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A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series

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  • George Monokroussos

Abstract

Estimating Limited Dependent Variable Time Series models through standard extremum methods can be a daunting computational task because of the need for integration of high order multiple integrals and/or numerical optimization of difficult objective functions. This paper proposes a classical Markov Chain Monte Carlo (MCMC) estimation technique with data augmentation that overcomes both of these problems. The asymptotic properties of the proposed estimator are established. Furthermore, a practical and flexible algorithmic framework for this class of models is proposed and is illustrated using simulated data, thus also offering some insight into the small-sample biases of such estimators. Finally, the versatility of the proposed framework is illustrated with an application of a dynamic tobit model for the Open Market Desk's Daily Reaction Function.

Suggested Citation

  • George Monokroussos, 2009. "A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series," Discussion Papers 09-07, University at Albany, SUNY, Department of Economics.
    • Handle: RePEc:nya:albaec:09-07
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    File URL: http://www.albany.edu/economics/research/workingp/2009/mcmcldv.pdf
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    References listed on IDEAS

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    1. de Jong, Robert M. & Woutersen, Tiemen, 2011. "Dynamic Time Series Binary Choice," Econometric Theory, Cambridge University Press, vol. 27(04), pages 673-702, August.
    2. Chib, Siddhartha, 2001. "Markov chain Monte Carlo methods: computation and inference," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 57, pages 3569-3649 Elsevier.
    3. Richard Clarida & Jordi Galí & Mark Gertler, 2000. "Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory," The Quarterly Journal of Economics, Oxford University Press, vol. 115(1), pages 147-180.
    4. Robert Jong & Ana María Herrera, 2011. "Dynamic Censored Regression and the Open Market Desk Reaction Function," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 228-237, April.
    5. John P. Judd & Glenn D. Rudebusch, 1998. "Taylor's rule and the Fed, 1970-1997," Economic Review, Federal Reserve Bank of San Francisco, pages 3-16.
    6. Feinman, Joshua N, 1993. "Estimating the Open Market Desk's Daily Reaction Function," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 25(2), pages 231-247, May.
    7. Albert, James H & Chib, Siddhartha, 1993. "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 1-15, January.
    8. Michael J. Dueker, 1999. "Measuring monetary policy inertia in target Fed funds rate changes," Review, Federal Reserve Bank of St. Louis, issue Sep, pages 3-10.
    9. Orphanides, Athanasios, 2004. "Monetary Policy Rules, Macroeconomic Stability, and Inflation: A View from the Trenches," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(2), pages 151-175, April.
    10. Hajivassiliou, Vassilis & McFadden, Daniel & Ruud, Paul, 1996. "Simulation of multivariate normal rectangle probabilities and their derivatives theoretical and computational results," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 85-134.
    11. Lee, Lung-fei, 1999. "Estimation of dynamic and ARCH Tobit models," Journal of Econometrics, Elsevier, vol. 92(2), pages 355-390, October.
    12. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    13. Eichengreen, Barry & Watson, Mark W & Grossman, Richard S, 1985. "Bank Rate Policy under the Interwar Gold Standard: A Dynamic Probit Model," Economic Journal, Royal Economic Society, vol. 95(379), pages 725-745, September.
    14. Pesaran, M Hashem & Samiei, Hossein, 1992. "An Analysis of the Determination of Deutsche Mark/French Franc Exchange Rate in a Discrete-Time Target-Zone Model," Economic Journal, Royal Economic Society, vol. 102(411), pages 388-401, March.
    15. Demiralp, Selva & Farley, Dennis, 2005. "Declining required reserves, funds rate volatility, and open market operations," Journal of Banking & Finance, Elsevier, vol. 29(5), pages 1131-1152, May.
    16. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    17. Vassilis A. Hajivassiliou & Daniel L. McFadden, 1998. "The Method of Simulated Scores for the Estimation of LDV Models," Econometrica, Econometric Society, vol. 66(4), pages 863-896, July.
    18. Dueker, Michael, 1999. "Conditional Heteroscedasticity in Qualitative Response Models of Time Series: A Gibbs-Sampling Approach to the Bank Prime Rate," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 466-472, October.
    19. Athanasios Orphanides, 2002. "Monetary-Policy Rules and the Great Inflation," American Economic Review, American Economic Association, vol. 92(2), pages 115-120, May.
    20. George Monokroussos, 2011. "Dynamic Limited Dependent Variable Modeling and U.S. Monetary Policy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43, pages 519-534, March.
    21. Jae-Young Kim, 1998. "Large Sample Properties of Posterior Densities, Bayesian Information Criterion and the Likelihood Principle in Nonstationary Time Series Models," Econometrica, Econometric Society, vol. 66(2), pages 359-380, March.
    22. Michael J. Dueker, 2002. "Regime-dependent recession forecasts and the 2001 recession," Review, Federal Reserve Bank of St. Louis, issue Nov, pages 29-36.
    23. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-1057, September.
    24. Canova, Fabio, 1994. "Were Financial Crises Predictable?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 26(1), pages 102-124, February.
    25. Geweke, John & Keane, Michael, 2001. "Computationally intensive methods for integration in econometrics," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 56, pages 3463-3568 Elsevier.
    26. Donald W. K. Andrews, 1999. "Estimation When a Parameter Is on a Boundary," Econometrica, Econometric Society, vol. 67(6), pages 1341-1384, November.
    27. Steven Wei, 1999. "A bayesian approach to dynamic tobit models," Econometric Reviews, Taylor & Francis Journals, vol. 18(4), pages 417-439.
    28. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
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    Cited by:

    1. George Monokroussos, 2011. "Dynamic Limited Dependent Variable Modeling and U.S. Monetary Policy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43, pages 519-534, March.
    2. George Monokroussos, 2006. "A Dynamic Tobit Model for the Open Market Desk's Daily Reaction Function," Computing in Economics and Finance 2006 390, Society for Computational Economics.

    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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