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2010 | 2 | nr 4 | 279--314
Tytuł artykułu

Estimation Methods Comparison of SVAR Models with a Mixture of Two Normal Distributions

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper addresses the issue of obtaining maximum likelihood estimates of parameters for structural VAR models with a mixture of distributions. Hence the problem does not have a closed form solution, numerical optimization procedures need to be used. A Monte Carlo experiment is designed to compare the performance of four maximization algorithms and two estimation strategies. It is shown that the EM algorithm outperforms the general maximization algorithms such as BFGS, NEWTON and BHHH. Moreover, simplification of the problem introduced in the two steps quasi ML method does not worsen small sample properties of the estimators and therefore may be recommended in the empirical analysis. (original abstract)
Rocznik
Tom
2
Numer
Strony
279--314
Opis fizyczny
Twórcy
  • Wroclaw University of Technology, Poland
Bibliografia
  • [1] Day, N.E. (1969). Estimating the components of a mixture of normal distributions. "Biometrica" 56 (3), 463-474.
  • [2] Dempster, A.P., Laird N. M., Rubin D.B. (1977). Maximum-likelihood from incomplete data via the EM algorithm. "Journal of Royal Statistics Society Ser. B (methodological)" 39, 1-38.
  • [3] Diebold, F.X., Rudebusch G.D. (1996). Measuring business cycle: A modern perspective. "Review of Economics and Statistics" 78, 67-77.
  • [4] Goodwin, T.H. (1993). Business-cycle analysis with a markov-switching model. "Journal of Business & Economic Statistics" 11 (3), 331-339.
  • [5] Hamilton, J.D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. "Econometrica" 57, 357-384.
  • [6] Hathaway, R.J. (1985). A constrained formulation of maximum-likelihood estimation for normal mixture distribution. "The Annals of Statistics" 18 (2), 795-800.
  • [7] Jedidi, K., Jagpal H.S., Desarbo W.S. (1997). Finite mixture structural equation models for response-based segmentation and unobserved heterogeneity. "Marketing Science" 16, 35-59.
  • [8] Kiefer, A., Wolfowitz J. (1956). Consistency of the maximum likelihood estimator in the presence of infinitely many incidental parameters. "Annals of Mathematical Statistics" 27 (4), 887-906.
  • [9] Kim, C.-J., Nelson C.R. (1998). Business cycle turning points, a new coincident index, and tests of duration dependence based on a dynamic factor model with regime switching. "The Review of Economics and Statistics" 80 (2), 188-201.
  • [10] Kim, C.-J., Nelson C.R. (1999). Has the U.S. economy become more stable? a bayesian approach based on a markow-switching model of the business cycle. "The Review of Economics and Statistics" 81 (4), 608-616.
  • [11] Krolzig, H.-M. (1997). Markov-Switching Vector Autoregressions: Modelling, Statistical Inference, and Application to Business Cycle Analysis. Springer-Verlag, Berlin.
  • [12] Lanne, M. (2006). Nonlinear dynamics of interest rate and in ation. "Journal of Applied Econometrics" 21 (8), 1157-1168.
  • [13] Lanne, M., Lütkepohl H. (2008). Identifying monetary policy shocks via changes in volatility. "Journal of Money, Credit and Banking" 40, 1131-1149.
  • [14] Lanne, M., Lütkepohl H. (2009). Structural vector autoregressions with nonnormal residuals. "Journal of Business and Economic Statistics" 28 (1), 159-168.
  • [15] Liesenfeld, R. (1998). Dynamic bivariate mixture models: modeling the behavior of prices and trading volume. "Journal of Business and Economic Statistics" 16, 101-109.
  • [16] Liesenfeld, R. (2001). A generalized bivariate mixture model for stock price volatility and trading volume. "Journal of Econometrics" 104, 141-178.
  • [17] Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer-Verlag, Berlin.
  • [18] McLachlan, G.J., Krishnan T. (1997). The EM Algorithm and Extensions. Wiley.
  • [19] McLachlan, G.J., Peel D. (2000). Finite Mixture Models. Wiley.
  • [20] Perez-Quiros, G., Timmermann A. (2001). Business cycle asymmetries in stock returns: Evidence from higher order moments and conditional densities. "Journal of Econometrics" 103, 259-306.
  • [21] Redner, R.A., Walker H.F. (1984). Mixture densities, maximum likelihood and the EM algorithm. "Society for Industrial and Appied Mathematics" 26, 195-239.
  • [22] Rigobon, R. (2003). Identification through heteroscedasticity. "Review of Economics and Statistics" 85, 777-792.
  • [23] Rothenberg, T.J. (1971). Identification in parametric models. "Econometrica" 39 (3), 577-591.
  • [24] Sims, C.A., Zha T. (2006). Were there regime switches in U.S. monetary policy? "American Economic Review" 96, 54-81.
  • [25] Smith, A., Naik P.A., Tsai C.-L. (2006). Markov-switching model selection using Kullback-Leibler divergence. "Journal of Econometrics" 134, 553-577.
  • [26] Wong, C.S., Li W.K. (2000). On a mixture autoregressive model. "Journal of Royal Statistical Society B" 62 (1), 95-115.
  • [27] Zhang, Z., Li W.K., Yuen K.C. (2006). On a mixture garch time-series model. "Journal of Time Series Analysis" 27 (4), 577-597.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171226379

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