Estimation of the probit model with autocorrelated errors via the MCECM algorithm

References

• Albert , J. H. and Chib , S. 1993 . Bayesian analysis of binary and polychotomous response data . Journal of the American Statistical Association , 88 : 669 – 679 .
   Web of Science ®Google Scholar
• Boschen , J. F. and Mills , L. O. 1995 . The relationship between narrative and money market indicators of monetary policy . Economic Inquiry , 33 : 23 – 44 .
   Google Scholar
• Chib , S. and Greenberg , E. 1994 . Bayes inference in regression models with ARMA(p,q) errors . Journal of Econometrics , 64 : 183 – 206 .
   Web of Science ®Google Scholar
• Chib , S. and Greenberg , E. 1998 . Bayesian analysis of the multivariate probit model . Biometrika , 85 : 347 – 361 .
   Web of Science ®Google Scholar
• Chib , S. , Greenberg , E. and Winkelmann , R. 1998 . Posterior simulation and Bayes factors in panel count data models . Journal of Econometrics , 86 : 33 – 54 .
   Web of Science ®Google Scholar
• Dempster , A. P. , Laird , N. M. and Rubin , D. B. 1977 . Maximum likelihood from incomplete data via the EM algorithm . Journal of the Royal Statistical Society, Series B , 39 : 1 – 38 .
   Google Scholar
• Estrella , A. and Hardouvelis , G. 1991 . The term structure as a predictor of real economic activity . Journal of Finance , 46 : 555 – 576 .
   Web of Science ®Google Scholar
• Estrella , A. and Mishkin , F. S. 1998 . Predicting U.S. recessions: Financial variables as leading indicators . Review of Economics and Statistics , 80 : 45 – 61 .
   Web of Science ®Google Scholar
• Estrella , A. and Rodrigues , A. P. 1998 . Consistent covariance matrix estimation in Probit model with autocorrelated errors Working paper
   Google Scholar
• Huang , C. J. , Sloan , F. A. and Adamache , K. W. 1987 . Estimation of seemingly unrelated Tobit regressions via the EM algorithm . Journal of Business & Economic Statistics , 5 : 425 – 430 .
   Web of Science ®Google Scholar
• Huang , River H. C. 1998 . Estimation of the SUR tobit model by the MCECM algorithm Working paper
   Google Scholar
• Louis , T. A. 1982 . Finding the observed information matrix using the EM algorithm . Journal of the Royal Statistical Society , 44 : 226 – 233 .
   Google Scholar
• Maddala , G. S. 1983 . Limited Dependent and Qualitative Variables in Econometrics , Cambridge : Cambridge University Press .
   Google Scholar
• Magnus , J. R. 1978 . Maximum likelihood estimation of the GLS model with unknown parameters in the disturbance covariance matrix . Journal of Econometrics , 7 : 281 – 312 .
   Google Scholar
• Meng , X. and Rubin , D. B. 1996 . “ Efficient methods for estimation and testing with seemingly unrelated regressions in the presence of latent variables and missing observations ” . In Bayesian Analysis in Statistics and Econometrics , Edited by: Berry , D. A. , Chaloner , K. M. and Geweke , J. K. 215 – 227 . John Wiley & Sons, Inc .
   Google Scholar
• Poirier , D. J. and Ruud , P. A. 1988 . Probit with dependent observations . Review of Economic Studies , 15 : 593 – 614 .
   Google Scholar
• Romer , C. D. and Romer , D. H. 1989 . Does monetary policy matter? A new test in the spirit of Friedman and Schwartz . NBER Macroeconomics Annual , : 121 – 183 .
   Web of Science ®Google Scholar
• Shen , C. H. and Chen , H. R. 1996 . Monetary policy index and policy reaction function . Academia Economic Papers , 24 : 559 – 590 .
   Google Scholar
• Shen , C. H. and Huang , River H. C. 1998 . Estimation of the reaction function of monetary policy by the probit model with autocorrelated errors Working paper
   Google Scholar
• Stock , J. and Watson , M. 1989 . “ New indexes of coincident and leading economic indicators ” . In NBER Macroeconomic Annual Edited by: Blanchard , O. and Fischer , S. Vol. 4 , 351 – 394 .
   Google Scholar
• Tanner , M. A. 1996 . Tools for Statistical Inference , New York : Springer-Verlag .
   Google Scholar
• Wei , G. C. G. and Tanner , M. A. 1990 . A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms . Journal of the American Statistical Association , 85 : 699 – 704 .
   Web of Science ®Google Scholar