Non-Markovian Regime-Switching Models
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To date, almost all extensions and applications of Hamilton’s (1989) regime switching model have been based on the assumption that the latent regimeindicator variable follows a Markovian switching process. This paper doubts the universal validity of this assumption and develops an MCMC algorithm for estimation of the non-Markovian regime switching model which employs an autoregressive continuous latent variable in specifying the dynamics of the discrete latent regime-indicator variable within the Probit specification. We show that, in spite of the non-Markovian nature of the discrete regime indicator variable, the Markovian property of this continuous latent variable allows us to successfully estimate the model. Our empirical results suggest that, for modeling volatility of the stock return, the non-Markovian switching model is strongly preferred to the Markovian switching model. However, for modeling the regime-switching nature of the business cycle based on real GDP, the convention of assuming Markovian switching seems to be valid.
Abstract
To date, almost all extensions and applications of Hamilton’s (1989) regime switching model have been based on the assumption that the latent regimeindicator variable follows a Markovian switching process. This paper doubts the universal validity of this assumption and develops an MCMC algorithm for estimation of the non-Markovian regime switching model which employs an autoregressive continuous latent variable in specifying the dynamics of the discrete latent regime-indicator variable within the Probit specification. We show that, in spite of the non-Markovian nature of the discrete regime indicator variable, the Markovian property of this continuous latent variable allows us to successfully estimate the model. Our empirical results suggest that, for modeling volatility of the stock return, the non-Markovian switching model is strongly preferred to the Markovian switching model. However, for modeling the regime-switching nature of the business cycle based on real GDP, the convention of assuming Markovian switching seems to be valid.
참고문헌 (28)
- 정민수, "Return Predictability using an Endogenous Regime Switching Model", 계량경제학보, 33(1), pp.1~27 (2022), 한국계량경제학회[DOI]
- 최승문, "KOSPI 200을 이용한 국면전환 연속시간 확률 변동성 모형의 추정", 계량경제학보, 30(1), pp.59~95 (2019), 한국계량경제학회[DOI]
- 양현진, "내생적 국면전환을 이용한 GARCH 모형", 계량경제학보, 30(2), pp.20~54 (2019), 한국계량경제학회[DOI]
- Albert, J. H., "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance", Journal of Business & Economic Statistics, 11(1), pp.1~15 (1993)
- Albert, J. H., "Bayesian Analysis of Binary and Polychotomous Response Data", Journal of the American Statistical Association, 88, pp.669~679 (1993)
- Chauvet, M., "Forecasting Recessions Using the Yield Curve", Journal of Forecasting, 24(2), pp.77~103 (2005)
- Non-Markovian Regime Switching with Endogenous States and Time-Varying State Strengths, "Chib, S.", Washington University, ~ (2004)
- Dueker, M., "Dynamic Forecasts of Qualitative Variables: A Qual VAR Model of U.S. Recessions", Journal of Business & Economic Statistics, 23(1), pp.96~104 (2005)
- Gelman, A., "Inference from iterative simulation using multiple sequences", Statistical Science, 7(4), pp.457~472 (1992)
- Geweke, J., "Evaluating the accuracy of sampling-based approaches to the calculations of posterior moments", Bayesian Statistics, 4, pp.641~649 (1992)
- Hamilton, James D., "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle", Econometrica, 57(2), pp.357~384 (1989)
- Hamilton, James D., "Handbook of Macroeconomics, 2A", pp.163~201 (2016), Elsevier
- Harvey, A., "Estimation of an Asymmetric Stochastic Volatility Model for Asset Returns", Journal of Business and Economic Statistics, 14(4), pp.429~434 (1996)
- Hering, A. S., "A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales", Resources, 4, pp.70~92 (2015)
- Hwu, S. T., "An N-state endogenous Markovswitching model with applications in Macroeconomics and Finance", Macroeconomic Dynamics, 25(8), pp.1937~1965 (2021)
- Jacquier, E., "Bayesian Analysis of Stochastic Volatility Models with Fat Tails and Correlated Errors", Journal of Econometrics, 122, pp.185~212 (2004)
- Kauppi, H., "Predicting US recessions with dynamic binary response models", The Review of Economics and Statistics, 90(4), pp.777~791 (2008)
- Kim, C. -J., "Has the U.S. Economy Become More Stable? A Bayesian Approach Based on a Markov-Switching Model of Business Cycle", Review of Economics and Statistics, 81(4), pp.608~616 (1999)
- Kim, C. -J., "Estimation of Markov RegimeSwitching Regressions with Endogenous Switching", Journal of Econometrics, 143(2), pp.263~273 (2008)
- Li, W. K., "Time Series Models Based on Generalized Linear Models:Some Further Results", Biometrics, 50, pp.506~511 (1994)
- Neale, M. C., "Regime Switching Modeling of Substance Use: Time-Varying and Second-Order Markov Models of Individual Probability Plots", Structural Equation Modeling, 23(2), pp.221~233 (2016)
- Raftery, A. E., "A Model for High-Order Markov Chains", Journal of Royal Statistical Society, Series B, 47, pp.528~539 (1985)
- Raftery, A. E., "Stopping the Gibbs sampler, the use of morphology, and other issues in spatial statistics", Annals of the Institute of Statistical Mathematics, 43(1), pp.32~43 (1991)
- Siu, T. K., "A Higher-Order Markov-Switching Model for Risk Measurement", Computers and Mathematics with Applications, 58, pp.1~10 (2009)
- Startz, R., "Binomial Autoregressive Moving Average Models with an Application to US Recessions", Journal of Business and Economic Statistics, 26(1), pp.1~8 (2008)
- Watanabe, S., "Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory", Journal of Machine Learning Research, 11, pp.3571~3594 (2010)
- Yu, J., "On Leverage in a Stochastic Volatility Model", Journal of Econometrics, 127, pp.165~178 (2005)
- Zeger, S. L., "Markov Regression Models for time series:A quasi-likelihood approach", Biometrics, 44, pp.1019~1031 (1988)