discussion Papers 15854, C.E.P.R. Discussion Papers.
    16. Klaus Abberger, 2005. "Qualitative Business Surveys and the Assessment of Employment A Case Study for Germany," ifo Working Paper Series 11, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    17. Qian, Hang, 2012. "A Flexible State Space Model and its Applications," MPRA Paper 38455, University Library of Munich, Germany.
    18. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Empirical modelling of survey-based expectations for the design of economic indicators in five European regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(2), pages 205-227, May.
    19. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72, July.
    20. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    21. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
    22. Klaus Wohlrabe, 2009. "Makroökonomische Prognosen mit gemischten Frequenzen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    23. Heinisch Katja & Scheufele Rolf, 2019. "Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, De Gruyter, vol. 20(4), pages 170-200, December.
    24. Ramadani Gani & Petrovska Magdalena & Bucevska Vesna, 2021. "Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia," South East European Journal of Economics and Business, Sciendo, vol. 16(2), pages 43-52, December.
    25. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    26. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.
    27. Blasques, F. & Koopman, S.J. & Mallee, M. & Zhang, Z., 2016. "Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 405-417.
    28. Cecilia Frale, Serena Teobaldo, Marco Cacciotti, Alessandra Caretta, 2013. "A Quarterly Measure Of Potential Output In The New European Fiscal Framework," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 181-197, April-Jun.
    29. Kai Carstensen & Steffen Henzel & Johannes Mayr & Klaus Wohlrabe, 2009. "IFOCAST: Methoden der ifo-Kurzfristprognose," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(23), pages 15-28, December.
    30. Sieds, 2013. "Complete Volume LXVII n.2 2013," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 1-197, April-Jun.
    31. Anna Sophia Ciesielski & Klaus Wohlrabe, 2011. "Sektorale Prognosen im Verarbeitenden Gewerbe," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 64(22), pages 27-35, November.
    32. Franco, Ray John Gabriel & Mapa, Dennis S., 2014. "The Dynamics of Inflation and GDP Growth: A Mixed Frequency Model Approach," MPRA Paper 55858, University Library of Munich, Germany.
    33. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," IREA Working Papers 201801, University of Barcelona, Research Institute of Applied Economics, revised Jan 2018.
    34. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 698-721.
    35. Qian, Hang, 2013. "Vector Autoregression with Mixed Frequency Data," MPRA Paper 47856, University Library of Munich, Germany.
    36. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    37. Ojogho, Osaihiomwan & Egware, Robert Awotu, 2015. "Price Generating Process And Volatility In Nigerian Agricultural Commodities Market," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 3(4), pages 1-10, October.
    38. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
    39. Heinisch, Katja, 2016. "A real-time analysis on the importance of hard and soft data for nowcasting German GDP," VfS Annual Conference 2016 (Augsburg): Demographic Change 145864, Verein für Socialpolitik / German Economic Association.
    40. Daniel Roash & Tanya Suhoy, 2019. "Sentiment Indicators Based on a Short Business Tendency Survey," Bank of Israel Working Papers 2019.11, Bank of Israel.
    41. Neville Francis & Eric Ghysels & Michael T. Owyang, 2011. "The low-frequency impact of daily monetary policy shocks," Working Papers 2011-009, Federal Reserve Bank of St. Louis.
    42. Klaus Abberger & Klaus Wohlrabe, 2006. "Einige Prognoseeigenschaften des ifo Geschäftsklimas - Ein Überblick über die neuere wissenschaftliche Literatur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 59(22), pages 19-26, November.
    43. Neville Francis, 2012. "The Low-Frequency Impact of Daily Monetary Policy Shock," 2012 Meeting Papers 198, Society for Economic Dynamics.
    44. Chen, Pu, 2009. "A Note on Updating Forecasts When New Information Arrives between Two Periods," Economics Discussion Papers 2009-22, Kiel Institute for the World Economy (IfW Kiel).
    45. Vermeulen, Philip, 2014. "An evaluation of business survey indices for short-term forecasting: Balance method versus Carlson–Parkin method," International Journal of Forecasting, Elsevier, vol. 30(4), pages 882-897.

  9. Baoline Chen and Peter Zadrozny, 2001. "An Anticipative Feedback Solution for Infinite-Horizon Linear-Quadratic Dynamic Stackelberg Games," Computing in Economics and Finance 2001 110, Society for Computational Economics.

    Cited by:

    1. Richard Dennis, 2001. "Optimal policy in rational-expectations models: new solution algorithms," Working Paper Series 2001-09, Federal Reserve Bank of San Francisco.
    2. Nie, Pu-yan & Chen, Li-hua & Fukushima, Masao, 2006. "Dynamic programming approach to discrete time dynamic feedback Stackelberg games with independent and dependent followers," European Journal of Operational Research, Elsevier, vol. 169(1), pages 310-328, February.
    3. David Yeung & Ovanes Petrosian, 2017. "Infinite Horizon Dynamic Games: A New Approach via Information Updating," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 19(04), pages 1-23, December.

