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.

scholarly journals Capital adjustment cost and inconsistency in income-based dynamic panel models with fixed effects

2019 ◽  
Vol 20 (4) ◽  
pp. e1002-e1018 ◽  
Author(s):  
Parantap Basu ◽  
Yoseph Getachew ◽  
Keshab Bhattarai
Keyword(s):  
Fixed Effects ◽  
Cost Of Capital ◽  
Moving Average ◽  
Adjustment Cost ◽  
Dynamic Panel ◽  
Panel Models ◽  
Capital Adjustment ◽  
Long Run ◽  
Fixed Effects Estimator ◽  
Dynamic Panel Models

Abstract After the seminal work of Nickell (1981), a vast literature demonstrates the inconsistency of ‘conditional convergence’ estimator in income-based dynamic panel models with fixed effects when the time horizon (T) is short but the sample of countries (N) is large. Less attention is given to the economic root of inconsistency of the fixed effects estimator when T is also large. Using a variant of the Ramsey growth model with long-run adjustment cost of capital, we demonstrate that the fixed effects estimator of such models could be inconsistent when T is large. This inconsistency arises because of the long-run adjustment cost of capital which gives rise to a negative moving average coefficient in the error term. Income convergence will be thus overestimated. We theoretically characterize the order of this inconsistency. Our Monte Carlo simulation demonstrates that the size of the bias is substantial and it is greater in economies with higher capital adjustment costs. We show that the use of instrumental variables that take into account the presence of the negative moving average term in the error will overcome this bias.

scholarly journals Does job promotion affect men’s and women’s health differently? Dynamic panel models with fixed effects

2016 ◽  
pp. dyw310 ◽  
Author(s):  
Anna Nyberg ◽  
Paraskevi Peristera ◽  
Hugo Westerlund ◽  
Gunn Johansson ◽  
Linda L Magnusson Hanson
Keyword(s):  
Women’S Health ◽  
Women's Health ◽  
Fixed Effects ◽  
Dynamic Panel ◽  
Panel Models ◽  
Job Promotion ◽  
Dynamic Panel Models

scholarly journals Bidirectional Associations Between Depressive and Anxiety Symptoms and Loneliness During the COVID-19 Pandemic: Dynamic Panel Models With Fixed Effects

2021 ◽  
Vol 12 ◽  
Author(s):  
Cillian P. McDowell ◽  
Jacob D. Meyer ◽  
Daniel W. Russell ◽  
Cassandra Sue Brower ◽  
Jeni Lansing ◽  
...  
Keyword(s):  
Mental Health ◽  
Depressive Symptoms ◽  
Fixed Effects ◽  
Online Survey ◽  
Retention Rate ◽  
Anxiety Symptoms ◽  
Dynamic Panel ◽  
Panel Models ◽  
Bidirectional Associations ◽  
Dynamic Panel Models

Background: Understanding the direction and magnitude of mental health-loneliness associations across time is important to understand how best to prevent and treat mental health and loneliness. This study used weekly data collected over 8 weeks throughout the COVID-19 pandemic to expand previous findings and using dynamic panel models with fixed effects which account for all time-invariant confounding and reverse causation.Methods: Prospective data on a convenience and snowball sample from all 50 US states and the District of Colombia (n = 2,361 with ≥2 responses, 63.8% female; 76% retention rate) were collected weekly via online survey at nine consecutive timepoints (April 3–June 3, 2020). Anxiety and depressive symptoms and loneliness were assessed at each timepoint and participants reported the COVID-19 containment strategies they were following. Dynamic panel models with fixed effects examined bidirectional associations between anxiety and depressive symptoms and loneliness, and associations of COVID-19 containment strategies with these outcomes.Results: Depressive symptoms were associated with small increases in both anxiety symptoms (β = 0.065, 95% CI = 0.022–0.109; p = 0.004) and loneliness (β = 0.019, 0.008–0.030; p = 0.001) at the subsequent timepoint. Anxiety symptoms were associated with a small subsequent increase in loneliness (β = 0.014, 0.003–0.025; p = 0.015) but not depressive symptoms (β = 0.025, −0.020–0.070; p = 0.281). Loneliness was strongly associated with subsequent increases in both depressive (β = 0.309, 0.159–0.459; p < 0.001) and anxiety (β = 0.301, 0.165–0.436; p < 0.001) symptoms. Compared to social distancing, adhering to stay-at-home orders or quarantining were not associated with anxiety and depressive symptoms or loneliness (both p ≥ 0.095).Conclusions: High loneliness may be a key risk factor for the development of future anxiety or depressive symptoms, underscoring the need to combat or prevent loneliness both throughout and beyond the COVID-19 pandemic. COVID-19 containment strategies were not associated with mental health, indicating that other factors may explain previous reports of mental health deterioration throughout the pandemic.

scholarly journals Estimating dynamic Panel data. A practical approach to perform long panels.

2018 ◽  
Vol 41 (1) ◽  
pp. 31-52 ◽  
Author(s):  
Romilio Labra Lillo ◽  
Celia Torrecillas
Keyword(s):  
Social Sciences ◽  
Panel Data ◽  
Dynamic Models ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Individual Effects ◽  
Quantitative Analyses ◽  
Number Of Individuals ◽  
Static Nature ◽  
Practical Constraints

Panel data methodology is one of the most popular tools for quantitative analyses in the field of social sciences, particularly on topics related to economics and business. This technique allows us simultaneously addressing individual effects, numerous periods, and in turn, the endogeneity of the model or independent regressors. Despite these advantages, there are several methodological and practical limitations to perform estimations using this tool. Two types of models can be estimated with Panel data. While those of static nature have been the most developed, for performing dynamic models still remain some theoretical and practical constraints. This paper focus precisely on the latter, dynamics panel data, using an approach that combines theory and praxis, and paying special attention on estimations with macro database, that is to say, dataset with a long period of time and a small number of individuals, also called long panels.

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