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Econometrics

Paper Session

Saturday, Jan. 4, 2025 2:30 PM - 4:30 PM (PST)

Hilton San Francisco Union Square, Union Square 10
Hosted By: American Economic Association
  • Chair: Andrew Foerster, Federal Reserve Bank of San Francisco

Identifying Random Effects Groups in Panel Data: Cluster Analysis Approach

Sanghoon Kim
,
SUNY-Buffalo

Abstract

In panel data regressions, researchers define random effects groups based on their intuitions or the contexts of research topics, such as at the individual level, at different calendar times, or both. However, misclassified groups by researchers lead to inefficient estimates. This paper proposes a method that employs cluster analysis, an unsupervised machine learning technique, to address the problem. The clustering algorithm proposed in this paper identifies random effects groups by learning from data rather than by researchers. The method can assist researchers in defining random effects groups and thus improve the efficiency of estimates. Monte-Carlo evidence is provided.

Testing for Instrument Validity with Higher-Order Cumulants

Aaron Pancost
,
University of Texas-Austin
Garrett Schaller
,
Colorado State University

Abstract

Instrumental variables estimators are commonly used in economics and finance to
establish causal relationships. Although instruments that fail the exclusion restriction
do not reliably estimate parameters of interest, testing the exclusion restriction is
uncommon, due to the difficulty of finding multiple valid instruments. We derive
closed-form instrumental variable estimators that allow for tests of over-identifying
restrictions even for the case of a single valid instrument. We also derive estimators
that are consistent when instruments and regressors are mis-measured with correlated
errors. Monte Carlo simulations suggest that our estimators have power to reject even
in relatively small samples. We also apply our estimators to the IV regressions of Mian
and Sufi (2014) and cannot reject the null hypothesis that the exclusion restriction
holds.

Trimmed Mean Group Estimation of Average Treatment E ects in Ultra Short T Panels under Correlated Heterogeneity

M. Hashem Pesaran
,
University of Southern California, University of Cambridge, and Trinity College
Liying Yang
,
University of British Columbia

Abstract

Under correlated heterogeneity, the commonly used two-way  fixed effects estimator
is biased and can lead to misleading inference. This paper proposes a new trimmed mean
group (TMG) estimator which is consistent at the irregular rate of n^{1/3} even if the time
dimension of the panel is as small as the number of its regressors. Extensions to panels
with time effects are provided, and a Hausman-type test of correlated heterogeneity
is proposed. Small sample properties of the TMG estimator (with and without time
effects) are investigated by Monte Carlo experiments and shown to be satisfactory and
perform better than other trimmed estimators proposed in the literature. The proposed
test of correlated heterogeneity is also shown to have the correct size and satisfactory
power. The utility of the TMG approach is illustrated with an empirical application.
JEL Classifications
  • C0 - General