scholarly journals Young Children Who Eat Animal Sourced Foods Grow Less Stunted: Findings of Contemporaneous and Lagged Analyses from Nepal, Uganda and Bangladesh

2020 ◽  
Author(s):  
Sonia Zaharia ◽  
Shibani Ghosh ◽  
Robin Shrestha ◽  
Swetha Manohar ◽  
Andrew Thorne-Lyman ◽  
...  
Keyword(s):  
Panel Data ◽  
Young Children ◽  
Empirical Evidence ◽  
Fixed Effects ◽  
Linear Growth ◽  
Child Growth ◽  
Resource Constrained ◽  
Cross Sectional ◽  
Fixed Effects Models

Abstract In resource constrained countries, animal-sourced foods (ASFs) are an important nutrient-dense source of vitamins, minerals and macronutrients. While several studies have suggested the value of ASFs to child growth, most empirical evidence is based on cross-sectional data which can only provide information about the contemporaneous relationship between diet and anthropometric outcomes. This study uses longitudinal panel data for Nepal, Bangladesh, and Uganda to assess the association between contemporaneous as well as past ASF consumption and linear growth of children aged 6-24 months. Fixed effects models found that ASF consumption was significantly correlated with lower stunting, with a decline in stunting prevalence as high as 10% in Nepali children who had consumed any ASF in the previous year. Consuming two or more ASFs showed an even higher magnitude of association, ranging from a 10% decline in prevalence of stunting associated with lagged consumption in Bangladesh to a 16% decline in Nepal.

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 Asymmetric Fixed Effects Models for Panel Data

2018 ◽  
Author(s):  
Paul D Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed effects methods presume that effects of variables are symmetric: the effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light (2017) showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this paper, I show that there are several aspects of their method that need improvement. I also develop a data generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

scholarly journals Asymmetric Fixed-effects Models for Panel Data

2019 ◽  
Vol 5 ◽  
pp. 237802311982644 ◽  
Author(s):  
Paul D. Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed-effects methods presume that effects of variables are symmetric: The effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this article, I show that there are several aspects of their method that need improvement. I also develop a data-generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

scholarly journals Bias correction methods for dynamic panel data models with fixed effects

2017 ◽  
Vol 6 (2) ◽  
pp. 58
Author(s):  
Mohamed Abonazel
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Estimation Methods ◽  
Time Dimension ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Cross Sectional Dimension ◽  
Ls Estimator

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally; we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the conventional estimators.

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 ANALISIS PENGARUH UTANG BERBASIS BUNGA DAN PENDAPATAN NON-HALAL TERHADAP NILAI PERUSAHAAN EMITEN SAHAM SYARIAH

2018 ◽  
Vol 4 (2) ◽  
pp. 103
Author(s):  
Muhammad Adlan ◽  
Imron Mawardi
Keyword(s):  
Panel Data ◽  
Firm Value ◽  
Fixed Effects ◽  
Lower Portion ◽  
Standard Errors ◽  
Fixed Effects Models ◽  
Total Debt ◽  
Robust Standard Errors ◽  
Operating Revenue

This study aims to determine whether interest-based debt limitation and non-halal income limitation have significant effect on the firm value. Sharia stock issuers in Indonesia are obliged to pass several conditions set by the market regulator, some of them are limitations of the interest-based debt and non-halal income. This study assumes that the lower portion of interest-based debt and non-halal income, the more the investors will prefer the stocks, thus increasing the firm value. The subjects of this study are the companies listed on JII period 2013-2017. This study measures interest-based debt with ratio of interest-based debt devided by total debt, measures non-halal income with ratio of non-halal income divided by operating revenue, and measures the value of the firm with PBV. The analysis of this study using panel data regressions with fixed effects models with robust standard errors. The results shows that interest-based debt and non-halal income have no effects on the value of the firm, partially and simultaneously

DISTRIBUTION-FREE ESTIMATION OF THE BOX–COX REGRESSION MODEL WITH CENSORING

2011 ◽  
Vol 28 (3) ◽  
pp. 680-695 ◽  
Author(s):  
Songnian Chen
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Fixed Effects ◽  
Cox Regression ◽  
Cox Model ◽  
Parametric Estimation ◽  
Cross Sectional ◽  
Cox Regression Model ◽  
Distribution Free ◽  
The Cross

The Box–Cox regression model has been widely used in applied economics. However, there has been very limited discussion when data are censored. The focus has been on parametric estimation in the cross-sectional case, and there has been no discussion at all for the panel data model with fixed effects. This paper fills these important gaps by proposing distribution-free estimators for the Box–Cox model with censoring in both the cross-sectional and panel data settings. The proposed methods are easy to implement by combining a convex minimization problem with a one-dimensional search. The procedures are applicable to other transformation models.

The effects of previous entrepreneurial experience on subsequent wage-employment wages

2018 ◽  
pp. 103-120
Author(s):  
L. I. Smirnykh ◽  
A. V. Aistov ◽  
E. N. Taruninа
Keyword(s):  
Growth Rate ◽  
Panel Data ◽  
Empirical Analysis ◽  
Fixed Effects ◽  
Wage Growth ◽  
Wage Penalty ◽  
Entrepreneurial Experience ◽  
Fixed Effects Models ◽  
Wage Employment

The article considers influence of entrepreneurial experience on the wageemployment wages. Empirical analysis is based on the RLMS-HSE panel data, 2000—2013, with using fixed effects models on the overall sample, five-year- and flexible window. Results show that transition from entrepreneurship to wageemployment leads to penalty of wages. Wage growth rate of former entrepreneurs’ lag behind the wage growth rate of workers without entrepreneurial experience. The size of wage-penalty decreases if the profession remains the same in transition from entrepreneurship to wage-employment.

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