Advanced Search
MyIDEAS: Login to save this article or follow this journal

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

Contents:

Author Info

  • 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 discussed. 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 proposed framework is used to estimate a dynamic, discrete-choice monetary policy reaction function for the United States during the Greenspan years. Copyright Springer Science+Business Media New York 2013

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://hdl.handle.net/10.1007/s10614-012-9339-6
Download Restriction: Access to full text is restricted to subscribers.

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 42 (2013)
Issue (Month): 1 (June)
Pages: 71-105

as in new window
Handle: RePEc:kap:compec:v:42:y:2013:i:1:p:71-105

Contact details of provider:
Web page: http://www.springerlink.com/link.asp?id=100248
More information through EDIRC

Related research

Keywords: Discrete choice models; Censored models; Data augmentation; Markov Chain Monte Carlo; Gibbs sampling; Taylor rules; Alan Greenspan; C15; C24; C25; E52;

Other versions of this item:

Find related papers by JEL classification:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. 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 231-47, May.
  2. V A Hajivassiliou & DL McFadden, 1997. "The Method of Simulated Scores for the Estimation of LDV Models," STICERD - Econometrics Paper Series /1997/328, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  3. 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.
  4. 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.
  5. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
  6. 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 725-45, September.
  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. 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.
  9. Richard Clarida & Jordi Galí & Mark Gertler, 1997. "Monetary policy rules and macroeconomic stability: Evidence and some theory," Economics Working Papers 350, Department of Economics and Business, Universitat Pompeu Fabra, revised May 1999.
  10. 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 151-75, April.
  11. Michael Dueker, 1998. "Conditional heteroskedasticity in qualitative response models of time series: a Gibbs sampling approach to the bank prime rate," Working Papers 1998-011, Federal Reserve Bank of St. Louis.
  12. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
  13. 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.
  14. Michael Dueker, 1999. "Measuring monetary policy inertia in target Fed funds rate changes," Review, Federal Reserve Bank of St. Louis, issue Sep, pages 3-10.
  15. George Monokroussos, 2006. "Dynamic Limited Dependent Variable Modeling and U.S. Monetary Policy," Discussion Papers 06-02, University at Albany, SUNY, Department of Economics.
  16. Donald W. K. Andrews, 1999. "Estimation When a Parameter Is on a Boundary," Econometrica, Econometric Society, vol. 67(6), pages 1341-1384, November.
  17. Steven Wei, 1999. "A bayesian approach to dynamic tobit models," Econometric Reviews, Taylor & Francis Journals, vol. 18(4), pages 417-439.
  18. 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.
  19. 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.
  20. Robert M. de Jong & Tiemen Woutersen, 2007. "Dynamic time series binary choice," Economics Working Paper Archive 538, The Johns Hopkins University,Department of Economics.
  21. Lee, Lung-fei, 1999. "Estimation of dynamic and ARCH Tobit models," Journal of Econometrics, Elsevier, vol. 92(2), pages 355-390, October.
  22. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-57, September.
  23. Jong, Robert & Herrera, Ana María, 2011. "Dynamic Censored Regression and the Open Market Desk Reaction Function," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 228-237.
  24. Athanasios Orphanides, 2002. "Monetary policy rules and the Great Inflation," Finance and Economics Discussion Series 2002-8, Board of Governors of the Federal Reserve System (U.S.).
  25. Michael J. Dueker, 2002. "Regime-dependent recession forecasts and the 2001 recession," Review, Federal Reserve Bank of St. Louis, issue Nov, pages 29-36.
  26. Canova, Fabio, 1994. "Were Financial Crises Predictable?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 26(1), pages 102-24, February.
  27. 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.
  28. 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.
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. George Monokroussos, 2006. "Dynamic Limited Dependent Variable Modeling and U.S. Monetary Policy," Discussion Papers 06-02, University at Albany, SUNY, Department of Economics.
  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.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:42:y:2013:i:1:p:71-105. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Guenther Eichhorn) or (Christopher F. Baum).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.