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Peter A. Zadrozny

Personal Details

First Name:Peter
Middle Name:A.
Last Name:Zadrozny
Suffix:
RePEc Short-ID:pza34
[This author has chosen not to make the email address public]
Bureau of Labor Statistics 2 Massachusetts Ave., NE, Room 3105 Washington, DC 20212, USA
Terminal Degree:1980 Department of Economics; University of Chicago (from RePEc Genealogy)

Affiliation

Bureau of Labor Statistics
Department of Labor
Government of the United States

Washington, District of Columbia (United States)
http://www.bls.gov/

: (202) 606-5900
(202) 606-7890
2 Massachusetts Avenue, N.E. Room 2860, Washington, D. C. 20212
RePEc:edi:blsgvus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. 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 Group Munich.
  2. Zadrozny, Peter A., 2015. "Extended Yule-Walker identification of Varma models with single- or mixed frequency data," CFS Working Paper Series 526, Center for Financial Studies (CFS).
  3. Baoline Chen & Peter A. Zadrozny, 2009. "Further Model-Based Estimates of U.S. Total Manufacturing Production Capital and Technology, 1949-2005," Working Papers 430, U.S. Bureau of Labor Statistics.
  4. Baoline Chen & Peter A. Zadrozny, 2009. "Estimated U.S. Manufacturing Production Capital and Technology Based on an Estimated Dynamic Structural Economic Model," Working Papers 429, U.S. Bureau of Labor Statistics.
  5. Byeongchan Seong & Sung K. Ahn & Peter Zadrozny, 2007. "Cointegration Analysis with Mixed-Frequency Data," CESifo Working Paper Series 1939, CESifo Group Munich.
  6. Peter A. Zadrozny & Baoline Chen, 2005. "Testing Substitution Bias of the Solow-Residual Measure of Total Factor Productivity Using CES-Class Production Functions," Computing in Economics and Finance 2005 378, Society for Computational Economics.
  7. 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 Group Munich.
  8. 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 Group Munich.
  9. 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 Group Munich.
  10. 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.
  11. 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.
  12. Peter A. Zadrozny, 1988. "Analytic Derivatives for Estimation of Linear Dynamic Models," Working Papers 88-5, Center for Economic Studies, U.S. Census Bureau.
  13. Robert H Mcguckin & Peter Zadrozny, 1988. "Long-Run Expectations And Capacity," Working Papers 88-1, Center for Economic Studies, U.S. Census Bureau.

Articles

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  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. 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.
  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.
  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.
  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.
  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.
  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.
  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.
  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(01), pages 108-124, April.
  14. Zadrozny, Peter, 1988. "Analytic Derivatives for Estimation of Discrete-Time,," Econometrica, Econometric Society, vol. 56(2), pages 467-472, March.
  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.

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, 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 Group Munich.

    Cited by:

    1. John S. Greenlees & Elliot Williams, 2009. "Reconsideration of Weighting and Updating Procedures in the US CPI," Working Papers 431, U.S. Bureau of Labor Statistics.
    2. Jan P. A. M. Jacobs & Samad Sarferaz & Simon van Norden & Jan-Egbert Sturm, 2013. "Modeling Multivariate Data Revisions," CIRANO Working Papers 2013s-44, CIRANO.

  2. Zadrozny, Peter A., 2015. "Extended Yule-Walker identification of Varma models with single- or mixed frequency data," CFS Working Paper Series 526, Center for Financial Studies (CFS).

    Cited by:

    1. Thornton, Michael A. & Chambers, Marcus J., 2017. "Continuous time ARMA processes: Discrete time representation and likelihood evaluation," Journal of Economic Dynamics and Control, Elsevier, vol. 79(C), pages 48-65.
    2. Morris, Stephen D., 2017. "DSGE pileups," Journal of Economic Dynamics and Control, Elsevier, vol. 74(C), pages 56-86.
    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. Hecq, Alain & Goetz, Thomas, 2018. "Granger causality testing in mixed-frequency Vars with possibly (co)integrated processes," MPRA Paper 87746, University Library of Munich, Germany.

  3. Baoline Chen & Peter A. Zadrozny, 2009. "Further Model-Based Estimates of U.S. Total Manufacturing Production Capital and Technology, 1949-2005," Working Papers 430, U.S. Bureau of Labor Statistics.

