J
Jeffrey M. Wooldridge
Researcher at Michigan State University
Publications - 168
Citations - 74861
Jeffrey M. Wooldridge is an academic researcher from Michigan State University. The author has contributed to research in topics: Estimator & Conditional expectation. The author has an hindex of 60, co-authored 159 publications receiving 70124 citations. Previous affiliations of Jeffrey M. Wooldridge include University of California, San Diego & Massachusetts Institute of Technology.
Papers
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Book
Econometric Analysis of Cross Section and Panel Data
TL;DR: This is the essential companion to Jeffrey Wooldridge's widely-used graduate text Econometric Analysis of Cross Section and Panel Data (MIT Press, 2001).
Book
Introductory Econometrics: A Modern Approach
TL;DR: In this article, the authors present a regression analysis with time series data using OLS asymptotics and a simple regression model in Matrix Algebra, which is based on the linear regression model.
Journal ArticleDOI
Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances
TL;DR: In this paper, the authors study the properties of the quasi-maximum likelihood estimator and related test statistics in dynamic models that jointly parameterize conditional means and conditional covariances, when a normal log-likelihood is maximized but the assumption of normality is violated.
Journal ArticleDOI
A Capital Asset Pricing Model with Time-varying Covariances
TL;DR: In this paper, a multivariate generalized autoregressive conditional heteroscedastic process is estimated for returns to bills, bonds, and stock where the expected return is proportional to the conditional convariance of each return with that of a fully diversified or market portfolio.
Posted Content
Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates
TL;DR: In this paper, simple quasi-likelihood methods for estimating regression models with a fractional dependent variable and for performing asymptotically valid inference are proposed, and they apply these methods to a data set of employee participation rates in 401(k) pension plans.