Evaluating Conditional Forecasts from Vector Autoregressions

Evaluating Conditional Forecasts from Vector Autoregressions

Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on or with methods for evaluating conditional forecasts. This paper provides analytical, Monte Carlo, and empirical evidence on tests of predictive ability for ...

Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections

This paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large models that can be cast in a linear state space representation. We build large vector autoregressions (VAR...

Stability of Mixtures of Vector Autoregressions with Autoregressive Conditional Heteroskedasticity

This paper gives necessary and sufficient conditions for stationarity and existence of second moments in mixtures of linear vector autoregressive models with autoregressive conditional heteroskedasticity. Sufficient conditions are also provided for a more general model in which the mixture components are permitted to exhibit limited forms of nonlinearity. When specialized to the corresponding n...

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*