    Cited by:

    1. Peter A. Zadrozny, 2016. "Extended Yule-Walker Identification of Varma Models with Single- or Mixed-Frequency Data," CESifo Working Paper Series 5884, CESifo Group Munich.

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

    Cited by:

    1. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, Elsevier.
    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. J. Isaac Miller, 2012. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Working Papers 1211, Department of Economics, University of Missouri.
    4. 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.
    5. 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. 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 Group Munich.

    Cited by:

    1. Todd, Prono, 2010. "Simple GMM Estimation of the Semi-Strong GARCH(1,1) Model," MPRA Paper 20034, University Library of Munich, Germany.
    2. 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).
    3. 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.

  6. 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 Group Munich.

    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. Rodrigo Fuentes, Marco Morales, 2007. "Measuring TFP: A Latent Variable Approach," Working Papers Central Bank of Chile 419, Central Bank of Chile.
    3. 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.

  7. 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 Group Munich.

    Cited by:

    1. 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 - Italian Review of Economics, Demography and Statistics, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 181-197, April-Jun.
    2. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    3. 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.
    4. 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.
    5. 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.
    6. Sieds, 2013. "Complete Volume LXVII n.2 2013," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - Italian Review of Economics, Demography and Statistics, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 1-197, April-Jun.
    7. 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.
    8. 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.
    9. 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.
    10. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, Elsevier.
    11. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Byeongchan Seong & Sung K. Ahn & Peter Zadrozny, 2007. "Cointegration Analysis with Mixed-Frequency Data," CESifo Working Paper Series 1939, CESifo Group Munich.
    19. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 13(3), pages 698-721.
    20. Qian, Hang, 2013. "Vector Autoregression with Mixed Frequency Data," MPRA Paper 47856, University Library of Munich, Germany.
    21. 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. 0(Number 4), pages 1-10, October.
    22. Qian, Hang, 2012. "A Flexible State Space Model and its Applications," MPRA Paper 38455, University Library of Munich, Germany.
    23. 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.
    24. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    25. Heinisch, Katja, 2016. "A real-time analysis on the importance of hard and soft data for nowcasting German GDP," Annual Conference 2016 (Augsburg): Demographic Change 145864, Verein für Socialpolitik / German Economic Association.
    26. 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.
    27. 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.
    28. 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.
    29. Neville Francis, 2012. "The Low-Frequency Impact of Daily Monetary Policy Shock," 2012 Meeting Papers 198, Society for Economic Dynamics.
    30. Heinisch, Katja & Scheufele, Rolf, 2017. "Should forecasters use real-time data to evaluate leading indicator models for GDP prediction? German evidence," IWH Discussion Papers 5/2017, Halle Institute for Economic Research (IWH).
    31. 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).
    32. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    33. 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.
    34. 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.

  8. 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. 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.
    3. 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.
    4. Iskrev, Nikolay, 2008. "Evaluating the information matrix in linearized DSGE models," Economics Letters, Elsevier, vol. 99(3), pages 607-610, June.
    5. André Klein & Guy Melard & Abdessamad Saidi, 2008. "The asymptotic and exact Fisher information matrices," ULB Institutional Repository 2013/13766, ULB -- Universite Libre de Bruxelles.
    6. Nikolay Iskrev, 2013. "On the distribution of information in the moment structure of DSGE models," 2013 Meeting Papers 339, Society for Economic Dynamics.

  9. 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. 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.
    2. 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.
    3. Carol Corrado & Joe Mattey, 1997. "Capacity Utilization," Journal of Economic Perspectives, American Economic Association, vol. 11(1), pages 151-167, Winter.
    4. 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.

Articles

  1. 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.
  2. 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 S. Bonham, 2011. "Forecasting Based on Common Trends in Mixed Frequency Samples," Working Papers 201110, University of Hawaii at Manoa, Department of Economics.
    2. 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.
    3. 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.).
    4. 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.
    5. Chambers, Marcus J., 2016. "The estimation of continuous time models with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 390-404.
    6. Ruiz Ortega, Esther & Poncela, Pilar & Corona, Francisco, 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.
    7. Hecq A.W. & Urbain J.R.Y.J. & Götz T.B., 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).