  10. Peter A. Zadrozny, 1990. "Estimating A Multivariate Arma Model with Mixed-Frequency Data: An Application to Forecasting U.S. GNP at Monthly Intervals," Working Papers 90-5, Center for Economic Studies, U.S. Census Bureau.

    Cited by:

    1. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    2. Helena Rodríguez, 2014. "Un indicador de la evolución del PIB uruguayo en tiempo real," Documentos de trabajo 2014009, Banco Central del Uruguay.
    3. Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
    4. Tóth, Peter, 2014. "Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP]," MPRA Paper 63713, University Library of Munich, Germany.
    5. Lenza, Michele & Cimadomo, Jacopo & Giannone, Domenico & Monti, Francesca & Sokol, Andrej, 2021. "Nowcasting with Large Bayesian Vector Autoregressions," CEPR Discussion Papers 15854, C.E.P.R. Discussion Papers.
    6. Ghysels, Eric & Miller, J. Isaac, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," CEPR Discussion Papers 9654, C.E.P.R. Discussion Papers.
    7. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72, July.
    8. Martha Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Nowcasting," Working Papers ECARES ECARES 2010-021, ULB -- Universite Libre de Bruxelles.
    9. Thomas B Götz & Klemens Hauzenberger, 2021. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 442-461.
    10. Chen, Baoline & Zadrozny, Peter A., 2009. "Estimated U.S. manufacturing production capital and technology based on an estimated dynamic structural economic model," Journal of Economic Dynamics and Control, Elsevier, vol. 33(7), pages 1398-1418, July.
    11. Paul Viefers, 2011. "Bayesian Inference for the Mixed-Frequency VAR Model," Discussion Papers of DIW Berlin 1172, DIW Berlin, German Institute for Economic Research.

  11. Peter A. Zadrozny, 1988. "Analytic Derivatives for Estimation of Linear Dynamic Models," Working Papers 88-5, Center for Economic Studies, U.S. Census Bureau.

    Cited by:

    1. André Klein & Guy Melard & Toufik Zahaf, 1998. "Computation of the exact information matrix of Gaussian dynamic regression time series models," ULB Institutional Repository 2013/13738, ULB -- Universite Libre de Bruxelles.
    2. Anderson, Evan W. & McGrattan, Ellen R. & Hansen, Lars Peter & Sargent, Thomas J., 1996. "Mechanics of forming and estimating dynamic linear economies," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 4, pages 171-252, Elsevier.
    3. Judd, Kenneth L., 1996. "Approximation, perturbation, and projection methods in economic analysis," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 12, pages 509-585, Elsevier.
    4. Andrew P. Blake, 2004. "Analytic Derivatives for Linear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, vol. 24(1), pages 77-96, August.
    5. Peter A. Zadrozny, 1990. "Estimating A Multivariate Arma Model with Mixed-Frequency Data: An Application to Forecasting U.S. GNP at Monthly Intervals," Working Papers 90-5, Center for Economic Studies, U.S. Census Bureau.
    6. André Klein & Guy Melard, 2004. "An algorithm for computing the asymptotic Fisher information matrix for seasonal SISO models," ULB Institutional Repository 2013/13746, ULB -- Universite Libre de Bruxelles.
    7. Iskrev, Nikolay, 2008. "Evaluating the information matrix in linearized DSGE models," Economics Letters, Elsevier, vol. 99(3), pages 607-610, June.
    8. McGrattan, Ellen R., 1994. "The macroeconomic effects of distortionary taxation," Journal of Monetary Economics, Elsevier, vol. 33(3), pages 573-601, June.
    9. André Klein & Guy Melard & Abdessamad Saidi, 2008. "The asymptotic and exact Fisher information matrices," ULB Institutional Repository 2013/13766, ULB -- Universite Libre de Bruxelles.
    10. Nikolay Iskrev, 2013. "On the distribution of information in the moment structure of DSGE models," 2013 Meeting Papers 339, Society for Economic Dynamics.

  12. Robert H Mcguckin & Peter Zadrozny, 1988. "Long-Run Expectations And Capacity," Working Papers 88-1, Center for Economic Studies, U.S. Census Bureau.

    Cited by:

    1. Sang V Nguyen & Robert H Mcguckin & Arnold P Reznek, 1995. "The Impact Of Ownership Change On Employment, Wages, And Labor Productivity In U.S. Manufacturing 1977-87," Working Papers 95-8, Center for Economic Studies, U.S. Census Bureau.
    2. Joe Mattey, 1993. "Evidence on IO Technology Assumptions From the Longitudinal Research Database," Working Papers 93-8, Center for Economic Studies, U.S. Census Bureau.
    3. Maura P Doyle, 2000. "The 1989 Change in the Definition of Capacity: A Plant-Level Perspective," Working Papers 00-09, Center for Economic Studies, U.S. Census Bureau.
    4. Carol Corrado & Joe Mattey, 1997. "Capacity Utilization," Journal of Economic Perspectives, American Economic Association, vol. 11(1), pages 151-167, Winter.

