• Corpus ID: 239768938

Slow Movers in Panel Data

  title={Slow Movers in Panel Data},
  author={Yuya Sasaki and Takuya Ura},
Panel data often contain stayers (units with no within-variations) and slow movers (units with little within-variations). In the presence of many slow movers, conventional econometric methods can fail to work. We propose a novel method of robust inference for the average partial effects in correlated random coefficient models robustly across various distributions of within-variations, including the cases with many stayers and/or many slow movers in a unified manner. In addition to this… 

Figures and Tables from this paper


Nonparametric identification in panels using quantiles
This paper considers identification and estimation of ceteris paribus effects of continuous regressors in nonseparable panel models with time homogeneity. The effects of interest are derivatives of
Panel Data Models with Nonadditive Unobserved Heterogeneity: Estimation and Inference
This paper considers fixed effects estimation and inference in linear and nonlinear panel data models with random coefficients and endogenous regressors. The quantities of interest -- means,
Average and Quantile Effects in Nonseparable Panel Models
Nonseparable panel models are important in a variety of economic settings, including discrete choice. This paper gives identification and estimation results for nonseparable models under
Bayes Estimation of Short-run Coefficients in Dynamic Panel Data Models
This study is concerned with estimating the mean of the coefficients in a dynamic panel data model when the coefficients are assumed to be randomly distributed across cross- sectional units. The
Cross Section and Panel Data Estimators for Nonseparable Models with Endogenous Regressors
We propose two new methods for estimating models with nonseparable errors and endogenous regressors. The first method estimates a local average response. One estimates the response of the conditional
Identifying distributional characteristics in random coefficients panel data models
We study the identification of panel models with linear individual-specific coefficients, when T is fixed. We show identification of the variance of the effects under conditional uncorrelatedness.
Multivariate regression models for panel data
Identification and Estimation of Average Partial Effects in “Irregular” Correlated Random Coefficient Panel Data Models
In this paper we study identification and estimation of a correlated random coefficients (CRC) panel data model. The outcome of interest varies linearly with a vector of endogenous regressors. The
Analysis of Panel Data
Substantially revised from the second edition, this book includes two new chapters on modeling cross-sectionally dependent data and dynamic systems of equations.