Inference on co-integration parameters in heteroskedastic vector autoregressions

Bayesian Vector Autoregressions

This article provides an introduction to the burgeoning academic literature on Bayesian Vector Autoregressions, benchmark models for applied macroeconomic research. We first explain Bayes’ theorem and the derivation of the closed-form solution for the posterior distribution of the parameters of the model given data. We further consider parameter shrinkage, a distinguishing feature of the prior ...

Vector Autoregressions and Cointegration*

On the Identification of Structural Vector Autoregressions

F ollowing seminal work by Sims (1980a, 1980b), the economics profession has become increasingly concerned with studying sources of economic fluctuations. Sims’s use of vector autoregressions (VARs) made it possible to address both the relative importance and the dynamic effect of various shocks on macroeconomic variables. This type of empirical analysis has had at least two important consequen...

Lecture Notes on Structural Vector Autoregressions

• Macroeconomic background – Sims (1980) – Stock and Watson (1988) • Vector Autoregressions 1. Stationarity vs. nonstationarity 2. Structural models 3. Dynamic experiments 4. Estimation – Lütkepohl (1991), chapter 2 – Hamilton (1994), chapter 11 – Sims (1980) – Cooley and LeRoy (1985) – Runkle (1987) • Cointegration and Common Trends – Johansen and Juselius (1990) – King, Plosser, Stock, and Wa...

Prior Selection for Vector Autoregressions∗

Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-ofsample forecasts, particularly for models with many variables. A potential solution to this problem is to use informative priors, in order to shrink the richly param...