Articles

  1. Peter A. Zadrozny & Baoline Chen, 2019. "Weighted‐Covariance Factor Decomposition of Varma Models Applied to Forecasting Quarterly U.S. Real GDP at Monthly Intervals," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 968-986, November.
    See citations under working paper version above.
  2. Zadrozny, Peter A., 2016. "Extended Yule–Walker identification of VARMA models with single- or mixed-frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 438-446.
    See citations under working paper version above.
  3. Byeongchan Seong & Sung K. Ahn & Peter A. Zadrozny, 2013. "Estimation of vector error correction models with mixed-frequency data," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(2), pages 194-205, March.

    Cited by:

    1. Peter Fuleky & Carl Bonham, 2010. "Forecasting Based on Common Trends in Mixed Frequency Samples," Working Papers 2010-17R1, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, revised Jul 2013.
    2. Matteo Barigozzi & Matteo Luciani, 2017. "Common Factors, Trends, and Cycles in Large Datasets," Finance and Economics Discussion Series 2017-111, Board of Governors of the Federal Reserve System (U.S.).
    3. Matteo Barigozzi & Matteo Luciani, 2019. "Quasi Maximum Likelihood Estimation of Non-Stationary Large Approximate Dynamic Factor Models," Papers 1910.09841, arXiv.org.
    4. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    5. Seong, Byeongchan, 2020. "Smoothing and forecasting mixed-frequency time series with vector exponential smoothing models," Economic Modelling, Elsevier, vol. 91(C), pages 463-468.
    6. Peter Fuleky & Carl S. Bonham, 2013. "Forecasting with Mixed Frequency Samples: The Case of Common Trends," Working Papers 201305, University of Hawaii at Manoa, Department of Economics.
    7. Chambers, Marcus J., 2020. "Frequency domain estimation of cointegrating vectors with mixed frequency and mixed sample data," Journal of Econometrics, Elsevier, vol. 217(1), pages 140-160.
    8. Nusair, Salah A. & Olson, Dennis, 2021. "Asymmetric oil price and Asian economies: A nonlinear ARDL approach," Energy, Elsevier, vol. 219(C).
    9. Chambers, Marcus J., 2016. "The estimation of continuous time models with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 390-404.
    10. Corona, Francisco & Poncela, Pilar, 2017. "Estimating non-stationary common factors : Implications for risk sharing," DES - Working Papers. Statistics and Econometrics. WS 24585, Universidad Carlos III de Madrid. Departamento de Estadística.
    11. Paola Arce & Jonathan Antognini & Werner Kristjanpoller & Luis Salinas, 2019. "Fast and Adaptive Cointegration Based Model for Forecasting High Frequency Financial Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 99-112, June.
    12. Cuixia Jiang & Tingting Zhao & Qifa Xu & Dan Hu, 2024. "An unrestricted MIDAS ordered logit model with applications to credit ratings," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 2722-2739, July.
    13. Ines Fortin & Jaroslava Hlouskova & Leopold Sögner, 2023. "Financial and economic uncertainties and their effects on the economy," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 50(2), pages 481-521, May.
    14. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2013. "Testing for Granger Causality with Mixed Frequency Data," CEPR Discussion Papers 9655, C.E.P.R. Discussion Papers.
    15. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2013. "Testing for common cycles in non-stationary VARs with varied frecquency data," Research Memorandum 002, Maastricht University, Graduate School of Business and Economics (GSBE).
    16. Zhou, Xinquan & Bagnarosa, Guillaume & Gohin, Alexandre & Pennings, Joost M.E. & Debie, Philippe, 2023. "Microstructure and high-frequency price discovery in the soybean complex," Journal of Commodity Markets, Elsevier, vol. 30(C).

  4. Baoline Chen & Peter Zadrozny, 2013. "Further model-based estimates of US total manufacturing production capital and technology, 1949–2005," Journal of Productivity Analysis, Springer, vol. 39(1), pages 61-73, February.

    Cited by:

    1. Peter A. Zadrozny, 2015. "Extended Yule-Walker Identification of Varma Models with Single- or Mixed- Frequency Data," Economic Working Papers 485, Bureau of Labor Statistics.

  5. Chen, Baoline & Zadrozny, Peter A., 2009. "Multi-step perturbation solution of nonlinear differentiable equations applied to an econometric analysis of productivity," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2061-2074, April.

    Cited by:

    1. Viktoria Blüschke-Nikolaeva & Dmitri Blüschke & Reinhard Neck, 2010. "Optimal Control of Nonlinear Dynamic Econometric Models: An Algorithm and an Application," Working Papers 032, COMISEF.
    2. D. Blueschke & V. Blueschke-Nikolaeva & Ivan Savin, 2012. "New Insights Into Optimal Control of Nonlinear Dynamic Econometric Models: Application of a Heuristic Approach," Jena Economics Research Papers 2012-008, Friedrich-Schiller-University Jena.
    3. Maliar, Lilia & Maliar, Serguei & Villemot, Sébastien, 2011. "Taking Perturbation to the Accuracy Frontier: A Hybrid of Local and Global Solutions," Dynare Working Papers 6, CEPREMAP, revised Jul 2012.

