Legrand, Romain (2019): Time-Varying Vector Autoregressions: Efficient Estimation, Random Inertia and Random Mean.
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Abstract
Time-varying VAR models represent fundamental tools for the anticipation and analysis of economic crises. Yet they remain subject to a number of limitations. The conventional random walk assumption used for the dynamic parameters appears excessively restrictive, and the existing estimation procedures are largely inefficient. This paper improves on the existing methodologies in four directions: i) it introduces a general time-varying VAR model which relaxes the standard random walk assumption and defines the dynamic parameters as general autoregressive processes with equation-specific mean values and autoregressive coefficients. ii) it develops an efficient estimation algorithm for the model which proceeds equation by equation and combines the traditional Kalman filter approach with the recent precision sampler methodology. iii) it develops extensions to estimate endogenously the mean values and autoregressive coefficients associated with each dynamic process. iv) through a case study of the Great Recession in four major economies (Canada, the Euro Area, Japan and the United States), it establishes that forecast accuracy can be significantly improved by using the proposed general time-varying model and its extensions in place of the traditional random walk specification.
Item Type: | MPRA Paper |
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Original Title: | Time-Varying Vector Autoregressions: Efficient Estimation, Random Inertia and Random Mean |
Language: | English |
Keywords: | Time-varying coefficients; Stochastic volatility; Bayesian methods; Markov Chain Monte Carlo methods; Forecasting; Great Recession |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; Cycles F - International Economics > F4 - Macroeconomic Aspects of International Trade and Finance > F47 - Forecasting and Simulation: Models and Applications |
Item ID: | 95707 |
Depositing User: | Romain Legrand |
Date Deposited: | 26 Aug 2019 11:23 |
Last Modified: | 15 Nov 2024 15:09 |
References: | Banbura, M., Giannone, D., and Reichlin, L. (2010). Nowcasting. Working Paper Series 1275, European Central Bank. Baumeister, C. and Benati, L. (2010). Unconventional monetary policy and the great recession: estimating the impact of a compression in the yield spread at the zero lower bound. Working Paper Series 1258, European Central Bank. Benati, L. and Lubik, T. (2014). The time-varying Beveridge curve. In van Gellecom, F. S., editor, Advances in Non-linear Economic Modeling. Dynamic Modeling and Econometrics in Economics and Finance, volume 17. Springer. Bijsterbosch, M. and Falagiarda, M. (2014). Credit supply dynamics and economic activity in euro area countries: a time-varying parameter VAR analysis. Working Paper Series 1714, European Central Bank. Boyd, S. and Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press. Canova, F. (1993). Modelling and forecasting exchange rates with a bayesian time-varying coefficient model. Journal of Economic Dynamics and Control, 17:233–261. Carriero, A., Clark, T., and Marcellino, M. (2015). Bayesian VARs: Specification choices and forecast accuracy. Journal of Applied Econometrics, 30(1):46–73. Carriero, A., Clark, T., and Marcellino, M. (2016). Large vector autoregressions with stochastic volatility and flexible priors. Working Paper 16-17, Federal Reserve Bank of Cleveland. Carter, C. and Kohn, R. (1994). On gibbs sampling for state space models. Biometrika, 81(3). Chan, J. (2013). Moving average stochastic volatility models with application to inflation forecast. Journal of Econometrics, 176:162–172. Chan, J. and Eisenstat, E. (2018). Bayesian model comparison for time-varying parameter VARs with stochastic volatility. Journal of applied econometrics. Chan, J. and Jeliazkov, I. (2009). Efficient simulation and integrated likelihood estimation in state space models. International Journal of Mathematical Modelling and Numerical Optimisa- tion, 1(1-2):101–120. Chib, S., Nardari, F., and Shephard, N. (2002). Markov chain monte carlo methods for stochastic volatility models. Journal of Econometrics, 108. Chib, S., Nardari, F., and Shephard, N. (2006). Analysis of high dimensional multivariate stochastic volatility models. Journal of Econometrics, 134(2):341–371. Chiu, C.