A NEW PANEL DATA TREATMENT FOR HETEROGENEITY IN TIME TRENDS

Aloïs Kneip · Robin C. Sickles · Wonho Song

114 citations0/0 files storedEconometric Theory

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Spatial and Panel Data Analysis Efficiency Analysis Using DEA Statistical Methods and Bayesian Inference

Abstract

This paper introduces a new estimation method for arbitrary temporal heterogeneity in panel data models. The paper provides a semiparametric method for estimating general patterns of cross-sectional specific time trends. The methods proposed in the paper are related to principal component analysis and estimate the time-varying trend effects using a small number of common functions calculated from the data. An important application for the new estimator is in the estimation of time-varying technical efficiency considered in the stochastic frontier literature. Finite sample performance of the estimators is examined via Monte Carlo simulations. We apply our methods to the analysis of productivity trends in the U.S. banking industry.

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References

2021Econometric Analysis of Panel Data 1977Formulation and estimation of stochastic frontier production function models 1977Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error 1982Trends and random walks in macroeconmic time series 2002Determining the Number of Factors in Approximate Factor Models 2002Forecasting Using Principal Components From a Large Number of Predictors 1992Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India 1999Unit Roots, Cointegration, and Structural Change

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