scholarly journals ESTIMATING PANEL DATA DURATION MODELS WITH CENSORED DATA

2008 ◽  
Vol 24 (5) ◽  
pp. 1254-1276 ◽  
Author(s):  
Sokbae Lee
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Unobserved Heterogeneity ◽  
Right Censoring ◽  
Duration Models ◽  
Finite Sample ◽  
Asymptotically Normal ◽  
Monte Carlo Studies

This paper presents a method for estimating a class of panel data duration models, under which an unknown transformation of the duration variable is linearly related to the observed explanatory variables and the unobserved heterogeneity (or frailty) with completely known error distributions. This class of duration models includes a panel data proportional hazards model with fixed effects. The proposed estimator is shown to be n1/2-consistent and asymptotically normal with dependent right censoring. The paper provides some discussions on extending the estimator to the cases of longer panels and multiple states. Some Monte Carlo studies are carried out to illustrate the finite-sample performance of the new estimator.

scholarly journals TESTING HOMOGENEITY IN PANEL DATA MODELS WITH INTERACTIVE FIXED EFFECTS

2013 ◽  
Vol 29 (6) ◽  
pp. 1079-1135 ◽  
Author(s):  
Liangjun Su ◽  
Qihui Chen
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Data Models ◽  
Development Economic ◽  
Strong Mixing ◽  
Panel Data Models ◽  
Finite Sample ◽  
Test Statistic ◽  
Lm Test ◽  
Interactive Fixed Effects

This paper proposes a residual-based Lagrange Multiplier (LM) test for slope homogeneity in large-dimensional panel data models with interactive fixed effects. We first run the panel regression under the null to obtain the restricted residuals and then use them to construct our LM test statistic. We show that after being appropriately centered and scaled, our test statistic is asymptotically normally distributed under the null and a sequence of Pitman local alternatives. The asymptotic distributional theories are established under fairly general conditions that allow for both lagged dependent variables and conditional heteroskedasticity of unknown form by relying on the concept of conditional strong mixing. To improve the finite-sample performance of the test, we also propose a bootstrap procedure to obtain the bootstrap p-values and justify its validity. Monte Carlo simulations suggest that the test has correct size and satisfactory power. We apply our test to study the Organization for Economic Cooperation and Development economic growth model.

Limitations of Fixed-Effects Models for Panel Data

2019 ◽  
Vol 63 (3) ◽  
pp. 357-369 ◽  
Author(s):  
Terrence D. Hill ◽  
Andrew P. Davis ◽  
J. Micah Roos ◽  
Michael T. French
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Statistical Power ◽  
Unobserved Heterogeneity ◽  
Critical Discussion ◽  
Causal Inferences ◽  
Cross Sectional ◽  
Fixed Effects Models ◽  
Time Invariance ◽  
Type Ii Errors

Although fixed-effects models for panel data are now widely recognized as powerful tools for longitudinal data analysis, the limitations of these models are not well known. We provide a critical discussion of 12 limitations, including a culture of omission, low statistical power, limited external validity, restricted time periods, measurement error, time invariance, undefined variables, unobserved heterogeneity, erroneous causal inferences, imprecise interpretations of coefficients, imprudent comparisons with cross-sectional models, and questionable contributions vis-à-vis previous work. Instead of discouraging the use of fixed-effects models, we encourage more critical applications of this rigorous and promising methodology. The most important deficiencies—Type II errors, biased coefficients and imprecise standard errors, misleading p values, misguided causal claims, and various theoretical concerns—should be weighed against the likely presence of unobserved heterogeneity in other regression models. Ultimately, we must do a better job of communicating the pitfalls of fixed-effects models to our colleagues and students.

scholarly journals A tutorial on frailty models

2020 ◽  
Vol 29 (11) ◽  
pp. 3424-3454 ◽  
Author(s):  
Theodor A Balan ◽  
Hein Putter
Keyword(s):  
Random Effects ◽  
Recurrent Events ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Unobserved Heterogeneity ◽  
Frailty Models ◽  
Survival Outcomes ◽  
Time To Event ◽  
Homogeneous Population ◽  
Hazards Model