  3. 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.
    See citations under working paper version above.
  4. 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. Blueschke-Nikolaeva, V. & Blueschke, D. & Neck, R., 2012. "Optimal control of nonlinear dynamic econometric models: An algorithm and an application," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3230-3240.
    2. Blueschke, D. & Blueschke-Nikolaeva, V. & Savin, I., 2013. "New insights into optimal control of nonlinear dynamic econometric models: Application of a heuristic approach," Journal of Economic Dynamics and Control, Elsevier, vol. 37(4), pages 821-837.
    3. Lilia Maliar & Serguei Maliar & Sébastien Villemot, 2013. "Taking Perturbation to the Accuracy Frontier: A Hybrid of Local and Global Solutions," Computational Economics, Springer;Society for Computational Economics, vol. 42(3), pages 307-325, October.

  5. 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. Stephanie Schmitt-Grohe & Martin Uribe, 2001. "Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function," Departmental Working Papers 200106, Rutgers University, Department of Economics.
    2. 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.
    3. Hong Lan & Alexander Meyer-Gohde, 2011. "Solving DSGE Models with a Nonlinear Moving Average," SFB 649 Discussion Papers SFB649DP2011-087, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. 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.
    5. Andrew Binning, 2013. "Third-order approximation of dynamic models without the use of tensors," Working Paper 2013/13, Norges Bank.
    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.

  6. 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.

    Cited by:

    1. 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.
    2. 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.
    3. Richard Dennis, 2001. "Optimal policy in rational-expectations models: new solution algorithms," Working Paper Series 2001-09, Federal Reserve Bank of San Francisco.

  7. 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.

  8. 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. 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.
    2. Nason, James M. & Smith, Gregor W., 2005. "Identifying the New Keynesian Phillips Curve," Queen's Economics Department Working Papers 273464, Queen's University - Department of Economics.
    3. 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.
    4. Peter A. Zadrozny, 2016. "Extended Yule-Walker Identification of Varma Models with Single- or Mixed-Frequency Data," CESifo Working Paper Series 5884, CESifo Group Munich.
    5. Alexander Meyer-Gohde, 2007. "Solving Linear Rational Expectations Models with Lagged Expectations Quickly and Easily," SFB 649 Discussion Papers SFB649DP2007-069, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. 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.
    7. 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.
    8. Ronald J. Balvers & Douglas W. Mitchell, 2001. "Reducing the Dimensionality of Linear Quadratic Control Problems," Tinbergen Institute Discussion Papers 01-043/2, Tinbergen Institute.
    9. 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.
    10. 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.
    11. 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.
    12. Thornton, Michael A. & Chambers, Marcus J., 2017. "Continuous time ARMA processes: Discrete time representation and likelihood evaluation," Journal of Economic Dynamics and Control, Elsevier, vol. 79(C), pages 48-65.
    13. 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.
    14. 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.
    15. 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.
    16. Ric D. Herbert & Peter Stemp, 2000. "Exploiting Model Structure to Solve the Dynamics of a Macro Model," CESifo Working Paper Series 266, CESifo Group Munich.

  9. 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. Baoline Chen & Peter A. Zadrozny, 2009. "Estimated U.S. Manufacturing Production Capital and Technology Based on an Estimated Dynamic Structural Economic Model," Working Papers 429, U.S. Bureau of Labor Statistics.
    2. Peter A. Zadrozny, 2016. "Extended Yule-Walker Identification of Varma Models with Single- or Mixed-Frequency Data," CESifo Working Paper Series 5884, CESifo Group Munich.
    3. 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.

  10. 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 Group Munich.
    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. 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.
    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. Atsushi Inoue & Lutz Kilian, 2013. "Inference on Impulse Response Functions in Structural VAR Models," DSSR Discussion Papers 11, Graduate School of Economics and Management, Tohoku University.
    7. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, Elsevier.
    8. Renata Wróbel-Rotter, 2016. "Impulse Response Functions in the Dynamic Stochastic General Equilibrium Vector Autoregression Model," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 8(2), pages 93-114, June.
    9. 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.
    10. 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.
    11. Christopher A. Sims & Tao Zha, 1995. "Error bands for impulse responses," FRB Atlanta Working Paper 95-6, Federal Reserve Bank of Atlanta.
    12. Kirstin Hubrich & Peter Vlaar, 2004. "Monetary transmission in Germany: Lessons for the Euro area," Empirical Economics, Springer, vol. 29(2), pages 383-414, May.
    13. 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.
    14. 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.
    15. Willi Semmler & Stefan Mittnik, 2012. "Estimating a Banking-Macro Model for Europe Using a Multi-Regime VAR," EcoMod2012 4122, EcoMod.
    16. 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.
    17. 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.
    18. 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.

