1,721,183 research outputs found
Replication data for: Prior Selection for Vector Autoregressions
No full textGiannone, Domenico, Lenza, Michele, and Primiceri, Giorgio E., (2015) "Prior Selection for Vector Autoregressions." Review of Economics and Statistics 97:2, 436-451
Replication data for: Prior Selection for Vector Autoregressions
No full textGiannone, Domenico, Lenza, Michele, and Primiceri, Giorgio E., (2015) "Prior Selection for Vector Autoregressions." Review of Economics and Statistics 97:2, 436-451
Does information help recovering structural shocks from past observations?
No full textinfo:eu-repo/semantics/publishe
Macroeconomic forecasting and structural change
No full textThe aim of this paper is to assess whether modeling structural change can help improving the accuracy of macroeconomic forecasts. We conduct a simulated real-time out-of-sample exercise using a time-varying coefficients vector autoregression (VAR) with stochastic volatility to predict the inflation rate, unemployment rate and interest rate in the USA. The model generates accurate predictions for the three variables. In particular, the forecasts of inflation are much more accurate than those obtained with any other competing model, including fixed coefficients VARs, time-varying autoregressions and the naïve random walk model. The results hold true also after the mid 1980s, a period in which forecasting inflation was particularly hard. © 2011 John Wiley & Sons, Ltd.SCOPUS: ar.jFLWINinfo:eu-repo/semantics/publishe
Comparing Alternative Predictors Based on Large-Panel Factor Models
No full textThis article compares the predictive ability of the factor models of Stock and Watson (2002a) and Forni, Hallin, Lippi and Reichlin (2005) using a 'large' panel of macroeconomic variables of the United States. We propose a nesting procedure of comparison that clarifies and partially overturns the results of similar exercises in the literature. Our main conclusion is that with the dataset at hand the two methods have a similar performance and produce highly collinear forecasts. © 2011 Blackwell Publishing Ltd and the Department of Economics, University of Oxford.SCOPUS: ar.jFLWINinfo:eu-repo/semantics/publishe
VARs, common factors and the empirical validation of equilibrium business cycle models
No full textinfo:eu-repo/semantics/publishe
Large Bayesian vector auto regressions
No full textThis paper shows that vector auto regression (VAR) with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results of De Mol and co-workers (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting performance of small monetary VARs can be improved by adding additional macroeconomic variables and sectoral information. In addition, we show that large VARs with shrinkage produce credible impulse responses and are suitable for structural analysis. © 2009 John Wiley & Sons, Ltd.SCOPUS: ar.jFLWINinfo:eu-repo/semantics/publishe
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