Data-based priors for vector autoregressions with drifting coefficients

Korobilis, Dimitris (2014): Data-based priors for vector autoregressions with drifting coefficients.

Abstract

This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require minimal input by the user, and they result in shrinkage posterior representations, thus, making them appropriate for models of large dimensions. A comprehensive forecasting exercise involving TVP-VARs of different dimensions establishes the usefulness of the proposed approach.

• Item Type: MPRA Paper
• Language: English
• Keywords: TVP-VAR, shrinkage, data-based prior, forecasting
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling; E - Macroeconomics and Monetary Economics > E1 - General Aggregative Models > E17 - Forecasting and Simulation: Models and Applications; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E58 - Central Banks and Their Policies

References

Belmonte, M. A. G., Koop, G. and Korobilis, D. (2014). Hierarchical shrinkage in time-varying parameter models. Journal of Forecasting, forthcoming.

Bhattacharya, A. and Dunson, D. B. (2011). Sparse Bayesian infinite factor models. Biometrika 98, pp. 291-306.

Bouriga, M. and Féron, O. (2013). Estimation of covariance matrices based on hierarchical inverse-Wishart priors. Journal of Statistical Planning and Inference 143, pp. 795-808.

Chen, Z. and Dunson, D. (2003). Random effects selection in linear mixed models. Biometrics 59 (4), pp. 762-769.

Clark, T. and Ravazzolo, F. (forthcoming). The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility. Journal of Applied Econometrics.

Cooley, T. F. (1971). Estimation in the presence of sequential parameter variation. Ph.D Thesis. Department of Economics, University of Pennsylvania.

D'Agostino, A., Gambetti, L. and Giannone, D. (2013). Macroeconomic forecasting and structural change. Journal of Applied Econometrics 28, pp. 82-101.

Doan, T., Litterman, R. and Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3, pp. 1-100.

Eisenstat, E., Chan, J. C. C. and Strachan, R. (2013). Stochastic model specification search for time-varying parameter VARs. manuscript.

Frühwirth-Schnatter, S. and Wagner, H. (2010). Stochastic model specification search for Gaussian and partial non-Gaussian state space models. Journal of Econometrics 154, pp. 85--100.

Giannone, D., Lenza, M. and Primiceri, G. E. (2012). Prior selection for vector autoregressions. Working Paper Series 1494, European Central Bank.

Groen, J. J. J., Paap, R. and Ravazzolo, F. (2012). Real-time inflation forecasting in a changing world. Journal of Business and Economic Statistics, 31, pp. 29-44.

Koop, G. and Korobilis, D. (2010). Bayesian multivariate time series methods for empirical macroeconomics. Foundations and Trends in Econometrics 3, pp. 267-358.

Koop, G. and Korobilis, D. (2013). Large Time-Varying Parameter VARs. Journal of Econometrics 177, pp. 185-198.

Korobilis, D. (2013a). Bayesian forecasting with highly correlated predictors. Economics Letters 118, pp. 148-150.

Korobilis, D. (2013b). Hierarchical shrinkage priors for dynamic regressions with many predictors. International Journal of Forecasting 29, pp. 43-59.

Korobilis, D. (2013c). VAR forecasting using Bayesian variable selection. Journal of Applied Econometrics 28, pp. 204-230.

Kulhavý, R. and Zarrop, M. B. (1993). On a general concept of forgetting. International Journal of Control 58, pp. 905-924.

Litterman, R. (1979). Techniques of forecasting using vector autoregressions. Federal Reserve Bank of Minneapolis Working Paper 115.

Sims, C. (1989). A nine variable probabilistic macroeconomic forecasting model. Federal Reserve Bank of Minneapolis Discussion paper no. 14.

Stein, C. (1975). Estimation of a covariance matrix. In: Rietz Lecture, 39th Annual Meeting IMS. Atlanta, Georgia.