-W., Mumtaz, H., and Pinter, G. (2015). Forecasting with VAR models: fat tails and stochastic volatility. Bank of England working papers 528, Bank of England. Ciccarelli, M. and Rebucci, A. (2002). The transmission mechanism of european monetary policy; is there heterogeneity? is it changing over time? IMF Working Papers 02/54, International Monetary Fund. Ciccarelli, M. and Rebucci, A. (2003). Measuring contagion with a bayesian, time-varying coef- ficient model. Working Paper Series 263, European Central Bank. Clark, T. and Ravazzolo, F. (2015). The macroeconomic forecasting performance of autore- gressive models with alternative specifications of time-varying volatility. Journal of applied econometrics, 30(4):551–575. Cogley, T. (2001). How fast can the new economy grow? a bayesian analysis of the evolution of trend growth. ASU Economics Working Paper 16/2001, ASU. Cogley, T. and Sargent, T. J. (2005). Drifts and volatilities: monetary policies and outcomes in the post wwii us. Review of Economic Dynamics, 8(2):262–302. Del Negro, M. and Primiceri, G. E. (2015). Time-varying structural vector autoregressions and monetary policy: a corrigendum. Review of Economic Studies, 82(4):1342–1345. Delle Monache, D. and Petrella, I. (2016). Adaptive models and heavy tails. Temi di Discussione 1052, Banca d’Italia. Doh, T. and Connolly, M. (2013). The state-space representation and estimation of a time- varying parameter VAR with stochastic volatility. In Zeng, Y. and Wu, S., editors, State-Space Models. Statistics and Econometrics for Finance, volume 1. Springer. Eisenstat, E., Chan, J., and Strachan, R. (2016). Stochastic model specification search for time-varying parameter VARs. Econometric Reviews, 35(8-10). Ellington, M., Florackis, C., and Milas, C. (2017). Liquidity shocks and real GDP growth: Evidence from a bayesian time-varying parameter VAR. Journal of International Money and Finance, 72:93–117. Fagan, G., Henry, J., and Mestre, R. (2001). An area-wide model (AWM) for the Euro Area. Working Paper Series 42, European Central Bank. Gambetti, L. and Musso, A. (2017). Loan supply shocks and the business cycle. Journal of Applied Econometrics, 32:764–782. Giannone, D., Lenza, M., and Primiceri, G. (2015). Prior selection for vector autoregressions. Review of Economics and Statistics, 97(2):436–451. Gneiting, T. and Raftery, A. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102(477):359–378. Gorgi, P., Koopman, S. J., and Schaumburg, J. (2017). Time-varying vector autoregressive models with structural dynamic factors. Working paper. Harvey, A., Ruiz, E., and Shephard, N. (1994). Multivariate stochastic variance models. Review of Economic Studies, 61(2):247–264. Jacquier, E., Polson, N., and Rossi, P. (1994). Bayesian analysis of stochastic volatility models. Journal of Business and Economic Statistics, 12:371–417. Jacquier, E., Polson, N., and Rossi, P. (1995). Models and priors for multivariate stochastic volatility. CIRANO Working Papers 95s-18, CIRANO. Jacquier, E., Polson, N., and Rossi, P. (2004). Bayesian analysis of stochastic volatility models with fat-tails and correlated errors. Journal of Econometrics, 122(1):185–212. Kalli, M. and Griffin, J. (2018). Bayesian nonparametric vector autoregressive models. Journal of econometrics, 203:267–282. Kapetanios, G., Marcellino, M., and Venditti, F. (2017). Large time-varying parameter VARs: a non-parametric approach. Temi di Discussione 1122, Banca d’Italia. Kim, S., Shephard, N., and Chib, S. (1998). Stochastic volatility: likelihood inference and comparison with ARCH models. Review of Economic Studies, 65:361–393. Koop, G. and Korobilis, D. (2010). Bayesian multivariate time series methods for empirical macroeconomics. Foundations and Trends in Econometrics, 3(4). Koop, G. and Korobilis, D. (2013). Large time-varying parameter VARs. Journal of Economet- rics, 177(2):185–198. Koop, G., Korobilis, D., and Pettenuzzo, D. (2018). Bayesian compressed vector autoregressions. Journal of Econometrics. Lubik, T. A. and Matthes, C. (2015). Time-varying parameter vector autoregressions: Specifi- cation, estimation, and an application. Economic Quarterly, 101(4):323–352. Mumtaz, H. and Zanetti, F. (2013). The impact of the volatility of monetary policy shocks. Journal of Money, Credit and Banking, 45(4):535–558. Nakajima, J. and West, M. (2015). Dynamic network signal processing using latent threshold models. Digital Signal Processing, 47. Petrova, K. (2018). A quasi-bayesian nonparametric approach to time-varying parameter VAR models. Journal of Econometrics. Prado, R. and West, M. (2010). Times Series: Modelling, Computation, and Inference. CRC Press. Primiceri, G. E. (2005). Time-varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72:821–852. Sims, C. (1980). Macroeconomics and reality. Econometrica, 48(1):1–48. Stock, J. and Watson, M. (1996). Evidence on structural instability in macroeconomic time-series relations. Journal of Business and Economic Statistics, 14(1):11–30. Stock, J. and Watson, M. (2012). Disentangling the channels of the 2007-2009 recession. Brook- ings Papers on Economic Activity, 1:81–135. Uhlig, H. (1997). Bayesian vector autoregressions with stochastic volatility. Econometrica, 65(1):59–74. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/95707 |
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Time-Varying Vector Autoregressions: Efficient Estimation, Random Inertia and Random Mean. (deposited 15 Sep 2018 11:07)
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