Two tests for strict exogeneity in a correlated random effects panel data Tobit model

2014 ◽  
Vol 69 (2) ◽  
pp. 115-125
Author(s):  
Adriaan Kalwij
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Tobit Model ◽  
Correlated Random Effects

scholarly journals Forecasting With Dynamic Panel Data Models

Econometrica ◽  
2020 ◽  
Vol 88 (1) ◽  
pp. 171-201 ◽  
Author(s):  
Laura Liu ◽  
Hyungsik Roger Moon ◽  
Frank Schorfheide
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Empirical Bayes ◽  
Population Distribution ◽  
Monte Carlo Study ◽  
Dynamic Panel Data ◽  
Random Coefficients ◽  
Cross Sectional ◽  
Posterior Mean ◽  
Correlated Random Effects

This paper considers the problem of forecasting a collection of short time series using cross‐sectional information in panel data. We construct point predictors using Tweedie's formula for the posterior mean of heterogeneous coefficients under a correlated random effects distribution. This formula utilizes cross‐sectional information to transform the unit‐specific (quasi) maximum likelihood estimator into an approximation of the posterior mean under a prior distribution that equals the population distribution of the random coefficients. We show that the risk of a predictor based on a nonparametric kernel estimate of the Tweedie correction is asymptotically equivalent to the risk of a predictor that treats the correlated random effects distribution as known (ratio optimality). Our empirical Bayes predictor performs well compared to various competitors in a Monte Carlo study. In an empirical application, we use the predictor to forecast revenues for a large panel of bank holding companies and compare forecasts that condition on actual and severely adverse macroeconomic conditions.

Partial effects in non-linear panel data models with correlated random effects

2021 ◽  
Author(s):  
Jason Abrevaya ◽  
Yu-Chin Hsu
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Unobserved Heterogeneity ◽  
Data Models ◽  
Panel Data Models ◽  
New Concepts ◽  
Correlated Random Effects ◽  
Non Linear ◽  
Partial Effects

Summary Nonlinearity and heterogeneity are known to cause difficulties in estimating and interpreting partial effects. This paper provides a systematic characterization of the various partial effects in nonlinear panel data models that might be of interest to empirical researchers. The interpretation of the partial effects depends upon (i) whether the distribution of unobserved heterogeneity is treated as fixed or allowed to vary with covariates, and (ii) whether one is interested in particular covariate values or an average over such values. The characterization covers partial-effects concepts already in the literature but also includes new concepts for partial effects. A simple panel probit design highlights that the different partial effects can be quantitatively very different.

Refined-scale panel data crash rate analysis using random-effects tobit model

2014 ◽  
Vol 73 ◽  
pp. 323-332 ◽  
Author(s):  
Feng Chen ◽  
XiaoXiang Ma ◽  
Suren Chen
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Tobit Model ◽  
Rate Analysis
Sign in / Sign up

Export Citation Format

Share Document