  6. Baoline Chen & Peter A. Zadrozny, 2003. "Higher-Moments in Perturbation Solution of the Linear-Quadratic Exponential Gaussian Optimal Control Problem," Computational Economics, Springer;Society for Computational Economics, vol. 21(1_2), pages 45-64, February.

    Cited by:

    1. Uribe, Martín & Schmitt-Grohé, Stephanie, 2001. "Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function," CEPR Discussion Papers 2963, C.E.P.R. Discussion Papers.
    2. Andrew Binning, 2013. "Third-order approximation of dynamic models without the use of tensors," Working Paper 2013/13, Norges Bank.
    3. Baoline Chen & Peter A. Zadrozny, 2005. "Multi-Step Perturbation Solution of Nonlinear Rational Expectations Models," Computing in Economics and Finance 2005 254, Society for Computational Economics.
    4. Lan, Hong & Meyer-Gohde, Alexander, 2011. "Solving DSGE models with a nonlinear moving average," SFB 649 Discussion Papers 2011-087, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    5. Chen, Baoline & Zadrozny, Peter A., 2009. "Multi-step perturbation solution of nonlinear differentiable equations applied to an econometric analysis of productivity," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2061-2074, April.
    6. Anderson, Evan W. & Hansen, Lars Peter & Sargent, Thomas J., 2012. "Small noise methods for risk-sensitive/robust economies," Journal of Economic Dynamics and Control, Elsevier, vol. 36(4), pages 468-500.

  7. Chen, Baoline & Zadrozny, Peter A., 2002. "An anticipative feedback solution for the infinite-horizon, linear-quadratic, dynamic, Stackelberg game," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1397-1416, August.
    See citations under working paper version above.
  8. Chen, Baoline & Zadrozny, Peter A., 2001. "Analytic derivatives of the matrix exponential for estimation of linear continuous-time models1," Journal of Economic Dynamics and Control, Elsevier, vol. 25(12), pages 1867-1879, December.

    Cited by:

    1. Antonio Diez de los Rios & Enrique Sentana, 2011. "Testing Uncovered Interest Parity: A Continuous‐Time Approach," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 52(4), pages 1215-1251, November.
    2. Magnus, Jan R. & Pijls, Henk G.J. & Sentana, Enrique, 2021. "The Jacobian of the exponential function," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).

  9. Zadrozny, Peter A., 1998. "An eigenvalue method of undetermined coefficients for solving linear rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1353-1373, August.

    Cited by:

    1. James M. Nason & George A. Slotsve, 2004. "Along the New Keynesian Phillips curve with nominal and real rigidities," FRB Atlanta Working Paper 2004-9, Federal Reserve Bank of Atlanta.
    2. Nason, James M. & Rogers, John H., 2006. "The present-value model of the current account has been rejected: Round up the usual suspects," Journal of International Economics, Elsevier, vol. 68(1), pages 159-187, January.
    3. James M. Nason & Gregor W. Smith, 2005. "Identifying the New Keynesian Phillips curve," FRB Atlanta Working Paper 2005-01, Federal Reserve Bank of Atlanta.
    4. Peter A. Zadrozny, 2015. "Extended Yule-Walker Identification of Varma Models with Single- or Mixed- Frequency Data," Economic Working Papers 485, Bureau of Labor Statistics.
    5. Meyer-Gohde, Alexander, 2010. "Linear rational-expectations models with lagged expectations: A synthetic method," Journal of Economic Dynamics and Control, Elsevier, vol. 34(5), pages 984-1002, May.
    6. Peter Zadrozny, 1997. "An Econometric Analysis of Polish Inflation Dynamics with Learning about Rational Expectations," Economic Change and Restructuring, Springer, vol. 30(2), pages 221-238, May.
    7. Richard Mash, 2003. "A Note on Simple MSV Solution Methods for Rational Expectations Models of Monetary Policy," Economics Series Working Papers 173, University of Oxford, Department of Economics.
    8. Gary Anderson, 2008. "Solving Linear Rational Expectations Models: A Horse Race," Computational Economics, Springer;Society for Computational Economics, vol. 31(2), pages 95-113, March.
    9. Zadrozny, Peter A., 2022. "Linear identification of linear rational-expectations models by exogenous variables reconciles Lucas and Sims," CFS Working Paper Series 682, Center for Financial Studies (CFS).
    10. Michael Thornton & Marcus Chambers, 2016. "Continuous Time ARMA Processes: Discrete Time Representation and Likelihood Evaluation," Discussion Papers 16/10, Department of Economics, University of York.
    11. Ric D. Herbert & Peter Stemp & Peter J. Stemp, 2000. "Exploiting Model Structure to Solve the Dynamics of a Macro Model," CESifo Working Paper Series 266, CESifo.
    12. Hernandez, Kolver, 2013. "A system reduction method to efficiently solve DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 37(3), pages 571-576.
    13. Onatski, Alexei, 2006. "Winding number criterion for existence and uniqueness of equilibrium in linear rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 30(2), pages 323-345, February.
    14. Ronald J. Balvers & Douglas W. Mitchell, 2001. "Reducing the Dimensionality of Linear Quadratic Control Problems," Tinbergen Institute Discussion Papers 01-043/2, Tinbergen Institute.
    15. Tan, Fei & Walker, Todd B., 2015. "Solving generalized multivariate linear rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 60(C), pages 95-111.
    16. Binder, Michael & Pesaran, Hashem, 2000. "Solution of finite-horizon multivariate linear rational expectations models and sparse linear systems," Journal of Economic Dynamics and Control, Elsevier, vol. 24(3), pages 325-346, March.
    17. Baoline Chen & Peter A. Zadrozny, 2005. "Multi-Step Perturbation Solution of Nonlinear Rational Expectations Models," Computing in Economics and Finance 2005 254, Society for Computational Economics.

