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on Econometric Time Series |
| By: | Dimitris Korobilis; Kenichi Shimizu |
| Abstract: | In all areas of human knowledge, datasets are increasing in both size and complexity, creating the need for richer statistical models. This trend is also true for economic data, where high-dimensional and nonlinear/nonparametric inference is the norm in several fields of applied econometric work. The purpose of this paper is to introduce the reader to the world of Bayesian model determination, by surveying modern shrinkage and variable selection algorithms and methodologies. Bayesian inference is a natural probabilistic framework for quantifying uncertainty and learning about model parameters, and this feature is particularly important for inference in modern models of high dimensions and increased complexity. We begin with a linear regression setting in order to introduce various classes of priors that lead to shrinkage/sparse estimators of comparable value to popular penalized likelihood estimators (e.g. ridge, lasso). We explore various methods of exact and approximate inference, and discuss their pros and cons. Finally, we explore how priors developed for the simple regression setting can be extended in a straightforward way to various classes of interesting econometric models. In particular, the following case-studies are considered, that demonstrate application of Bayesian shrinkage and variable selection strategies to popular econometric contexts: i) vector autoregressive models; ii) factor models; iii) time-varying parameter regressions; iv) confounder selection in treatment effects models; and v) quantile regression models. A MATLAB package and an accompanying technical manual allow the reader to replicate many of the algorithms described in this review. |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:gla:glaewp:2021_19&r= |
| By: | JIN SEO CHO (Yonsei Univ); MENG HUANG (PNC); HALBERT WHITE (University of California) |
| Abstract: | In this paper, we study functional ordinary least squares estimator and its properties in testing the hypothesis of a constant zero mean function or an unknown constant non-zero mean function. We exploit the recent work by Cho, Phillips, and Seo (2021) and show that the associated Wald test statistics have standard chi-square limiting null distributions, standard non-central chi-square distributions for local alternatives converging to zero at a √n rate, and are consistent against global alternatives. These properties permit computationally convenient tests of hypotheses involving nuisance parameters. In particular, we develop new alternatives to tests for regression misspecification, that involves nuisance parameters identified only under the alternative. In Monte Carlo studies, we find that our tests have well behaved levels. We also find that functional ordinary least squares tests can have power better than existing methods that do not exploit this covariance structure, like the specification testing procedures of Bierens (1982, 1990) or Stinchcombe and White(1998). Finally, we apply our methodology to the probit models for voting turnout that are estimated by Wolfinger and Resenstone (1980) and Nagler (1991) and test whether the models are correctly specified or not. |
| Keywords: | Davies Test; Functional Data; Misspecification; Nuisance Parameters; Wald Test; Voting Turnout. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:yon:wpaper:2021rwp-190&r= |
| By: | Kilian, Lutz |
| Abstract: | A series of recent articles has called into question the validity of VAR models of the global market for crude oil. These studies seek to replace existing oil market models by structural VAR models of their own based on different data, different identifying assumptions, and a different econometric approach. Their main aim has been to revise the consensus in the literature that oil demand shocks are a more important determinant of oil price fluctuations than oil supply shocks. Substantial progress has been made in recent years in sorting out the pros and cons of the underlying econometric methodologies and data in this debate, and in separating claims that are supported by empirical evidence from claims that are not. The purpose of this paper is to take stock of the VAR literature on global oil markets and to synthesize what we have learned. Combining this evidence with new data and analysis, I make the case that the concerns regarding the existing VAR oil market literature have been overstated and that the results from these models are quite robust to changes in the model specification. |
| Keywords: | Elasticity,structural VAR,Bayesian inference,oil price,global real activity,oil inventories |
| JEL: | Q43 Q41 C36 C52 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfswop:661&r= |