scholarly journals Identification and Estimation of Nonseparable Single-Index Models in Panel Data with Correlated Random Effects

2013 ◽  
Author(s):  
Pavel Cizek ◽  
Jinghua Lei
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Single Index ◽  
Correlated Random Effects ◽  
Single Index Models

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.

scholarly journals Estimation and testing for panel data partially linear single-index models with errors correlated in space and time

2019 ◽  
Vol 09 (02) ◽  
pp. 2150005
Author(s):  
Jian-Qiang Zhao ◽  
Yan-Yong Zhao ◽  
Jin-Guan Lin ◽  
Zhang-Xiao Miao ◽  
Waled Khaled
Keyword(s):  
Panel Data ◽  
Normal Distribution ◽  
Test Method ◽  
Link Function ◽  
Single Index ◽  
Asymptotically Normal ◽  
Space And Time ◽  
Partially Linear ◽  
Single Index Models ◽  
Unknown Link

We consider a panel data partially linear single-index models (PDPLSIM) with errors correlated in space and time. A serially correlated error structure is adopted for the correlation in time. We propose using a semiparametric minimum average variance estimation (SMAVE) to obtain estimators for both the parameters and unknown link function. We not only establish an asymptotically normal distribution for the estimators of the parameters in the single index and the linear component of the model, but also obtain an asymptotically normal distribution for the nonparametric local linear estimator of the unknown link function. Then, a fitting of spatial and time-wise correlation structures is investigated. Based on the estimators, we propose a generalized F-type test method to deal with testing problems of index parameters of PDPLSIM with errors correlated in space and time. It is shown that under the null hypothesis, the proposed test statistic follows asymptotically a [Formula: see text]-distribution with the scale constant and degrees of freedom being independent of nuisance parameters or functions. Simulated studies and real data examples have been used to illustrate our proposed methodology.

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.

Sign in / Sign up

Export Citation Format

Share Document