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.

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.

scholarly journals Nonparametric Estimation of a Conditional Quantile Function in a Fixed Effects Panel Data Model

2018 ◽  
Vol 11 (3) ◽  
pp. 44 ◽  
Author(s):  
Karen Yan ◽  
Qi Li
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Numerical Optimization ◽  
Fixed Effects ◽  
Quantile Function ◽  
Panel Data Model ◽  
Nonparametric Method ◽  
Conditional Quantile ◽  
Finite Samples ◽  
Conditional Quantile Function

This paper develops a nonparametric method to estimate a conditional quantile function for a panel data model with an additive individual fixed effects. The proposed method is easy to implement, it does not require numerical optimization and automatically ensures quantile monotonicity by construction. Monte Carlo simulations show that the proposed estimator performs well in finite samples.

scholarly journals Maternal Mortality in Ghana: Impact of the Fee-Free Delivery Policy and the National Health Insurance Scheme

2015 ◽  
Vol 4 (3) ◽  
pp. 232
Author(s):  
Seidu Sofo ◽  
Emmanuel Thompson
Keyword(s):  
Health Insurance ◽  
Panel Data ◽  
Maternal Mortality ◽  
National Health Insurance ◽  
National Health ◽  
Data Model ◽  
Fixed Effects ◽  
Panel Data Model ◽  
Skilled Delivery ◽  
The Impact

<p>Maternal mortality (MMR) is the second largest cause of female deaths in Ghana. Yet, many households cannot afford the cost of skilled delivery The study utilized the Panel Data Model to examine the impact of the fee-free delivery (FDP) and the National Health Insurance Policy (NIP) exemptions on MMR in Ghana. The Demographic and Health Survey reports on Ghana from 2002 to 2009 served as the main data source. Data were analyzed using Panel data model with within group fixed effects estimator. MMR declined significantly over the period studied. Both FDP and NIP positively impacted MMR at a 5% level of significance. In addition, skilled delivery was a significant predictor of MMR. Stakeholders would do well to ensure NIP is adequately funded in order to sustain the decline in MMR.</p><p> </p><p><strong><br /></strong></p>

Quantile regression for panel data models with fixed effects under random censoring

2019 ◽  
Vol 49 (18) ◽  
pp. 4430-4445
Author(s):  
Dai Xiaowen ◽  
Jin Libin ◽  
Tian Yuzhu ◽  
Tian Maozai ◽  
Tang Manlai
Keyword(s):  
Panel Data ◽  
Quantile Regression ◽  
Fixed Effects ◽  
Data Models ◽  
Panel Data Models ◽  
Random Censoring

scholarly journals Optimal trade-off between sample size, precision of supervision, and selection probabilities for the unbalanced fixed effects panel data model

2020 ◽  
Vol 24 (21) ◽  
pp. 15937-15949
Author(s):  
Giorgio Gnecco ◽  
Federico Nutarelli ◽  
Daniela Selvi
Keyword(s):  
Panel Data ◽  
Sample Size ◽  
Upper Bound ◽  
Data Model ◽  
Fixed Effects ◽  
Conditional Variance ◽  
Panel Data Model ◽  
Generalization Error ◽  
Trade Off ◽  
Selection Probabilities

Abstract This paper is focused on the unbalanced fixed effects panel data model. This is a linear regression model able to represent unobserved heterogeneity in the data, by allowing each two distinct observational units to have possibly different numbers of associated observations. We specifically address the case in which the model includes the additional possibility of controlling the conditional variance of the output given the input and the selection probabilities of the different units per unit time. This is achieved by varying the cost associated with the supervision of each training example. Assuming an upper bound on the expected total supervision cost and fixing the expected number of observed units for each instant, we analyze and optimize the trade-off between sample size, precision of supervision (the reciprocal of the conditional variance of the output) and selection probabilities. This is obtained by formulating and solving a suitable optimization problem. The formulation of such a problem is based on a large-sample upper bound on the generalization error associated with the estimates of the parameters of the unbalanced fixed effects panel data model, conditioned on the training input dataset. We prove that, under appropriate assumptions, in some cases “many but bad” examples provide a smaller large-sample upper bound on the conditional generalization error than “few but good” ones, whereas in other cases the opposite occurs. We conclude discussing possible applications of the presented results, and extensions of the proposed optimization framework to other panel data models.

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