Koop, Gary and Korobilis, Dimitris (2012): Large time-varying parameter VARs.
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In this paper we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.
|Item Type:||MPRA Paper|
|Original Title:||Large time-varying parameter VARs|
|Keywords:||Bayesian VAR; forecasting; time-varying coefficients; state-space model|
|Subjects:||E - Macroeconomics and Monetary Economics > E2 - Macroeconomics: Consumption, Saving, Production, Employment, and Investment > E27 - Forecasting and Simulation: Models and Applications
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection
E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General
|Depositing User:||Dimitris Korobilis|
|Date Deposited:||06. May 2012 00:50|
|Last Modified:||12. Feb 2013 22:12|
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