The Department of Economics Econometrics Seminar Series presents
"Nonparametric Vector Autoregressions: Specification, Estimation, and Inference"
with Ivan Jeliazkov, Associate Professor of Economics, UCI
May 13, 2013
Social Science Plaza B, Room 3218
For a number of decades vector autoregressions have played a central role in empirical macroeconomics. These models are general, can capture sophisticated dynamic behavior and have recently been adapted to capture additional features such as structural instability, time-varying parameters, dynamic factors, threshold-crossing behavior, and discrete outcomes. Building upon growing evidence that the assumption of linearity may be undesirable in modeling certain macroeconomic relationships, this talk seeks to add to recent advances in VAR modeling by proposing a nonparametric dynamic model for multivariate time series. In this model, the problems of modeling and estimation are approached from a hierarchical Bayesian perspective. The talk considers the issues of identification, estimation, and model comparison, enabling nonparametric VAR models to be fit efficiently by Markov chain Monte Carlo algorithms and compared to parametric and semiparametric alternatives by marginal likelihoods and Bayes factors. Among other benefits, the methodology allows for a more careful study of structural instability while guarding against the possibility of unaccounted nonlinearity in otherwise stable economic relationships. Extensions of the proposed nonparametric model to settings with important other modeling features are also considered. The techniques are employed to study the post-war US economy, supporting the contention that certain nonlinear relationships in the data can remain undetected by standard models and may, instead, be erroneously interpreted as evidence of structural instability.
For further information, please contact Gloria Simpson, email@example.com or 949-824-5788.