  10. Zadrozny, Peter A, 1997. "An Econometric Analysis of Polish Inflation Dynamics with Learning about Rational Expectations," Economic Change and Restructuring, Springer, vol. 30(2-3), pages 221-238.

    Cited by:

    1. Peter A. Zadrozny, 2015. "Extended Yule-Walker Identification of Varma Models with Single- or Mixed- Frequency Data," Economic Working Papers 485, Bureau of Labor Statistics.
    2. Zadrozny, Peter A., 2022. "Linear identification of linear rational-expectations models by exogenous variables reconciles Lucas and Sims," CFS Working Paper Series 682, Center for Financial Studies (CFS).
    3. Chen, Baoline & Zadrozny, Peter A., 2009. "Estimated U.S. manufacturing production capital and technology based on an estimated dynamic structural economic model," Journal of Economic Dynamics and Control, Elsevier, vol. 33(7), pages 1398-1418, July.
    4. Tihomir Enev & Kenneth Koford, 2000. "The Effect of Incomes Policies on Inflation in Bulgaria and Poland," Economic Change and Restructuring, Springer, vol. 33(3), pages 141-169, October.

  11. Mittnik, Stefan & Zadrozny, Peter A, 1993. "Asymptotic Distributions of Impulse Responses, Step Responses, and Variance Decompositions of Estimated Linear Dynamic Models," Econometrica, Econometric Society, vol. 61(4), pages 857-870, July.

    Cited by:

    1. Stefan Mittnik & Nikolay Robinzonov & Klaus Wohlrabe, 2013. "The Micro Dynamics of Macro Announcements," CESifo Working Paper Series 4421, CESifo.
    2. Mittnik, Stefan & Semmler, Willi, 2013. "The real consequences of financial stress," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1479-1499.
    3. André Klein & Guy Melard & Toufik Zahaf, 1998. "Computation of the exact information matrix of Gaussian dynamic regression time series models," ULB Institutional Repository 2013/13738, ULB -- Universite Libre de Bruxelles.
    4. Mittnik, Stefan & Semmler, Willi, 2018. "Overleveraging, Financial Fragility, And The Banking–Macro Link: Theory And Empirical Evidence," Macroeconomic Dynamics, Cambridge University Press, vol. 22(1), pages 4-32, January.
    5. Chiarella Carl & Semmler Willi & Mittnik Stefan & Zhu Peiyuan, 2002. "Stock Market, Interest Rate and Output: A Model and Estimation for US Time Series Data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(1), pages 1-39, April.
    6. Uhlig, Harald, 1999. "What are the Effects of Monetary Policy on Output? Results from an Agnostic Identification Procedure," CEPR Discussion Papers 2137, C.E.P.R. Discussion Papers.
    7. Chevillon, Guillaume & Mavroeidis, Sophocles & Zhan, Zhaoguo, 2016. "Robust inference in structural VARs with long-run restrictions," ESSEC Working Papers WP1702, ESSEC Research Center, ESSEC Business School.
    8. Christopher A. Sims & Tao Zha, 1995. "Error bands for impulse responses," FRB Atlanta Working Paper 95-6, Federal Reserve Bank of Atlanta.
    9. Rubio-Ramírez, Juan Francisco & Schorfheide, Frank & Fernández-Villaverde, Jesús, 2015. "Solution and Estimation Methods for DSGE Models," CEPR Discussion Papers 11032, C.E.P.R. Discussion Papers.
    10. Inoue, Atsushi & Kilian, Lutz, 2013. "Inference on impulse response functions in structural VAR models," Journal of Econometrics, Elsevier, vol. 177(1), pages 1-13.
    11. Huh, Hyeon-seung & Kim, David, 2013. "An empirical test of exogenous versus endogenous growth models for the G-7 countries," Economic Modelling, Elsevier, vol. 32(C), pages 262-272.
    12. Ekkehard Ernst & Stefan Mittnik & Willi Semmler, 2016. "Interaction of Labour and Credit Market in Growth Regimes: A Theoretical and Empirical Analysis," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 45(3), pages 393-422, November.
    13. Kirstin Hubrich & Peter J. G. Vlaar, 2000. "Germany and the Euro Area: Differences in the Transmission Process of Monetary Policy," Econometric Society World Congress 2000 Contributed Papers 1802, Econometric Society, revised 08 Nov 2000.
    14. Stefan Mittnik & Nikolay Robinzonov & Klaus Wohlrabe, 2013. "Was bewegt den DAX?," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(23), pages 32-36, December.
    15. Hyeon-Seung Huh, 2013. "A Monte Carlo test for the identifying assumptions of the Blanchard and Quah (1989) model," Applied Economics Letters, Taylor & Francis Journals, vol. 20(6), pages 601-605, April.
    16. A. Mazaheri, 1999. "Convenience yield, mean reverting prices, and long memory in the petroleum market," Applied Financial Economics, Taylor & Francis Journals, vol. 9(1), pages 31-50.
    17. André Klein & Guy Melard, 2004. "An algorithm for computing the asymptotic Fisher information matrix for seasonal SISO models," ULB Institutional Repository 2013/13746, ULB -- Universite Libre de Bruxelles.
    18. Gabriele Fiorentini & Enrique Sentana, 2020. "Discrete Mixtures of Normals Pseudo Maximum Likelihood Estimators of Structural Vector Autoregressions," Working Papers wp2020_2023, CEMFI.
    19. Renata Wróbel-Rotter, 2016. "Impulse Response Functions in the Dynamic Stochastic General Equilibrium Vector Autoregression Model," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(2), pages 93-114, June.
    20. Mittnik, Stefan & Semmler, Willi, 2012. "Regime dependence of the fiscal multiplier," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 502-522.
    21. Mounir Ben Mbarek & Samia Nasreen & Rochdi Feki, 2017. "The contribution of nuclear energy to economic growth in France: short and long run," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(1), pages 219-238, January.
    22. Uhlig, H.F.H.V.S., 1999. "What are the Effects of Monetary Policy on Output? Results from an Agnostic Identification Procedure," Other publications TiSEM 2e0fa8dd-ead5-4c6b-97cb-1, Tilburg University, School of Economics and Management.
    23. Kirstin Hubrich & Peter Vlaar, 2004. "Monetary transmission in Germany: Lessons for the Euro area," Empirical Economics, Springer, vol. 29(2), pages 383-414, May.
    24. Chung, Ching-Fan, 2001. "Calculating and analyzing impulse responses for the vector ARFIMA model," Economics Letters, Elsevier, vol. 71(1), pages 17-25, April.
    25. Willi Semmler & Stefan Mittnik, 2012. "Estimating a Banking-Macro Model for Europe Using a Multi-Regime VAR," EcoMod2012 4122, EcoMod.
    26. Stefan Mittnik & Willi Semmler, 2011. "The Instability of the Banking Sector and Macrodynamics: Theory and Empirics," DEGIT Conference Papers c016_080, DEGIT, Dynamics, Economic Growth, and International Trade.
    27. Zhou, Mo & Buongiorno, Joseph, 2005. "Price transmission between products at different stages of manufacturing in forest industries," Journal of Forest Economics, Elsevier, vol. 11(1), pages 5-19, June.

