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Dynamic time series binary choice

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  • Tiemen Woutersen
  • Robert M. de Jong

Abstract

This paper considers dynamic time series binary choice models. It shows in a time series setting the validity of the dynamic probit likelihood procedure when lags of the dependent binary variable are used as regressors, and it establishes the asymptotic validity of Horowitz' smoothed maximum score estimation of dynamic binary choice models with lags of the dependent variable as regressors. The latent error is explicitly allowed to be correlated. It turns out that no long-run variance estimator is needed for the validity of the smoothed maximum score procedure in the dynamic time series framework. One novel aspect of this paper is a proof that weak dependence properties hold for dynamic binary choice models with correlated errors

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File URL: http://repec.org/esNASM04/up.15309.1075481066.pdf
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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 North American Summer Meetings with number 365.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:nasm04:365

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Keywords: binary choice; near epoch dependence; asymptotic theory; smoothed maximum score;

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  1. 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.
  2. repec:cup:etheor:v:13:y:1997:i:3:p:353-67 is not listed on IDEAS
  3. Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-31, May.
  4. Imbens, G.W., 1990. "An Efficient Method Of Moments Estimator For Descrete Choice Models With Choice-Based Sampling," Papers 9009, Tilburg - Center for Economic Research.
  5. Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
  6. Newey, Whitney K. & McFadden, Daniel, 1986. "Large sample estimation and hypothesis testing," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 36, pages 2111-2245 Elsevier.
  7. de Jong, Robert M., 1997. "Central Limit Theorems for Dependent Heterogeneous Random Variables," Econometric Theory, Cambridge University Press, vol. 13(03), pages 353-367, June.
  8. Andrews, Donald W. K., 1987. "Laws of Large Numbers for Dependent Non-Identically Distributed Random Variables," Working Papers 645, California Institute of Technology, Division of the Humanities and Social Sciences.
  9. Matzkin, Rosa L, 1992. "Nonparametric and Distribution-Free Estimation of the Binary Threshold Crossing and the Binary Choice Models," Econometrica, Econometric Society, vol. 60(2), pages 239-70, March.
  10. Poirier, Dale J & Ruud, Paul A, 1988. "Probit with Dependent Observations," Review of Economic Studies, Wiley Blackwell, vol. 55(4), pages 593-614, October.
  11. Cosslett, Stephen R, 1983. "Distribution-Free Maximum Likelihood Estimator of the Binary Choice Model," Econometrica, Econometric Society, vol. 51(3), pages 765-82, May.
  12. Donald W.K. Andrews, 1986. "Consistency in Nonlinear Econometric Models: A Generic Uniform Law of Large Numbers," Cowles Foundation Discussion Papers 790, Cowles Foundation for Research in Economics, Yale University.
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Citations

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Cited by:
  1. Geert Dhaene & Koen Jochmans, 2014. "Split-Panel Jackknife Estimation of Fixed-Effect Models," Sciences Po publications 2014-03, Sciences Po.
  2. GNABO, Jean-Yves & LAURENT, Sébastien & LECOURT, Christelle, . "Does transparency in central bank intervention policy bring noise to the FX market? The case of the Bank of Japan," CORE Discussion Papers RP -2136, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. Stanislav Anatolyev & Nikolay Gospodinov, 2007. "Modeling Financial Return Dynamics by Decomposition," Working Papers w0095, Center for Economic and Financial Research (CEFIR).
  4. Jean-Yves Gnabo & Luiz de Mello & Diego Moccero, 2010. "Interdependencies between Monetary Policy and Foreign Exchange Interventions under Inflation Targeting: The Case of Brazil and the Czech Republic," International Finance, Wiley Blackwell, vol. 13(2), pages 195-221, 08.
  5. Siddhartha Chib & Michael J. Dueker, 2004. "Non-Markovian regime switching with endogenous states and time-varying state strengths," Working Papers 2004-030, Federal Reserve Bank of St. Louis.
  6. Harding, Don & Pagan, Adrian, 2011. "An Econometric Analysis of Some Models for Constructed Binary Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 86-95.
  7. Seo, Myung Hwan & Linton, Oliver, 2007. "A smoothed least squares estimator for threshold regression models," Journal of Econometrics, Elsevier, vol. 141(2), pages 704-735, December.
  8. Valentino Dardanoni & Paolo Li Donni, 2012. "Incentive and Selection Effects of Medigap Insurance on Inpatient Care," EIEF Working Papers Series 1203, Einaudi Institute for Economics and Finance (EIEF), revised Feb 2012.
  9. Taisuke Otsu & Myung Hwan Seo, 2014. "Extending the Scope of Cube Root Asymptotics," STICERD - Econometrics Paper Series /2014/571, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  10. Francis Bismans & Reynald Majetti, 2013. "Forecasting recessions using financial variables: the French case," Empirical Economics, Springer, vol. 44(2), pages 419-433, April.
  11. George Monokroussos, 2013. "A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series," Computational Economics, Society for Computational Economics, vol. 42(1), pages 71-105, June.
  12. Igor Kheifets & Carlos Velasco, 2012. "Model Adequacy Checks for Discrete Choice Dynamic Models," Working Papers w0170, Center for Economic and Financial Research (CEFIR).

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