The hazard function plays a central role in survival analysis. In a homogeneous population, the distribution of the time to event, described by the hazard, is the same for each individual. Heterogeneity in the distributions can be accounted for by including covariates in a model for the hazard, for instance a proportional hazards model. In this model, individuals with the same value of the covariates will have the same distribution. It is natural to think that not all covariates that are thought to influence the distribution of the survival outcome are included in the model. This implies that there is unobserved heterogeneity; individuals with the same value of the covariates may have different distributions. One way of accounting for this unobserved heterogeneity is to include random effects in the model. In the context of hazard models for time to event outcomes, such random effects are called frailties, and the resulting models are called frailty models. In this tutorial, we study frailty models for survival outcomes. We illustrate how frailties induce selection of healthier individuals among survivors, and show how shared frailties can be used to model positively dependent survival outcomes in clustered data. The Laplace transform of the frailty distribution plays a central role in relating the hazards, conditional on the frailty, to hazards and survival functions observed in a population. Available software, mainly in R, will be discussed, and the use of frailty models is illustrated in two different applications, one on center effects and the other on recurrent events.

PANEL DATA MODELS WITH FINITE NUMBER OF MULTIPLE EQUILIBRIA

2009 ◽  
Vol 26 (3) ◽  
pp. 863-881 ◽  
Author(s):  
Jinyong Hahn ◽  
Hyungsik Roger Moon
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Multiple Equilibria ◽  
Finite Sample ◽  
Finite Support ◽  
Time Dimension ◽  
Cross Sectional ◽  
Incidental Parameters ◽  
Cross Sectional Dimension ◽  
Fixed Effects Estimator

We study a nonlinear panel data model in which the fixed effects are assumed to have finite support. The fixed effects estimator is known to have the incidental parameters problem. We contribute to the literature by making a qualitative observation that the incidental parameters problem in this model may not be not as severe as in the conventional case. Because fixed effects have finite support, the probability of correctly identifying the fixed effect converges to one even when the cross sectional dimension grows as fast as some exponential function of the time dimension. As a consequence, the finite sample bias of the fixed effects estimator is expected to be small.

scholarly journals Parameter Estimation of Shared Frailty Models Based on Particle Swarm Optimization

2016 ◽  
Vol 6 (1) ◽  
pp. 48 ◽  
Author(s):  
Oykum Esra Askin ◽  
Deniz Inan ◽  
Ali Hakan Buyuklu
Keyword(s):  
Particle Swarm Optimization ◽  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Unobserved Heterogeneity ◽  
Particle Swarm ◽  
Likelihood Estimation ◽  
Real Data ◽  
Frailty Models ◽  
Swarm Optimization

Standard survival techniques such as proportional hazards model are suffering from the unobserved heterogeneity. Frailty models provide an alternative way in order to account for heterogeneity caused by unobservable risk factors. Although vast studies have been done on estimation procedures, Evolutionary Algorithms (EAs) haven't received much attention in frailty studies. In this paper, we investigate the estimation performance of maximum likelihood estimation (MLE) via Particle Swarm Optimization (PSO) in modelling multivariate survival data with shared gamma frailty. Simulation studies and real data application are performed in order to assess the performance of MLE via PSO, quasi-Newton  and conjugate gradient method.

scholarly journals A homogeneous approach to testing for Granger non-causality in heterogeneous panels

2020 ◽  
Author(s):  
Artūras Juodis ◽  
Yiannis Karavias ◽  
Vasilis Sarafidis
Keyword(s):  
Panel Data ◽  
Cross Sections ◽  
Fixed Effects ◽  
Wald Test ◽  
Panel Data Models ◽  
Finite Sample ◽  
Cross Sectional ◽  
Data Set ◽  
Jackknife Method ◽  
Heterogeneous Panels

AbstractThis paper develops a new method for testing for Granger non-causality in panel data models with large cross-sectional (N) and time series (T) dimensions. The method is valid in models with homogeneous or heterogeneous coefficients. The novelty of the proposed approach lies in the fact that under the null hypothesis, the Granger-causation parameters are all equal to zero, and thus they are homogeneous. Therefore, we put forward a pooled least-squares (fixed effects type) estimator for these parameters only. Pooling over cross sections guarantees that the estimator has a $$\sqrt{NT}$$ NT convergence rate. In order to account for the well-known “Nickell bias”, the approach makes use of the well-known Split Panel Jackknife method. Subsequently, a Wald test is proposed, which is based on the bias-corrected estimator. Finite-sample evidence shows that the resulting approach performs well in a variety of settings and outperforms existing procedures. Using a panel data set of 350 U.S. banks observed during 56 quarters, we test for Granger non-causality between banks’ profitability and cost efficiency.

