Estimation of linear dynamic panel data models with time-invariant regressors (working paper)
Kripfganz, S; Schwarz, C
Date: 25 August 2015
Working Paper
Publisher
European Central Bank
Publisher DOI
Abstract
We propose a two-stage estimation procedure to identify the effects of time-invariant regressors
in a dynamic version of the Hausman-Taylor model. We first estimate the coeffi-
cients of the time-varying regressors and subsequently regress the first-stage residuals on the
time-invariant regressors providing analytical standard error ...
We propose a two-stage estimation procedure to identify the effects of time-invariant regressors
in a dynamic version of the Hausman-Taylor model. We first estimate the coeffi-
cients of the time-varying regressors and subsequently regress the first-stage residuals on the
time-invariant regressors providing analytical standard error adjustments for the second-stage
coefficients. The two-stage approach is more robust against misspecification than GMM estimators
that obtain all parameter estimates simultaneously. In addition, it allows exploiting
advantages of estimators relying on transformations to eliminate the unit-specific heterogeneity.
We analytically demonstrate under which conditions the one-stage and two-stage GMM
estimators are equivalent. Monte Carlo results highlight the advantages of the two-stage approach
in finite samples. Finally, the approach is illustrated with the estimation of a dynamic
gravity equation for U.S. outward foreign direct investment.
Economics
Faculty of Environment, Science and Economy
Item views 0
Full item downloads 0