Estimating the Impact of Avian Flu on International Tourism Demand Using Panel Data

2009 ◽  
Vol 15 (3) ◽  
pp. 501-511 ◽  
Author(s):  
Hsiao-I Kuo ◽  
Chia-Lin Chang ◽  
Bing-Wen Huang ◽  
Chi-Chung Chen ◽  
Michael McAleer
Keyword(s):  
Panel Data ◽  
Pandemic Influenza ◽  
Fixed Effects ◽  
Data Models ◽  
International Tourism ◽  
Panel Data Models ◽  
Tourism Demand ◽  
Avian Flu ◽  
Potential Risks ◽  
The Impact

This paper investigates the impacts of avian flu on global and Asian tourism using panel data procedures. Both static and dynamic fixed effects panel data models are adopted to estimate the impacts of this infectious disease. The empirical results from static and dynamic fixed effects panel data models are consistent and indicate that the number of affected poultry outbreaks has significant impacts on the international tourism of global and Asian affected countries. The high mortality rate among humans, the potential of a global flu pandemic and some media frenzy with hype and speculation might adversely affect the images of these infected destinations as a safe tourist destination. Moreover, it was found that the average damage to Asian tourism was more serious, which might have been induced by an ineffective suppression in numerous Asian infected countries. In addition, Asia was the earliest affected region and the area infected most seriously by avian flu, both in humans and in poultry. Since the potential risks and damage arising from avian flu and the subsequent pandemic influenza are much greater than for previous diseases, the need to take necessary precautions in the event of an outbreak of avian flu and pandemic influenza warrants further attention and action in modelling and managing international tourism demand and risk.

Estimating the Impact of Avian Flu on International Tourism Demand Using Panel Data

2009 ◽  
Author(s):  
Hsiao-I Kuo ◽  
Chia-Lin Chang ◽  
Chi-Chung Chen ◽  
Biing-Wen Huang ◽  
Michael McAleer
Keyword(s):  
Panel Data ◽  
International Tourism ◽  
Tourism Demand ◽  
Avian Flu ◽  
The Impact

scholarly journals Comparing deep neural network and econometric approaches to predicting the impact of climate change on agricultural yield

2020 ◽  
Vol 23 (3) ◽  
pp. S59-S80
Author(s):  
Michael Keane ◽  
Timothy Neal
Keyword(s):  
Climate Change ◽  
Panel Data ◽  
Production Function ◽  
Fixed Effects ◽  
Data Models ◽  
Corn Yield ◽  
Panel Data Models ◽  
Yield Data ◽  
Impact Of Climate Change ◽  
The Impact

Summary Predicting the impact of climate change on crop yield is difficult, in part because the production function mapping weather to yield is high dimensional and nonlinear. We compare three approaches to predicting yields: (a) deep neural networks (DNNs), (b) traditional panel-data models, and (c) a new panel-data model that allows for unit and time fixed effects in both intercepts and slopes in the agricultural production function—made feasible by a new estimator called Mean Observation OLS (MO-OLS). Using U.S. county-level corn-yield data from 1950 to 2015, we show that both DNNs and MO-OLS models outperform traditional panel-data models for predicting yield, both in-sample and in a Monte Carlo cross-validation exercise. However, the MO-OLS model substantially outperforms both DNNs and traditional panel-data models in forecasting yield in a 2006–2015 holdout sample. We compare the predictions of all these models for climate change impacts on yields from 2016 to 2100.

Fitting partially linear functional-coefficient panel-data models with Stata

2020 ◽  
Vol 20 (4) ◽  
pp. 976-998
Author(s):  
Kerui Du ◽  
Yonghui Zhang ◽  
Qiankun Zhou
Keyword(s):  
Monte Carlo ◽  
Panel Data ◽  
Fixed Effects ◽  
Linear Functional ◽  
Semiparametric Estimation ◽  
Data Models ◽  
Panel Data Models ◽  
Panel Models ◽  
Partially Linear ◽  
Functional Coefficient

In this article, we describe the implementation of fitting partially linear functional-coefficient panel models with fixed effects proposed by An, Hsiao, and Li [2016, Semiparametric estimation of partially linear varying coefficient panel data models in Essays in Honor of Aman Ullah ( Advances in Econometrics, Volume 36)] and Zhang and Zhou (Forthcoming, Econometric Reviews). Three new commands xtplfc, ivxtplfc, and xtdplfc are introduced and illustrated through Monte Carlo simulations to exemplify the effectiveness of these estimators.

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.

Bootstrap LM Tests for Spatial Dependence in Panel Data Models with Fixed Effects

2019 ◽  
Vol 7 (4) ◽  
pp. 330-343
Author(s):  
Bianling Ou ◽  
Zhihe Long ◽  
Wenqian Li
Keyword(s):  
Panel Data ◽  
Spatial Dependence ◽  
Fixed Effects ◽  
Orthogonal Transformation ◽  
Transformation Method ◽  
Data Models ◽  
Panel Data Models ◽  
Significance Level ◽  
Lm Tests ◽  
Nominal Significance

Abstract This paper applies bootstrap methods to LM tests (including LM-lag test and LM-error test) for spatial dependence in panel data models with fixed effects, and removes fixed effects based on orthogonal transformation method proposed by Lee and Yu (2010). The consistencies of LM tests and their bootstrap versions are proved, and then some asymptotic refinements of bootstrap LM tests are obtained. It shows that the convergence rate of bootstrap LM tests is O((NT)−2) and that of fast double bootstrap LM tests is O((NT)−5/2). Extensive Monte Carlo experiments suggest that, compared to aysmptotic LM tests, the size of bootstrap LM tests gets closer to the nominal level of signifiance, and the power of bootstrap LM tests is higher, especially in the cases with small spatial correlation. Moreover, when the error is not normal or with heteroskedastic, asymptotic LM tests suffer from severe size distortion, but the size of bootstrap LM tests is close to the nominal significance level. Bootstrap LM tests are superior to aysmptotic LM tests in terms of size and power.

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