The Sensitivity of Labor-Supply Parameter Estimates to Unobserved Individual Effects: Fixed- and Random-Effects Estimates in a Nonlinear Model Using Panel Data

1988 ◽  
Vol 6 (3) ◽  
pp. 302-329 ◽  
Author(s):  
George Jakubson
Keyword(s):  
Panel Data ◽  
Labor Supply ◽  
Random Effects ◽  
Nonlinear Model ◽  
Parameter Estimates ◽  
Fixed And Random Effects ◽  
Individual Effects

scholarly journals Sars-Cov-2 in Argentina: Lockdown, Mobility, and Contagion

2020 ◽  
Author(s):  
Juan M.C. Larrosa
Keyword(s):  
Panel Data ◽  
Granger Causality ◽  
Random Effects ◽  
Social Costs ◽  
Political Discussion ◽  
Fixed And Random Effects ◽  
The World ◽  
Causality Tests ◽  
Granger Causality Tests ◽  
Over Time

AbstractThere is a debate in Argentina about the effectiveness of mandatory lockdown measures in containing COVID-19 that lasts five months making it one of the longest in the World. The population effort to comply the lockdown has been decreasing over time given the economic and social costs that it entails. We contributes by analyzing the Argentinian case through information of mobility and contagion given answers to recurrent questions on these topics. This paper aims to fill the gap in the literature by assessing the effects of lockdown measures and the regional relaxation on the numbers of rate of new infections. We also respond to issues of internal political discussion on regional contagion and the effect of marches and unexpected crowd events. We use pool, fixed and random effects panel data modeling and Granger causality tests identifying relations between mobility and contagion. Our results show that lockdown in Argentina has been effective in reducing the mobility but not in way that reduces the rate of contagion. Strict lockdown seems to be effective in short periods of time and by extend it without complementary measures loss effectiveness. Contagion rate seems to be discretely displaced in time and resurging amidst slowly increasing in mobility.

Analysis of aggregation, a worked example: numbers of ticks on red grouse chicks

Parasitology ◽  
2001 ◽  
Vol 122 (5) ◽  
pp. 563-569 ◽  
Author(s):  
D. A. ELSTON ◽  
R. MOSS ◽  
T. BOULINIER ◽  
C. ARROWSMITH ◽  
X. LAMBIN
Keyword(s):  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Negative Binomial ◽  
Generalized Linear Mixed Model ◽  
Red Grouse ◽  
Worked Example ◽  
Fixed And Random Effects ◽  
Individual Effects ◽  
Lagopus Lagopus Scoticus

The statistical aggregation of parasites among hosts is often described empirically by the negative binomial (Poisson-gamma) distribution. Alternatively, the Poisson-lognormal model can be used. This has the advantage that it can be fitted as a generalized linear mixed model, thereby quantifying the sources of aggregation in terms of both fixed and random effects. We give a worked example, assigning aggregation in the distribution of sheep ticksIxodes ricinuson red grouseLagopus lagopus scoticuschicks to temporal (year), spatial (altitude and location), brood and individual effects. Apparent aggregation among random individuals in random broods fell 8-fold when spatial and temporal effects had been accounted for.

A MIXTURE MODEL FOR PAYMENTS AND PAYMENT NUMBERS IN CLAIMS RESERVING

2016 ◽  
Vol 48 (1) ◽  
pp. 25-53 ◽  
Author(s):  
Patrizia Gigante ◽  
Liviana Picech ◽  
Luciano Sigalotti
Keyword(s):  
Random Effects ◽  
Linear Models ◽  
External Information ◽  
Parameter Estimates ◽  
Prediction Errors ◽  
Poisson Regression Model ◽  
External Data ◽  
Fixed And Random Effects ◽  
Likelihood Approach ◽  
The Impact

AbstractWe consider a Tweedie's compound Poisson regression model with fixed and random effects, to describe the payment numbers and the incremental payments, jointly, in claims reserving. The parameter estimates are obtained within the framework of hierarchical generalized linear models, by applying the h-likelihood approach. Regression structures are allowed for the means and also for the dispersions. Predictions and prediction errors of the claims reserves are evaluated. Through the parameters of the distributions of the random effects, some external information (e.g. a development pattern of industry wide-data) can be incorporated into the model. A numerical example shows the impact of external data on the reserve and prediction error evaluations.

scholarly journals Non-Negativity of a Quadratic form with Applications to Panel Data Estimation, Forecasting and Optimization

Stats ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 185-202
Author(s):  
Bhimasankaram Pochiraju ◽  
Sridhar Seshadri ◽  
Dimitrios Thomakos ◽  
Konstantinos Nikolopoulos
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Symmetric Matrix ◽  
Panel Data Models ◽  
Random Effects Models ◽  
Panel Data Estimation ◽  
Quadratic Minimization ◽  
Fixed And Random Effects ◽  
Estimated Error ◽  
Data Estimation

For a symmetric matrix B, we determine the class of Q such that Q t BQ is non-negative definite and apply it to panel data estimation and forecasting: the Hausman test for testing the endogeneity of the random effects in panel data models. We show that the test can be performed if the estimated error variances in the fixed and random effects models satisfy a specific inequality. If it fails, we discuss the restrictions under which the test can be performed. We show that estimators satisfying the inequality exist. Furthermore, we discuss an application to a constrained quadratic minimization problem with an indefinite objective function.

