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Citations for "Priors for macroeconomic time series and their application"

by John Geweke

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  1. Eric Jacquier & Nicholas G. Polson & Peter Rossi, "undated". "Stochastic Volatility: Univariate and Multivariate Extensions," Rodney L. White Center for Financial Research Working Papers 19-95, Wharton School Rodney L. White Center for Financial Research.
  2. Ching-Wai (Jeremy) Chiu & Haroon Mumtaz & Gabor Pinter, 2014. "Fat-tails in VAR Models," Working Papers 714, Queen Mary University of London, School of Economics and Finance.
  3. Jesús Fernández-Villaverde, 2009. "The Econometrics of DSGE Models," PIER Working Paper Archive 09-008, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  4. Pfann, Gerard A. & Schotman, Peter C. & Tschernig, Rolf, 1996. "Nonlinear interest rate dynamics and implications for the term structure," Journal of Econometrics, Elsevier, vol. 74(1), pages 149-176, September.
  5. Jacquier, Eric & Polson, Nicholas G. & Rossi, P.E.Peter E., 2004. "Bayesian analysis of stochastic volatility models with fat-tails and correlated errors," Journal of Econometrics, Elsevier, vol. 122(1), pages 185-212, September.
  6. Juan F. Rubio-Ramirez & Jesus Fernández-Villaverde, 2005. "Estimating dynamic equilibrium economies: linear versus nonlinear likelihood," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 891-910.
  7. Eric Ghysels, 1993. "A time series model with periodic stochastic regime switching," Discussion Paper / Institute for Empirical Macroeconomics 84, Federal Reserve Bank of Minneapolis.
  8. Kajal Lahiri & Jian Gao, 2001. "Bayesian Analysis of Nested Logit Model by Markov Chain Monte Carlo," Discussion Papers 01-14, University at Albany, SUNY, Department of Economics.
  9. John F. Geweke, 1995. "Monte Carlo simulation and numerical integration," Staff Report 192, Federal Reserve Bank of Minneapolis.
  10. Nikolay Hristov & Oliver Hülsewig & Thomas Siemsen & Timo Wollmershäuser, 2014. "Smells Like Fiscal Policy? Assessing the Potential Effectiveness of the ECB's OMT Program," CESifo Working Paper Series 4628, CESifo Group Munich.
  11. Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez, 2006. "Estimating Macroeconomic Models: A Likelihood Approach," Levine's Bibliography 122247000000000849, UCLA Department of Economics.
  12. Jesús Fernández-Villaverde & Juan Francisco Rubio-Ramírez, 2004. "Estimating nonlinear dynamic equilibrium economies: a likelihood approach," FRB Atlanta Working Paper 2004-1, Federal Reserve Bank of Atlanta.
  13. Tsionas, Efthymios G., 1998. "Monte Carlo inference in econometric models with symmetric stable disturbances," Journal of Econometrics, Elsevier, vol. 88(2), pages 365-401, November.
  14. Schotman, Peter, 1996. "A Bayesian approach to the empirical valuation of bond options," Journal of Econometrics, Elsevier, vol. 75(1), pages 183-215, November.
  15. Watanabe, Toshiaki, 2001. "On sampling the degree-of-freedom of Student's-t disturbances," Statistics & Probability Letters, Elsevier, vol. 52(2), pages 177-181, April.
  16. Alain Desgagné & Jean-François Angers, 2007. "Conflicting information and location parameter inference," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 67-97.
  17. Fernández, C. & Steel, M.F.J., 1996. "On Bayesian Modelling of Fat Tails and Skewness," Discussion Paper 1996-58, Tilburg University, Center for Economic Research.
  18. Bin Chen & Jinho Choi & Juan Carlos Escanciano, 2015. "Testing for Fundamental Vector Moving Average Representations," Caepr Working Papers 2015-022 Classification-C, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
  19. Lori Dickes & Elizabeth Crouch, 2015. "The Impact of Changing Lake Levels on Property Values: A Hedonic Model of Lake Thurmond," The Review of Regional Studies, Southern Regional Science Association, vol. 45(3), pages 221-235, Winter.
  20. Cross, Jamie & Poon, Aubrey, 2016. "Forecasting structural change and fat-tailed events in Australian macroeconomic variables," Economic Modelling, Elsevier, vol. 58(C), pages 34-51.
  21. Jean-Francois Angers, 2000. "P-credence and outliersl," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3-4), pages 81-108.
  22. Christopher A. Sims, 1993. "A Nine-Variable Probabilistic Macroeconomic Forecasting Model," NBER Chapters, in: Business Cycles, Indicators and Forecasting, pages 179-212 National Bureau of Economic Research, Inc.
  23. Sauer, Johannes, 2008. "Quota Deregulation and Organic versus Conventional Milk – A Bayesian Distance Function Approach," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6425, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  24. Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez, 2006. "The Research Agenda: Jesus Fernandez-Villaverde and Juan F. Rubio-Ramirez on Estimating DSGE Models," EconomicDynamics Newsletter, Review of Economic Dynamics, vol. 8(1), November.
  25. Marriott, John & Newbold, Paul, 2000. "The strength of evidence for unit autoregressive roots and structural breaks: A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 98(1), pages 1-25, September.
  26. Stuart J. Fowler & Jennifer J. Wilgus, 2011. "An Estimatable DCDP Model of Search and Matching in Real Estate Markets," Working Papers 201105, Middle Tennessee State University, Department of Economics and Finance.
  27. Houser, Daniel, 2003. "Bayesian analysis of a dynamic stochastic model of labor supply and saving," Journal of Econometrics, Elsevier, vol. 113(2), pages 289-335, April.
  28. Vosseler, Alexander, 2016. "Bayesian model selection for unit root testing with multiple structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 616-630.
This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.