Abstract
The paper compares the power of two tests for serial correlation in regression models with lagged dependent variables, recently suggested by Durbin, with that of the likelihood ratio test by means of two sets of Monte-Carlo experiments-one in which the exogenous series is taken to be the quarterly GNP series for the USA and the other in which the exogenous series is generated by a known autoregression.
Keywords
Related Publications
Estimating Autocorrelations in Fixed-Effects Models
This paper discusses the estimation of serial correlation in fixed effects models for longitudinal data. Like time series data, longitudinal data often contain serially correlat...
MCMC Methods for Multi-Response Generalized Linear Mixed Models: The<b>MCMCglmm</b><i>R</i>Package
Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in clo...
How Much Should We Trust Differences-In-Differences Estimates?
Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors...
Introduction to Econometrics
Foreword. Preface to the Second Edition. Preface to the Third Edition. Obituary. INTRODUCTION AND THE LINEAR REGRESSION MODEL. What is Econometrics? Statistical Background and M...
CODA: convergence diagnosis and output analysis for MCMC
[1st paragraph] At first sight, Bayesian inference with Markov Chain Monte Carlo (MCMC) appears to be straightforward. The user defines a full probability model, perhaps using o...
Publication Info
- Year
- 1973
- Type
- article
- Volume
- 41
- Issue
- 4
- Pages
- 761-761
- Citations
- 98
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.2307/1914095