  12. Peter A. Zadrozny, 1990. "Forecasting U.S. GNP at monthly intervals with an estimated bivariate time series model," Economic Review, Federal Reserve Bank of Atlanta, issue Nov, pages 2-15.

    Cited by:

    1. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should macroeconomic forecasters use daily financial data and how?," University of Cyprus Working Papers in Economics 09-2010, University of Cyprus Department of Economics.
    2. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2021. "Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-41, December.
    3. Peter A. Zadrozny, 2015. "Extended Yule-Walker Identification of Varma Models with Single- or Mixed- Frequency Data," Economic Working Papers 485, Bureau of Labor Statistics.
    4. Seong, Byeongchan, 2020. "Smoothing and forecasting mixed-frequency time series with vector exponential smoothing models," Economic Modelling, Elsevier, vol. 91(C), pages 463-468.
    5. Jonas E. Arias & Minchul Shin, 2020. "Tracking U.S. Real GDP Growth During the Pandemic," Economic Insights, Federal Reserve Bank of Philadelphia, vol. 5(3), pages 9-14, September.
    6. Peter Zadrozny, 1997. "An Econometric Analysis of Polish Inflation Dynamics with Learning about Rational Expectations," Economic Change and Restructuring, Springer, vol. 30(2), pages 221-238, May.
    7. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2020. "Structural analysis with mixed-frequency data: A model of US capital flows," Economic Modelling, Elsevier, vol. 89(C), pages 427-443.
    8. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    9. Christensen, Bent Jesper & Neri, Luca & Parra-Alvarez, Juan Carlos, 2024. "Estimation of continuous-time linear DSGE models from discrete-time measurements," Journal of Econometrics, Elsevier, vol. 244(2).
    10. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    11. Neville Francis & Eric Ghysels & Michael T. Owyang, 2011. "The low-frequency impact of daily monetary policy shocks," Working Papers 2011-009, Federal Reserve Bank of St. Louis.
    12. Neville Francis, 2012. "The Low-Frequency Impact of Daily Monetary Policy Shock," 2012 Meeting Papers 198, Society for Economic Dynamics.

  13. Zadrozny, Peter, 1988. "Gaussian Likelihood of Continuous-Time ARMAX Models When Data Are Stocks and Flows at Different Frequencies," Econometric Theory, Cambridge University Press, vol. 4(1), pages 108-124, April.

