Abstract
We examine some issues in the estimation of time-series cross-section models, calling into question the conclusions of many published studies, particularly in the field of comparative political economy. We show that the generalized least squares approach of Parks produces standard errors that lead to extreme overconfidence, often underestimating variability by 50% or more. We also provide an alternative estimator of the standard errors that is correct when the error structures show complications found in this type of model. Monte Carlo analysis shows that these “panel-corrected standard errors” perform well. The utility of our approach is demonstrated via a reanalysis of one “social democratic corporatist” model.
Keywords
EstimatorEconometricsSeries (stratigraphy)Standard errorSection (typography)Overconfidence effectField (mathematics)Monte Carlo methodPanel dataEconomicsComputer scienceStatisticsMathematicsPsychologySocial psychology
Affiliated Institutions
Related Publications
Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Models
In a previous article we showed that ordinary least squares with panel corrected standard errors is superior to the Parks generalized least squares approach to the estimation of...
Dynamic panel estimation and homogeneity testing under cross section dependence *
This paper deals with cross section dependence, homogeneity restrictions and small sample bias issues in dynamic panel regressions. To address the bias problem we develop a pane...
IV Estimation with Heteroskedasticity and Many Instruments
It is common practice in econometrics to correct for heteroskedas- ticity. This paper does this for instrumental variables estimation with many in- struments. We give a heterosk...
Government Partisanship, Labor Organization, and Macroeconomic Performance: A Corrigendum
Alvarez, Garrett and Lange (1991) used cross-national panel data on the Organization for Economic Coordination and Development nations to show that countries with left governmen...
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...
Publication Info
- Year
- 1995
- Type
- article
- Volume
- 89
- Issue
- 3
- Pages
- 634-647
- Citations
- 6440
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
6440
OpenAlex
Cite This
Nathaniel Beck,
Jonathan N. Katz
(1995).
What To Do (and Not to Do) with Time-Series Cross-Section Data.
American Political Science Review
, 89
(3)
, 634-647.
https://doi.org/10.2307/2082979
Identifiers
- DOI
- 10.2307/2082979