  11. 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. Peter A. Zadrozny, 2016. "Extended Yule-Walker Identification of Varma Models with Single- or Mixed-Frequency Data," CESifo Working Paper Series 5884, CESifo Group Munich.
    3. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    4. 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.
    5. 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.
    6. Neville Francis, 2012. "The Low-Frequency Impact of Daily Monetary Policy Shock," 2012 Meeting Papers 198, Society for Economic Dynamics.
    7. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.

  12. 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(01), 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. Baoline Chen & Peter A. Zadrozny, 2009. "Estimated U.S. Manufacturing Production Capital and Technology Based on an Estimated Dynamic Structural Economic Model," Working Papers 429, U.S. Bureau of Labor Statistics.
    3. 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.
    4. 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 Group Munich.
    5. Peter A. Zadrozny, 2016. "Extended Yule-Walker Identification of Varma Models with Single- or Mixed-Frequency Data," CESifo Working Paper Series 5884, CESifo Group Munich.
    6. 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.
    7. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Paper 1227, Federal Reserve Bank of Cleveland.
    8. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Byeongchan Seong & Sung K. Ahn & Peter Zadrozny, 2007. "Cointegration Analysis with Mixed-Frequency Data," CESifo Working Paper Series 1939, CESifo Group Munich.
    15. Baoline Chen & Peter A. Zadrozny, 2009. "Further Model-Based Estimates of U.S. Total Manufacturing Production Capital and Technology, 1949-2005," Working Papers 430, U.S. Bureau of Labor Statistics.
    16. 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.
    17. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 13(3), pages 698-721.
    18. Qian, Hang, 2013. "Vector Autoregression with Mixed Frequency Data," MPRA Paper 47856, University Library of Munich, Germany.
    19. Qian, Hang, 2012. "A Flexible State Space Model and its Applications," MPRA Paper 38455, University Library of Munich, Germany.
    20. 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.
    21. 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.
    22. 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.
    23. Thornton, Michael A. & Chambers, Marcus J., 2017. "Continuous time ARMA processes: Discrete time representation and likelihood evaluation," Journal of Economic Dynamics and Control, Elsevier, vol. 79(C), pages 48-65.
    24. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    25. 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.
    26. 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.
    27. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed frequency structural VARs," Working Paper 2014/01, Norges Bank.
    28. 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.
    29. Chambers, Marcus J., 2016. "The estimation of continuous time models with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 390-404.
    30. 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.
    31. Michael A. Thornton & Marcus J. Chambers, 2013. "Temporal aggregation in macroeconomics," Chapters,in: Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 13, pages 289-310 Edward Elgar Publishing.
    32. Hecq A.W. & Urbain J.R.Y.J. & Götz T.B., 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).
    33. Paul Viefers, 2011. "Bayesian Inference for the Mixed-Frequency VAR Model," Discussion Papers of DIW Berlin 1172, DIW Berlin, German Institute for Economic Research.
    34. Qian, Hang, 2016. "A computationally efficient method for vector autoregression with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 433-437.
    35. 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.
    36. 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.
    37. 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.

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

    Cited by:

    1. Evan W. Anderson & Lars Peter Hansen & Ellen R. McGrattan & Thomas J. Sargent, 1995. "On the mechanics of forming and estimating dynamic linear economies," Staff Report 198, Federal Reserve Bank of Minneapolis.
    2. McGrattan, Ellen R., 1994. "The macroeconomic effects of distortionary taxation," Journal of Monetary Economics, Elsevier, vol. 33(3), pages 573-601, June.
    3. 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.

  14. 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. 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).
    2. 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.

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 7 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-EFF: Efficiency & Productivity (4) 2005-09-29 2005-11-19 2010-04-17 2010-04-17
  2. NEP-ECM: Econometrics (3) 2004-07-17 2005-08-13 2016-02-17
  3. NEP-ETS: Econometric Time Series (3) 2004-07-04 2005-08-13 2016-02-17
  4. NEP-BEC: Business Economics (2) 2010-04-17 2010-04-17
  5. NEP-FIN: Finance (1) 2005-08-13
  6. NEP-MAC: Macroeconomics (1) 2004-07-04

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