Journal ArticleDOI
Large Bayesian vector auto regressions
TLDR
In this article, the authors show that vector auto regression with Bayesian shrinkage is an appropriate tool for large dynamic models and that large VARs with shrinkage produce credible impulse responses and are suitable for structural analysis.Abstract:
This 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.read more
Citations
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ReportDOI
US Monetary Policy and the Global Financial Cycle
TL;DR: In this paper, a single global factor that explains an important share of the variation of risky asset prices around the world decreases significantly after a US monetary tightening, leading to significant deleveraging of global financial intermediaries, a decline in the provision of domestic credit globally, strong retrenchments of international credit flows, and tightening of foreign financial conditions.
Journal ArticleDOI
Forecasting with Medium and Large Bayesian VARs
TL;DR: In this article, a range of alternative priors have been used with small VARs, and the issues which arise when they are used with medium and large VAR and examine their forecast performance using a US macroeconomic dataset containing 168 variables.
Journal ArticleDOI
Large time-varying parameter VARs
Gary Koop,Dimitris Korobilis +1 more
TL;DR: To overcome computational constraints with likelihood-based estimation of large systems, Kalman filter estimation with forgetting factors is relied on and ideas from the dynamic model averaging literature are drawn and the TVP-VAR is extended so that its dimension can change over time.
Journal ArticleDOI
Does the Fed Respond to Oil Price Shocks
Lutz Kilian,Logan T. Lewis +1 more
TL;DR: The authors show that there is no credible evidence that monetary policy responses to oil price shocks caused large aggregate fluctuations in the 1970s and 1980s or more recently, and they suggest that the traditional monetary policy reaction framework should be replaced by models that take account of the endogeneity of the real price of oil and that allow policy response to depend on the underlying causes of oil price shock.
Journal ArticleDOI
Regularized estimation in sparse high-dimensional time series models
Sumanta Basu,George Michailidis +1 more
TL;DR: In this article, a measure of stability for stable Gaussian processes using their spectral properties is introduced, which provides insight into the effect of dependence on the accuracy of the regularized estimates.
References
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Journal ArticleDOI
Forecasting Using Principal Components From a Large Number of Predictors
James H. Stock,Mark W. Watson +1 more
TL;DR: In this paper, the authors consider forecasting a single time series when there are many predictors (N) and time series observations (T), and they show that the difference between the feasible forecasts and the infeasible forecasts constructed using the actual values of the factors converges in probability to 0 as both N and T grow large.
Posted Content
Monetary Policy Shocks: What Have We Learned and to What End?
TL;DR: The authors reviewed recent research that grapples with the question: What happens after an exogenous shock to monetary policy? They argue that this question is interesting because it lies at the center of a particular approach to assessing the empirical plausibility of structural economic models that can be used to think about systematic changes in monetary policy institutions and rules.
Journal ArticleDOI
Macroeconomic Forecasting Using Diffusion Indexes
James H. Stock,Mark W. Watson +1 more
TL;DR: This paper used principal component analysis (PCA) to predict macroeconomic time series variable using a large number of predictors, and the predictors were summarized using a small number of indexes constructed by principal component analyzer.
Journal ArticleDOI
The Generalized Dynamic-Factor Model: Identification and Estimation
TL;DR: In this article, a generalized dynamic factor model with infinite dynamics and nonorthogonal idiosyncratic components is proposed, which generalizes the static approximate factor model of Chamberlain and Rothschild (1983), as well as the exact factor model a la Sargent and Sims (1977).
ReportDOI
Forecasting and conditional projection using realistic prior distributions
TL;DR: This paper developed a forecasting procedure based on a Bayesian method for estimating vector autoregressions, which is applied to 10 macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations.