Bayesian Multivariate Time Series Methods for Empirical Macroeconomics

By Gary Koop, Dimitris Korobilis. 2010

779 citations recorded by the source corpus.

Macroeconomic practitioners frequently work with multivariate time series models such as VARs, factor augmented VARs as well as time-varying parameter versions of these models (including variants with multivariate stochastic volatility). These models have a large number of parameters and, thus, over-parameterization problems may arise. Bayesian methods have become increasingly popular as a way of overcoming these problems. In this monograph, we discuss VARs, factor augmented VARs and time-varying parameter extensions and show how Bayesian inference proceeds. Apart from the simplest of VARs, Bayesian inference requires the use of Markov chain Monte Carlo methods developed for state space models and we describe these algorithms. The focus is on the empirical macroeconomist and we offer advice on how to use these models and methods in practice and include empirical illustrations. A website provides Matlab code for carrying out Bayesian inference in these models.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/24087ed149e49a973879fada8de995832e588b5f

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In Economics and Econometrics

Citations in the corpus

Shocking language: understanding the macroeconomic effects of central bank communication (2016, neutral): “Given that the factors we extract are potentially subject to sampling error (Koop and Korobilis, 2010), consistent estimation of (8) is not guaranteed.”
Prior Selection for Vector Autoregressions (2015, neutral): “In the context of VARs, for example, Litterman (1980) and Doan, Litterman, and Sims (1984) set the tightness of the prior by maximizing the out-of-sample forecasting performance of the model. Bańbura, Giannone, and Reichlin (2010) propose instead to control for over-fitting by choosing the shrinkage”
A New Model of Inflation, Trend Inflation, and Long-Run Inflation Expectations (2015, background): “See also Koop and Korobilis (2010), p. 308 310, for a textbook treatment.”
Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications (2011, neutral): “, Leeper et al. (1996), Christiano et al.”
Forecasting with Bayesian Vector Autoregression (2013, positive): “The widely used Minnesota prior introduced by Litterman (1979) is a set of data centric prior beliefs that shrinks the parameters towards a stylized representation of macroeconomic data thereby reducing parameter uncertainty and improving forecast accuracy.”
On the effects of world stock market and oil price shocks on food prices: An empirical investigation based on TVP-VAR models with stochastic volatility (2014, methodology): “For a discussion about the different methods for the settlement of the priors, we refer to the work by Koop and Korobilis (2010).”
Bayesian nonparametric vector autoregressive models (2018, neutral): “See Koop and Korobilis (2010) for a recent review 3 of these methods.”
Identifying demand effects in a large network of product categories (2015, methodology): “diagonal, and estimated by Σ̂ii = σ̂ 2 i with σ̂ 2 i the standard OLS estimate of the error variance in an AR(p) model for the i time series (Koop and Korobilis, 2009).”
Large time-varying parameter VARs (2013, methodology): “…, a prior for the initial conditions (i.e. β0 and possibly Σ0 and Q0) and a prior for any remaining parameters of the model, then Bayesian statistical inference can proceed in a straightforward fashion (see, for instance, Koop and Korobilis (2009) for a textbook-level treatment) using MCMC methods.”
Forecasting the European carbon market (2013, methodology): “Standard methods for estimation and prediction exist with such models (see Koop and Korobilis (2009) for a textbook level Bayesian treatment of TVP models).”
Oil price shocks and uncertainty: How stable is their relationship over time? (2018, methodology): “Thus, we follow Nakajima (2011) and compute the impulse responses by fixing an initial shock size equal to the average of stochastic volatility over the sample period and employing the simultaneous relations at each point in time.”
Time-Varying Effect of Oil Market Shocks on the Stock Market (2015, positive): “5 See the detailed MCMC algorithm in the technical appendix of Primiceri (2005) and Koop et al. (2009). Recently, Del Negro and Primiceri (2014) have made a correction to the MCMC procedures in Primiceri (2005).”