Generalized least squares inference in panel and multilevel models with serial correlation and fixed effects

By Christian Hansen. 2007

370 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/828ba7fc5ad573ef17e9f2f34f5c197165b76c34

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Statistics and Probability

Citations in the corpus

The Impact of Minimum Wages on Labour Market Transitions (2013, methodology): “the formula for bias in the AR parameters in a short panel presented in Hansen (2007) implies very little bias in our case (on the order of 0.”
Cross-Validation for Correlated Data (2019, background): “Under some conditions, a predictor that uses the estimated random effect realizations is the best linear unbiased predictor (BLUP), for more information see Harville (1976).”
Inferring causal impact using Bayesian structural time-series models (2015, methodology): “When ﬁt to serially correlated data, static models yield overoptimistic inferences with too narrow uncertainty intervals [see also Bertrand, Duﬂo and Mullainathan (2002), Hansen (2007a, 2007b), Solon (1984)].”
When Should You Adjust Standard Errors for Clustering? (2017, neutral): “Typically the stated motivation is that unobserved components of outcomes for units within clusters are correlated (Moulton [1986, 1990], Moulton and Randolph [1989], Kloek [1981], Hansen [2007], Cameron and Miller [2015]).”
Feasible generalized least squares for panel data with cross-sectional and serial correlations (2019, methodology): “Hansen (2007a) studied an asymptotic theory that are robust to the heteroskedasticity and serial correlation in the case of large-N large-T .”
Universal Child Care, Maternal Labor Supply, and Family Well‐Being (2008, positive): “Finally, we implement the method of Hansen (2007): feasible generalized least squares (FGLS) assuming an AR(1) specification of the errors.”
Inference with Difference-in-Differences Revisited (2013, methodology): “Hansen (2007) derives a bias correction which is consistent as G → ∞, and develops the asymptotic properties of a FGLS estimator which uses it. But this correction may not work well with small G. Second, one may be worried about misspecification of the error process. However, neither of these issues”
A Practitioner’s Guide to Cluster-Robust Inference (2015, methodology): “Hansen (2007b) in simulations shows considerable effi ciency gains in bias- corrected FGLS compared to OLS.”
Alternative diff-in-diffs estimators with several pretreatment periods (2019, methodology): “…cross-sectional correlation within groups as well as correlations over time of the units in the di erent groups (in panel data applications) has been shown to substantially a ect the validity of traditional inference (Moulton, 1990; Bertrand et al., 2004; Donald and Lang, 2007; Hansen, 2007a,b).”
Institutional development and bank stability: Evidence from transition countries (2014, methodology): “Given that the reforms in transition countries took place at different time periods in different countries, we apply the DID approach in a multiple groups and multiple time periods framework (Bertrand and Mullainathan, 2003; Hansen, 2007; Haselmann et al., 2010).”