Uncertainty through the lenses of a mixed-frequency Bayesian panel Markov-switching model. (English) Zbl 1412.62197

Summary: We propose a Bayesian panel model for mixed frequency data, where parameters can change over time according to a Markov process. Our model allows for both structural instability and random effects. To estimate the model, we develop a Markov Chain Monte Carlo algorithm for sampling from the joint posterior distribution, and we assess its performance in simulation experiments. We use the model to study the effects of macroeconomic uncertainty and financial uncertainty on a set of variables in a multi-country context including the US, several European countries and Japan. We find that there are large differences in the effects of uncertainty in the contraction regime and the expansion regime. The use of mixed frequency data amplifies the relevance of the asymmetry. Financial uncertainty plays a more important role than macroeconomic uncertainty, and its effects are also more homogeneous across variables and countries. Disregarding either the mixed-frequency component or the Markov-switching mechanism can bring to substantially different results.


62P20 Applications of statistics to economics
62M02 Markov processes: hypothesis testing
Full Text: DOI Euclid


[1] Baker, S. R., Bloom, N. and Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics131 1593-1636.
[2] Bassetti, F., Casarin, R. and Leisen, F. (2014). Beta-product dependent Pitman-Yor processes for Bayesian inference. J. Econometrics180 49-72. · Zbl 1298.62148
[3] Billio, M., Casarin, R., Ravazzolo, F. and van Dijk, H. K. (2012). Combination schemes for turning point predictions. Quarterly Review of Economics and Finance52 402-412.
[4] Billio, M., Casarin, R., Costola, M. and Pasqualini, A. (2016a). An entropy-based early warning indicator for systemic risk. Journal of International Financial Markets, Institutions and Money45 42-59.
[5] Billio, M., Casarin, R., Ravazzolo, F. and Van Dijk, H. K. (2016b). Interconnections between Eurozone and US booms and busts using a Bayesian panel Markov-switching VAR model. J. Appl. Econometrics31 1352-1370.
[6] Bloom, N. (2009). The impact of uncertainty shocks. Econometrica77 623-685. · Zbl 1176.91114
[7] Bloom, N. (2014). Fluctuations in uncertainty. Journal of Economic Perspectives28 153-76.
[8] Caggiano, G., Castelnuovo, E. and Groshenny, N. (2014). Uncertainty shocks and unemployment dynamics in U.S. recessions. Journal of Monetary Economics67 78-92.
[9] Canova, F. and Ciccarelli, M. (2004). Forecasting and turning point predictions in a Bayesian panel VAR model. J. Econometrics120 327-359. · Zbl 1282.62211
[10] Canova, F. and Ciccarelli, M. (2009). Estimating multicountry VAR models. Internat. Econom. Rev.50 929-959.
[11] Carriero, A., Clark, T. E. and Marcellino, M. (2018). Measuring uncertainty and its impact on the economy. Rev. Econ. Stat. To appear.
[12] Casarin, R., Foroni, C., Marcellino, M. and Ravazzolo, F. (2018). Supplement to “Uncertainty through the lenses of a mixed-frequency Bayesian panel Markov-switching model.” DOI:10.1214/18-AOAS1168SUPPB, DOI:10.1214/18-AOAS1168SUPPC, DOI:10.1214/18-AOAS1168SUPPD, DOI:10.1214/18-AOAS1168SUPPE. · Zbl 1412.62197
[13] Chib, S. and Greenberg, E. (1995). Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models. J. Econometrics68 339-360. · Zbl 0833.62103
[14] Clark, T. E. and Ravazzolo, F. (2015). Macroeconomic forecasting performance under alternative specifications of time-varying volatility. J. Appl. Econometrics30 551-575.
[15] Dovern, J., Fritsche, U. and Slacalek, J. (2012). Disagreement among forecasters in G7 countries. Rev. Econ. Stat.94 1081-1096.
[16] Foroni, C. and Marcellino, M. (2014). Mixed-frequency structural models: Identification, estimation, and policy analysis. J. Appl. Econometrics29 1118-1144.
[17] Foroni, C., Marcellino, M. and Schumacher, C. (2015). Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials. J. Roy. Statist. Soc. Ser. A178 57-82.
[18] Frühwirth-Schnatter, S. (2006). Finite Mixture and Markov Switching Models. Springer, New York. · Zbl 1108.62002
[19] Ghysels, E., Santa-Clara, P. and Valkanov, R. (2005). There is a risk-return trade-off after all. J. Financ. Econ.76 509-548.
[20] Ghysels, E., Sinko, A. and Valkanov, R. (2007). Midas regressions: Further results and new directions. Econometric Rev.26 53-90. · Zbl 1108.62092
[21] Guérin, P. and Marcellino, M. (2013). Markov-switching MIDAS models. J. Bus. Econom. Statist.31 45-56.
[22] Jurado, K., Ludvigson, S. C. and Ng, S. (2015). Measuring uncertainty. Am. Econ. Rev.105 1177-1216.
[23] Kaufmann, S. (2010). Dating and forecasting turning points by Bayesian clustering with dynamic structure: A suggestion with an application to Austrian data. J. Appl. Econometrics25 309-344.
[24] Kaufmann, S. (2015). \(K\)-state switching models with time-varying transition distributions—does loan growth signal stronger effects of variables on inflation? J. Econometrics187 82-94. · Zbl 1337.62373
[25] Liu, J. S. (1994). The collapsed Gibbs sampler in Bayesian computations with applications to a gene regulation problem. J. Amer. Statist. Assoc.89 958-966. · Zbl 0804.62033
[26] Magnus, J. R. and Neudecker, H. (1999). Matrix Differential Calculus with Applications in Statistics and Econometrics. Wiley, Chichester. · Zbl 0912.15003
[27] Min, C. and Zellner, A. (1993). Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates. J. Econometrics56 89-118. · Zbl 0800.62800
[28] Mumtaz, H. and Theodoridis, K. (2018). The changing transmission of uncertainty shocks in the US. J. Bus. Econom. Statist.36 239-252.
[29] Pettenuzzo, D., Timmermann, A. and Valkanov, R. (2016). A MIDAS approach to modeling first and second moment dynamics. J. Econometrics193 315-334. · Zbl 1391.62293
[30] Robert, C. P. and Casella, G. (1999). Monte Carlo Statistical Methods. Springer, New York. · Zbl 0935.62005
[31] Roberts, G. O. and Sahu, S. K. (1997). Updating schemes, correlation structure, blocking and parameterization for the Gibbs sampler. J. Roy. Statist. Soc. Ser. B59 291-317. · Zbl 0886.62083
[32] Rodriguez, A. and Puggioni, G. (2010). Mixed frequency models: Bayesian approaches to estimation and prediction. Int. J. Forecast.26 293-311.
[33] Rossi, B. and Sekhposyan, T. (2015). Macroeconomic uncertainty indices based on nowcast and forecast error distributions. Am. Econ. Rev.105 650-655.
[34] Scotti, C. (2016). Surprise and uncertainty indexes: Real-time aggregation of real-activity macro-surprises. Journal of Monetary Economics82 1-19.
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