Estimating marginal and incremental effects in the analysis of medical expenditure panel data using marginalized two-part random-effects generalized Gamma models: Evidence from China healthcare cost data

2017 ◽  
Vol 27 (10) ◽  
pp. 3039-3061
Author(s):  
Bo Zhang ◽  
Wei Liu ◽  
Yingyao Hu
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Healthcare Cost ◽  
Healthcare Costs ◽  
Medical Expenditure ◽  
Parameter Estimates ◽  
Type I ◽  
Type Ii ◽  
Generalized Gamma ◽  
Marginal Inference

Conditional two-part random-effects models have been proposed for the analysis of healthcare cost panel data that contain both zero costs from the non-users of healthcare facilities and positive costs from the users. These models have been extended to accommodate more flexible data structures when using the generalized Gamma distribution to model the positive healthcare expenditures. However, a major drawback with the extended model, which is inherited from the conditional models, is that it is fairly difficult to make direct marginal inference with respect to overall healthcare costs that includes both zeros and non-zeros, or even on positive healthcare costs. In this article, we first propose two types of marginalized two-part random-effects generalized Gamma models (m2RGGMs): Type I m2RGGMs for the inference on positive healthcare costs and Type II m2RGGMs for the inference on overall healthcare costs. Then, the concepts of marginal effect and incremental effect of a covariate on overall and positive healthcare costs are introduced, and estimation of these effects is carefully discussed. Especially, we derive the variance estimates of these effects by following the delta methods and Taylor series approximations for the purpose of making marginal inference. Parameter estimates of Type I and Type II m2RGGMs are obtained through maximum likelihood estimation. An empirical analysis of longitudinal healthcare costs collected in the China Health and Nutrition Survey is conducted using the proposed methodologies.

Skipper skill and panel data in fishing industries

1999 ◽  
Vol 56 (11) ◽  
pp. 2011-2018 ◽  
Author(s):  
Dale Squires ◽  
James Kirkley
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Fixed Effects ◽  
Production Model ◽  
Fixed Effects Model ◽  
Managerial Ability ◽  
Trawl Fishery ◽  
The Pacific ◽  
Fixed And Random Effects ◽  
Catch Rates

Skipper skill or managerial ability plays a central role in the harvesting of fish and fishing power. Examining the influences of managerial ability on catch rates, however, may be complicated, since managerial ability is generally unobservable. Using panel data on production activities in the Pacific Coast trawl fishery, we examine the use of the fixed- and random-effects panel data models to depict managerial skill by intervessel differences, representing differences in technical efficiency. The random-effects production model is selected over the fixed-effects model. We conclude that skipper skill is more related to finding fish, dealing with unforseen events, and handling inclement weather than it is to managing the economic inputs.

Bridging Methodological Divides Between Macro- and Microresearch: Endogeneity and Methods for Panel Data

2019 ◽  
Vol 46 (1) ◽  
pp. 70-99 ◽  
Author(s):  
Paul D. Bliese ◽  
Donald J. Schepker ◽  
Spenser M. Essman ◽  
Robert E. Ployhart
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Fixed Effects ◽  
Theory Development ◽  
Modeling Framework ◽  
Random Effects Models ◽  
Data Support ◽  
Fixed And Random Effects

Both macro- and micro-oriented researchers frequently use panel data where the outcome of interest is measured repeated times. Panel data support at least five different modeling frameworks (within, between, incremental/emergent, cross-level, and growth). Researchers from macro- and micro-oriented domains tend to differentially use the frameworks and also use different analytic tools and terminology when using the same modeling framework. These differences have the potential to inhibit cross-discipline communication. In this review, we explore how macro- and microresearchers approach panel data with a specific emphasis on the theoretical implications of choosing one framework versus another. We illustrate how fixed-effects and random-effects models differ and how they are similar, and we conduct a thorough review of 142 articles that used panel data in leading management journals in 2017. Ultimately, our review identifies ways that researchers can better employ fixed- and random-effects models, model time as a meaningful predictor or ensure unobserved time heterogeneity is controlled, and align hypotheses to analytic choice. In the end, our goal is to help facilitate communication and theory development between macro- and micro-oriented management researchers.

Transformed-likelihood estimators for dynamic panel models with a very small T

2020 ◽  
pp. 1-20
Author(s):  
Mark Pickup ◽  
Vincent Hopkins
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Fixed Effects ◽  
Dynamic Models ◽  
Dynamic Panel ◽  
Panel Models ◽  
Long Run ◽  
Individual Effects ◽  
Lagged Dependent Variable ◽  
Dynamic Panel Models

Conventional OLS fixed-effects and GLS random-effects estimators of dynamic models that control for individual-effects are known to be biased when applied to short panel data (T ≤ 10). GMM estimators are the most used alternative but are known to have drawbacks. Transformed-likelihood estimators are unused in political science. Of these, orthogonal reparameterization estimators are only tangentially referred to in any discipline. We introduce these estimators and test their performance, demonstrating that the unused orthogonal reparameterization estimator in particular performs very well and is an improvement on the commonly used GMM estimators. When T and/or N are small, it provides efficiency gains and overcomes the issues GMM estimators encounter in the estimation of long-run effects when the coefficient on the lagged dependent variable is close to one.

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