scholarly journals Inference on semiparametric multinomial response models

2021 ◽  
Vol 12 (3) ◽  
pp. 743-777 ◽  
Author(s):  
Shakeeb Khan ◽  
Fu Ouyang ◽  
Elie Tamer
Keyword(s):  
Panel Data ◽  
Sufficient Conditions ◽  
Rates Of Convergence ◽  
Dynamic Panel Data ◽  
Finite Sample ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Data Set ◽  
Response Models ◽  
Section Data

We explore inference on regression coefficients in semiparametric multinomial response models. We consider cross‐sectional, and both static and dynamic panel settings where we focus throughout on inference under sufficient conditions for point identification. The approach to identification uses a matching insight throughout all three models coupled with variation in regressors: with cross‐section data, we match across individuals while with panel data, we match within individuals over time. Across models, we relax the Indpendence of Irrelevant Alternatives (or IIA assumption, see McFadden (1974)) and allow for arbitrary correlation in the unobservables that determine utility of various alternatives. For the cross‐sectional model, estimation is based on a localized rank objective function, analogous to that used in Abrevaya, Hausman, and Khan (2010), and presents a generalization of existing approaches. In panel data settings, rates of convergence are shown to exhibit a curse of dimensionality in the number of alternatives. The results for the dynamic panel data model generalize the work of Honoré and Kyriazidou (2000) to cover the semiparametric multinomial case. A simulation study establishes adequate finite sample properties of our new procedures. We apply our estimators to a scanner panel data set.

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.

Estimating Dynamic Panel Data Models in Political Science

2002 ◽  
Vol 10 (1) ◽  
pp. 25-48 ◽  
Author(s):  
Gregory Wawro
Keyword(s):  
Panel Data ◽  
Political Science ◽  
Dynamic Models ◽  
Party Identification ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Panel Data Models ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Moments Estimators

Panel data are a very valuable resource for finding empirical solutions to political science puzzles. Yet numerous published studies in political science that use panel data to estimate models with dynamics have failed to take into account important estimation issues, which calls into question the inferences we can make from these analyses. The failure to account explicitly for unobserved individual effects in dynamic panel data induces bias and inconsistency in cross-sectional estimators. The purpose of this paper is to review dynamic panel data estimators that eliminate these problems. I first show how the problems with cross-sectional estimators arise in dynamic models for panel data. I then show how to correct for these problems using generalized method of moments estimators. Finally, I demonstrate the usefulness of these methods with replications of analyses in the debate over the dynamics of party identification.

scholarly journals Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models

2016 ◽  
Author(s):  
Jan Kiviet ◽  
Milan Pleus ◽  
Rutger W. Poldermans
Keyword(s):  
Panel Data ◽  
Dynamic Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Weighting Matrix ◽  
Variance Estimators ◽  
Finite Samples ◽  
Inference Techniques

Studies employing Arellano-Bond and Blundell-Bond GMM estimation for single linear dynamic panel data models are growing exponentially in number. However, for researchers it is hard to make a reasoned choice between many different possible implementations of these estimators and associated tests. By simulation the effects are examined of many options regarding: (i) reducing, extending or modifying the set of instruments; (ii) specifying the weighting matrix in relation to the type of heteroskedasticity; (iii) using (robustified) 1-step or (corrected) 2-step variance estimators; (iv) employing 1-step or 2-step residuals in Sargan-Hansen overall or incremental overidentification restrictions tests. This is all done for models in which some regressors may be either strictly exogenous, predetermined or endogenous. Surprisingly, particular asymptotically optimal and relatively robust weighting matrices are found to be superior in finite samples to ostensibly more appropriate versions. Most of the variants of tests for overidentification restrictions show serious deficiencies. A recently developed modification of GMM is found to have great potential when the cross-sectional heteroskedasticity is pronounced and the time-series dimension of the sample not too small. Finally all techniques are employed to actual data and lead to some profound insights.

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 ECONOMETRIC ANALYSIS FOR TOURISM DEMAND FUNCTION IN EGYPT A DYNAMIC PANEL DATA APPROACH

2018 ◽  
pp. 321 ◽  
Author(s):  
Waleed Said Soliman Faragalla
Keyword(s):  
Panel Data ◽  
Demand Function ◽  
Dynamic Panel Data ◽  
Relative Price ◽  
Tourism Demand ◽  
Dynamic Panel ◽  
Data Set ◽  
Explanatory Variables ◽  
Time Period ◽  
Lagged Dependent Variable

In this paper, the author investigates the tourism demand function using the dynamic panel data approach in the case of Egypt. The panel data set covers the time period between 1995 and 2014. The individuals are 49 countries as origin countries for tourists, representing 92% of the total tourist arrivals to Egypt. Explanatory variables which affect the tourism demand function were taken into account: lag of dependent variable that leads to dynamic panel data approach, using DIFF-GMM estimator proposed by Arellano and Bond (1991); also, many other explanatory variables like GDP per capita, relative price index, distance, and dummy variable which represent the political situation. One of the important and significant conclusions of the paper is the significant effect of the lagged dependent variable (0.493), which may be explained as “Word of Mouth” to tourists’ decision when choosing the destination.

Gender differences in growth of Spanish university spin-offs

2018 ◽  
Vol 33 (2) ◽  
pp. 86-103 ◽  
Author(s):  
María Jesús Rodríguez-Gulías ◽  
Sara Fernández-López ◽  
David Rodeiro-Pazos
Keyword(s):  
Gender Differences ◽  
Panel Data ◽  
Firm Growth ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Data Set ◽  
Content Type ◽  
Resource Endowment ◽  
Similar Resource ◽  
The University

Purpose The purpose of this paper is to explore the hypothesis that the female-owned university spin-off organizations (USOs) have a similar resource endowment and, as a consequence, growth rates similar to the male-owned USOs. Design/methodology/approach A unique and original longitudinal data set, which is an unbalanced panel, consisting of 120 Spanish USOs over the period 2001-2010 has been constructed. The methodology includes the analysis of mean differences (t-test) and dynamic panel data models. Findings The results confirmed that there are no gender differences in either the firms’ initial resource endowment or in the preference for industries. There is no gender effect on the USOs’ growth, but the initial endowment resources matter. Thus the financial, human and technological resources have a positive effect on the USOs’ growth. This evidence suggests that the USOs’ context may mitigate the initial resource endowment of the female-owned firms and their preferences for traditional industries, showing similar rates of growth than male-owned USOs. Research limitations/implications Owners’ gender has been used as a proxy for founders’ gender. Also, only USOs included in the SABI database have been considered as part of the sample; the significant number of USOs that did not reveal information about their owners have been discarded. Practical implications It is important to continue supporting academic entrepreneurship, as in the university context, firm growth is not affected by gender differences. However, given that the percentage of female owners in university entrepreneurship is still lower compared to entrepreneurship in general, the universities’ entrepreneur programmes targeting women must adopt a gendered perspective. Originality/value Literature on USOs has traditionally analyzed the firm-specific characteristics that impact their growth without considering the influence of the owners’ gender. In this paper, an attempt to fill this gap has been made using a sample of 120 Spanish USOs and by applying the dynamic panel data methodology. In particular, it has been argued that the university context from which USOs emerge allows female-owned USOs to have a similar resource endowment and, as a consequence, a similar growth when compared to male-owned USOs.

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