Maximum-Likelihood Based Inference in the Two-Way Random Effects Model with Serially Correlated Time Effects

Sune Karlsson (sune.karlsson@oru.se) and Jimmy Skoglund
Additional contact information
Jimmy Skoglund: Stockholm School of Economics

No 1178, Econometric Society World Congress 2000 Contributed Papers from Econometric Society

Abstract: This paper considers maximum likelihood estimation and inference in the two-way random effects model with serial correlation. We derive a straightforward maximum likelihood estimator when the time-specific component follow an AR(1) or MA(1) process. The estimator is easily generalized to arbitrary stationary and strictly invertible ARMA processes. Furthermore we derive tests of the null hypothesis of no serial correlation as well as tests for discriminating between the AR(1) and MA(1) specifications. A Monte-Carlo experiment evaluates the finite-sample properties of the estimators and test-statistics

Date: 2000-08-01
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://fmwww.bc.edu/RePEc/es2000/1178.pdf main text (application/pdf)

Related works:
Journal Article: Maximum-likelihood based inference in the two-way random effects model with serially correlated time effects (2004)
Working Paper: Maximum-likelihood based inference in the two-way random effects model with serially correlated time effects (2000)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ecm:wc2000:1178

Access Statistics for this paper

More papers in Econometric Society World Congress 2000 Contributed Papers from Econometric Society Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum (baum@bc.edu).