scholarly journals Simulation Extrapolation Method for Cox Regression Model with a Mixture of Berkson and Classical Errors in the Covariates using Calibration Data

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Jean de Dieu Tapsoba ◽  
Edward C. Chao ◽  
Ching-Yun Wang
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Regression ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Classical Type ◽  
Calibration Data ◽  
Finite Sample ◽  
Cox Regression Analysis ◽  
Simulation Extrapolation

Abstract Many biomedical or epidemiological studies often aim to assess the association between the time to an event of interest and some covariates under the Cox proportional hazards model. However, a problem is that the covariate data routinely involve measurement error, which may be of classical type, Berkson type or a combination of both types. The issue of Cox regression with error-prone covariates has been well-discussed in the statistical literature, which has focused mainly on classical error so far. This paper considers Cox regression analysis when some covariates are possibly contaminated with a mixture of Berkson and classical errors. We propose a simulation extrapolation-based method to address this problem when two replicates of the mismeasured covariates are available along with calibration data for some subjects in a subsample only. The proposed method places no assumption on the mixture percentage. Its finite-sample performance is assessed through a simulation study. It is applied to the analysis of data from an AIDS clinical trial study.

scholarly journals Does Marriage Increase Couples’ Life Satisfaction?

2021 ◽  
Vol 46 ◽  
Author(s):  
Alexander Gattig ◽  
Lara Minkus
Keyword(s):  
Life Satisfaction ◽  
Panel Data ◽  
Fixed Effects ◽  
Unobserved Heterogeneity ◽  
Married Couples ◽  
Social Mechanisms ◽  
Special Issue ◽  
Demographic Research ◽  
Analytical Strategies ◽  
Lower Effect

Many contemporary studies find that married couples are more satisfied with life than unmarried people. However, whether marriage makes people more satisfied with life or whether more satisfied couples are more likely to marry remains a debated question. We reassess this relationship with panel data from the German Family Panel (pairfam) and extend previous analyses by adding individual trajectories (slopes) to standard fixed-effects regressions (FEIS). We are thereby able to distinguish – controlling for time-constant unobserved heterogeneity – whether there is in fact an effect of marriage on life satisfaction, whether people who are simply happier in their relationship are more likely to get married, or whether people whose development in life satisfaction is more positive are more likely to get married. We translate these different social mechanisms into different analytical strategies and find that OLS regression – due to its confounding effects between and within persons – overestimates the effect of marriage on life satisfaction. A fixed-effects estimator reveals a much lower effect of marriage on life satisfaction for couples who marry compared to those who continue to live apart together or cohabitate. Additionally, using a FEIS estimator and adjusting for – non-linear – development of individual life satisfaction over time, suggests that this effect is in fact causal. * This article belongs to a special issue on "Identification of causal mechanisms in demographic research: The contribution of panel data".

scholarly journals Instrumental Variable Quantile Regression of Spatial Dynamic Durbin Panel Data Model with Fixed Effects

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3261
Author(s):  
Danqing Chen ◽  
Jianbao Chen ◽  
Shuangshuang Li
Keyword(s):  
Panel Data ◽  
Quantile Regression ◽  
Data Model ◽  
Instrumental Variable ◽  
Fixed Effects ◽  
Finite Sample ◽  
Spatial Dynamic ◽  
Spatial Weights ◽  
Disturbance Term ◽  
Instrumental Variable Quantile Regression

This paper studies a quantile regression spatial dynamic Durbin panel data (SDDPD) model with fixed effects. Conventional fixed effects estimators of quantile regression specification are usually biased in the presentation of lagged response variables in spatial and time as regressors. To reduce this bias, we propose the instrumental variable quantile regression (IVQR) estimator with lagged covariates in spatial and time as instruments. Under some regular conditions, the consistency and asymptotic normalityof the estimators are derived. Monte Carlo simulations show that our estimators not only perform well in finite sample cases at different quantiles but also have robustness for different spatial weights matrices and for different disturbance term distributions. The proposed method is used to analyze the influencing factors of international tourism foreign exchange earnings of 31 provinces in China from 2011 to 2017.

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