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 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > 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|
Banbura, M., Giannone, D. and Reichlin, L. (2010). "Large Bayesian vector auto regressions," Journal of Applied Econometrics, 25, 71-92.
Brockwell, R. and Davis, P. (2009). Time series: Theory and methods (second edition). New York: Springer.
Carriero, A., Clark, T. and Marcellino, M. (2011). "Bayesian VARs: Specification choices and forecast accuracy," Federal Reserve Bank of Cleveland, working paper 11-12.
Carriero, A., Kapetanios, G. and Marcellino, M. (2009). "Forecasting exchange rates with a large Bayesian VAR," International Journal of Forecasting, 25, 400-417.
Chan, J., Koop, G., Leon-Gonzalez, R. and Strachan, R. (2012)."Time varying dimension models," Journal of Business and Economic Statistics, forthcoming.
Cogley, T., Morozov, S. and Sargent, T. (2005). "Bayesian fan charts for U.K. inflation: Forecasting and sources of uncertainty in an evolving monetary system," Journal of Economic Dynamics and Control, 29, 1893-1925.
Cogley, T. and Sargent, T. (2001). "Evolving post World War II inflation dynamics," NBER Macroeconomics Annual, 16, 331-373.
Cogley, T. and Sargent, T. (2005). "Drifts and volatilities: Monetary policies and outcomes in the post WWII U.S.," Review of Economic Dynamics, 8, 262-302.
D'Agostino, A., Gambetti, L. and Giannone, D. (2009). "Macroeconomic forecasting and structural change," Journal of Applied Econometrics, forthcoming.
Dangl, T. and Halling, M. (2012). "Predictive regressions with time varying coefficients," Journal of Financial Economics, forthcoming.
Doan, T., Litterman, R. and Sims, C. (1984). "Forecasting and conditional projections using a realistic prior distribution", Econometric Reviews, 3, 1-100.
Fagin, S. (1964). "Recursive linear regression theory, optimal filter theory, and error analyses of optimal systems," IEEE International Convention Record Part i, pages 216-240.
Fruhwirth-Schnatter, S., (2006). Finite Mixture and Markov Switching Models. New York: Springer.
Giannone, D., Lenza, M., Momferatou, D. and Onorante, L. (2010). "Short-term inflation projections: a Bayesian vector autoregressive approach," ECARES working paper 2010-011, Universite Libre de Bruxelles.
Giannone, D., Lenza, M. and Primiceri, G. (2012). "Prior selection for vector autoregressions," Centre for Economic Policy Research, working paper 8755.
Jazwinsky, A. (1970). Stochastic Processes and Filtering Theory. New York: Academic Pres.
Koop, G. (2011). "Forecasting with medium and large Bayesian VARs," Journal of Applied Econometrics, forthcoming.
Koop, G. and Korobilis, D. (2009). "Bayesian multivariate time series methods for empirical macroeconomics," Foundations and Trends in Econometrics, 3, 267-358.
Koop, G. and Korobilis, D. (2011). "Forecasting inflation using dynamic model averaging," International Economic Review, forthcoming.
Koop, G., Leon-Gonzalez, R. and Strachan, R. (2009). "On the evolution of the monetary policy transmission mechanism," Journal of Economic Dynamics and Control, 33, 997-1017.
Korobilis, D. (2012). "VAR forecasting using Bayesian variable selection", Journal of Applied Econometrics, forthcoming.
Marcellino, M., Stock, J. and Watson, M. (2006). "A comparison of direct and iterated AR methods for forecasting macroeconomic series h-steps ahead," Journal of Econometrics, 135, 499-526.
McCormick, T., Raftery, A. Madigan, D. and Burd, R. (2011). "Dynamic logistic regression and dynamic model averaging for binary classification," Biometrics, forthcoming.
Park, D., Jun, B. and Kim, J. (1991). "Fast tracking RLS algorithm using novel variable forgetting factor with unity zone," Electronics Letters, 27, 2150-2151.
Primiceri, G. (2005). "Time varying structural vector autoregressions and monetary policy," Review of Economic Studies, 72, 821-852.
Raftery, A., Karny, M. and Ettler, P. (2010). "Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill," Technometrics, 52, 52-66.
RiskMetrics (1996). Technical Document (Fourth Edition). Available at http://www. riskmetrics.com/system/files/private/td4e.pdf.
Stock, J. and Watson, M. (2008). "Forecasting in dynamic factor models subject to structural instability," in The Methodology and Practice of Econometrics, A Festschrift in Honour of Professor David F. Hendry, edited by J. Castle and N. Shephard, Oxford: Oxford University Press.
West, M. and Harrison, J. (1997). Bayesian Forecasting and Dynamic Models, second edition, New York: Springer.