    Cited by:

    1. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    2. Philipp Gersing & Leopold Soegner & Manfred Deistler, 2022. "Retrieval from Mixed Sampling Frequency: Generic Identifiability in the Unit Root VAR," Papers 2204.05952, arXiv.org, revised Jul 2023.
    3. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    4. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    5. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    6. Milena Hoyos, 2020. "Mixed First‐ and Second‐Order Cointegrated Continuous Time Models with Mixed Stock and Flow Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 249-267, March.
    7. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    8. Peter A. Zadrozny, 2015. "Extended Yule-Walker Identification of Varma Models with Single- or Mixed- Frequency Data," Economic Working Papers 485, Bureau of Labor Statistics.
    9. Claudia FORONI & Massimiliano MARCELLINO, 2012. "A Comparison of Mixed Frequency Approaches for Modelling Euro Area Macroeconomic Variables," Economics Working Papers ECO2012/07, European University Institute.
    10. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Working Papers 1403, Department of Economics, University of Missouri.
    11. Byeongchan Seong & Sung K. Ahn & Peter Zadrozny, 2007. "Cointegration Analysis with Mixed-Frequency Data," CESifo Working Paper Series 1939, CESifo.
    12. Ghysels, E. & Jasiak, J., 1994. "Stochastic Volatility and time Deformation: An Application of trading Volume and Leverage Effects," Cahiers de recherche 9403, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    13. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    14. Qian, Hang, 2012. "A Flexible State Space Model and its Applications," MPRA Paper 38455, University Library of Munich, Germany.
    15. Ghysels, Eric & Miller, J. Isaac, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," CEPR Discussion Papers 9654, C.E.P.R. Discussion Papers.
    16. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72, July.
    17. Peter Zadrozny, 1997. "An Econometric Analysis of Polish Inflation Dynamics with Learning about Rational Expectations," Economic Change and Restructuring, Springer, vol. 30(2), pages 221-238, May.
    18. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    19. Klaus Wohlrabe, 2009. "Makroökonomische Prognosen mit gemischten Frequenzen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    20. Jesica Escobar & Alexander Poznyak, 2022. "Robust Parametric Identification for ARMAX Models with Non-Gaussian and Coloured Noise: A Survey," Mathematics, MDPI, vol. 10(8), pages 1-38, April.
    21. Chambers, Marcus J., 2016. "The estimation of continuous time models with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 390-404.
    22. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2020. "Structural analysis with mixed-frequency data: A model of US capital flows," Economic Modelling, Elsevier, vol. 89(C), pages 427-443.
    23. Michael Thornton & Marcus Chambers, 2016. "Continuous Time ARMA Processes: Discrete Time Representation and Likelihood Evaluation," Discussion Papers 16/10, Department of Economics, University of York.
    24. Qian, Hang, 2010. "Vector autoregression with varied frequency data," MPRA Paper 34682, University Library of Munich, Germany.
    25. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    26. Qian, Hang, 2016. "A computationally efficient method for vector autoregression with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 433-437.
    27. Baoline Chen & Peter Zadrozny, 2013. "Further model-based estimates of US total manufacturing production capital and technology, 1949–2005," Journal of Productivity Analysis, Springer, vol. 39(1), pages 61-73, February.
    28. Thornton, Michael A. & Chambers, Marcus J., 2016. "The exact discretisation of CARMA models with applications in finance," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 739-761.
    29. Michael A. Thornton & Marcus J. Chambers, 2013. "Continuous-time autoregressive moving average processes in discrete time: representation and embeddability," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(5), pages 552-561, September.
    30. Vicky Fasen-Hartmann & Celeste Mayer, 2022. "Whittle estimation for continuous-time stationary state space models with finite second moments," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(2), pages 233-270, April.
    31. Vicky Fasen‐Hartmann & Sebastian Kimmig, 2020. "Robust estimation of stationary continuous‐time arma models via indirect inference," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(5), pages 620-651, September.
    32. Kai Carstensen & Steffen Henzel & Johannes Mayr & Klaus Wohlrabe, 2009. "IFOCAST: Methoden der ifo-Kurzfristprognose," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(23), pages 15-28, December.
    33. Peter A. Zadrozny, 2016. "Real-Time State Space Method for Computing Smoothed Estimates of Future Revisions of U.S. Monthly Chained CPI," CESifo Working Paper Series 5897, CESifo.
    34. Peter A. Zadrozny, 1990. "Estimating A Multivariate Arma Model with Mixed-Frequency Data: An Application to Forecasting U.S. GNP at Monthly Intervals," Working Papers 90-5, Center for Economic Studies, U.S. Census Bureau.
    35. Chen, Baoline & Zadrozny, Peter A., 2009. "Estimated U.S. manufacturing production capital and technology based on an estimated dynamic structural economic model," Journal of Economic Dynamics and Control, Elsevier, vol. 33(7), pages 1398-1418, July.
    36. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2013. "Testing for Granger Causality with Mixed Frequency Data," CEPR Discussion Papers 9655, C.E.P.R. Discussion Papers.
    37. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.
    38. Chambers, Marcus J., 1999. "Discrete time representation of stationary and non-stationary continuous time systems," Journal of Economic Dynamics and Control, Elsevier, vol. 23(4), pages 619-639, February.
    39. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 698-721.
    40. Qian, Hang, 2013. "Vector Autoregression with Mixed Frequency Data," MPRA Paper 47856, University Library of Munich, Germany.
    41. Chambers, MJ & McCrorie, JR & Thornton, MA, 2017. "Continuous Time Modelling Based on an Exact Discrete Time Representation," Economics Discussion Papers 20497, University of Essex, Department of Economics.
    42. Hermann Singer, 2003. "Simulated Maximum Likelihood in Nonlinear Continuous-Discrete State Space Models: Importance Sampling by Approximate Smoothing," Computational Statistics, Springer, vol. 18(1), pages 79-106, March.
    43. Robert Lehmann & Magnus Reif & Timo Wollmershäuser, 2020. "ifoCAST: Der neue Prognosestandard des ifo Instituts," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 73(11), pages 31-39, November.
    44. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed frequency structural VARs," Working Paper 2014/01, Norges Bank.
    45. Roderick McCrorie, J., 2001. "Interpolating exogenous variables in continuous time dynamic models," Journal of Economic Dynamics and Control, Elsevier, vol. 25(9), pages 1399-1427, September.
    46. Chen, Baoline & Zadrozny, Peter A., 2001. "Analytic derivatives of the matrix exponential for estimation of linear continuous-time models1," Journal of Economic Dynamics and Control, Elsevier, vol. 25(12), pages 1867-1879, December.
    47. J. Roderick McCrorie, 2000. "The Likelihood of a Continuous-time Vector Autoregressive Model," Working Papers 419, Queen Mary University of London, School of Economics and Finance.
    48. Michael A. Thornton & Marcus J. Chambers, 2013. "Temporal aggregation in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 13, pages 289-310, Edward Elgar Publishing.
    49. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2013. "Testing for common cycles in non-stationary VARs with varied frecquency data," Research Memorandum 002, Maastricht University, Graduate School of Business and Economics (GSBE).
    50. Paul Viefers, 2011. "Bayesian Inference for the Mixed-Frequency VAR Model," Discussion Papers of DIW Berlin 1172, DIW Berlin, German Institute for Economic Research.
    51. Uwe Hassler, 2013. "Effect of temporal aggregation on multiple time series in the frequency domain," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(5), pages 562-573, September.

  14. Zadrozny, Peter, 1988. "Analytic Derivatives for Estimation of Discrete-Time,," Econometrica, Econometric Society, vol. 56(2), pages 467-472, March.

    Cited by:

    1. Anderson, Evan W. & McGrattan, Ellen R. & Hansen, Lars Peter & Sargent, Thomas J., 1996. "Mechanics of forming and estimating dynamic linear economies," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 4, pages 171-252, Elsevier.
    2. Judd, Kenneth L., 1996. "Approximation, perturbation, and projection methods in economic analysis," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 12, pages 509-585, Elsevier.
    3. Alejandro Vicondoa & Andrea Gazzani, 2020. "Bridge Proxy-SVAR: Estimating the Macroeconomic Effects of Shocks Identified at High-Frequency," Documentos de Trabajo 533, Instituto de Economia. Pontificia Universidad Católica de Chile..
    4. Andrew P. Blake, 2004. "Analytic Derivatives for Linear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, vol. 24(1), pages 77-96, August.
    5. Andrea Giovanni Gazzani & Alejandro Vicondoa, 2019. "Proxy-SVAR as a Bridge for Identification with Higher Frequency Data," 2019 Meeting Papers 855, Society for Economic Dynamics.
    6. McGrattan, Ellen R., 1994. "The macroeconomic effects of distortionary taxation," Journal of Monetary Economics, Elsevier, vol. 33(3), pages 573-601, June.

  15. Zadrozny, Peter, 1988. "A consistent, closed-loop solution for infinite-horizon, linear-quadratic, dynamic Stackelberg games," Journal of Economic Dynamics and Control, Elsevier, vol. 12(1), pages 155-159, March.

    Cited by:

    1. Baoline Chen and Peter Zadrozny, 2001. "An Anticipative Feedback Solution for Infinite-Horizon Linear-Quadratic Dynamic Stackelberg Games," Computing in Economics and Finance 2001 110, Society for Computational Economics.
    2. Dan Protopopescu, 2009. "Dynamic Stackelberg Game with Risk-Averse Players: Optimal Risk-Sharing under Asymmetric Information," UFAE and IAE Working Papers 797.09, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).

Chapters

  1. Stefan Mittnik & Peter Zadrozny, 2005. "Forecasting Quarterly German GDP at Monthly Intervals Using Monthly Ifo Business Conditions Data," Contributions to Economics, in: Jan-Egbert Sturm & Timo Wollmershäuser (ed.), Ifo Survey Data in Business Cycle and Monetary Policy Analysis, pages 19-48, Springer.
    See citations under working paper version above.Sorry, no citations of chapters recorded.
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