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.

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 DEMOCRACY AND GROWTH: A DYNAMIC PANEL DATA STUDY

2018 ◽  
Vol 65 (01) ◽  
pp. 41-80 ◽  
Author(s):  
JEFFRY A. JACOB ◽  
THOMAS OSANG
Keyword(s):  
Economic Growth ◽  
Economic Policy ◽  
Panel Data ◽  
Direct Effect ◽  
Growth Process ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Explanatory Variables ◽  
Regime Stability ◽  
And Control

In this paper, we investigate the idea whether democracy has a direct effect on economic growth. We use a system GMM framework that allows us to model the dynamic aspects of the growth process and control for the endogenous nature of many explanatory variables. In contrast to the growth effects of institutions, regime stability, openness, geography and macro-economic policy variables, we find that measures of democracy matter little, if at all, for the economic growth process.

Modeling Turkish outbound tourism demand using a dynamic panel data approach

2016 ◽  
Vol 18 (4) ◽  
pp. 411-414 ◽  
Author(s):  
Tarik Dogru ◽  
Ercan Sirakaya-Turk
Keyword(s):  
Least Squares Method ◽  
Panel Cointegration ◽  
Ordinary Least Squares ◽  
Dynamic Panel Data ◽  
Relative Price ◽  
Tourism Demand ◽  
Dynamic Panel ◽  
Cointegration Tests ◽  
Managerial Implications ◽  
Ordinary Least Squares Method

The purpose of this study is to model and empirically test outbound tourism demand of Turkish travel market. For this purpose, panel cointegration tests and panel fully modified ordinary least squares method, which produces asymptotically unbiased and normally distributed coefficient estimates, are employed for 12 Turkish travel markets. The results indicate that there is a cointegration relation in the model, and income, relative price, and word of mouth are significant determinants affecting outbound tourism demand of Turkey for the 12 destinations. Managerial implications are discussed.

scholarly journals Modelling Dynamic Micro and Macro Panel Data with Autocorrelated Error Terms

2021 ◽  
pp. 58-70
Author(s):  
Kafayat T. Uthman ◽  
Iyabode F. Oyenuga ◽  
Taiwo M. Adegoke ◽  
Adewale P. Onatunji ◽  
Olanrewaju V. Oni
Keyword(s):  
Panel Data ◽  
Sample Size ◽  
Ordinary Least Squares ◽  
Dynamic Panel Data ◽  
Large Sample Size ◽  
Method Of Moment ◽  
Dynamic Panel ◽  
Time Period ◽  
One Step ◽  
Time Periods

Aims: The aim of this study is to determine the best estimator for estimating dynamic panel data model with serially uncorrelated disturbances and exogenous regressors. Methodology: In this study, properties of some Dynamic Panel Data estimators are investigated. These are Ordinary Least Squares (OLS), the Anderson-Hsiao(AH(d), Arellano-Bond Generalized Method of Moment (ABGMM) one-step, Blundell- Bond System (BBS) one-step, M- estimator,  MM estimators and proposed estimator, Modified Anderson-Hsiao with Arellano-Bond(MAHAB) estimator in the presence of autocorrelation. Also, this new estimator was proposed by modifying the existing estimators. Results: Monte-Carlo simulations were carried out at varying sample size (n) ranges from 10-200 and time period (T) ranges from 5-20 when autocorrelation ( ) is fixed at 0.3, 0.5 and 0.7. The estimators considered performed well except OLS and BBS for all time periods. Conclusion: AH estimator performed relatively well when the time period is small while ABGMM estimator outperformed all other estimators when sample size (n) is large for all the time periods considered. ABGMM shows the largest improvement as sample size (n) and time periods (T) increase. The MAHAB estimator outperformed all other estimators in small and large sample size irrespective of time period in the presence of autocorrelation.

scholarly journals Dinamika PMA dan PMDN di Indonesia Sebagai Dampak Dari Upah Minimum, Inflasi dan PDRB Tahun 2004-2012: Pendekatan Dynamic Panel Data Model

2016 ◽  
Vol 17 (3) ◽  
pp. 119
Author(s):  
Lea Widowati Sugiharto ◽  
Akhmad Syakir Kurnia
Keyword(s):  
Panel Data ◽  
Minimum Wage ◽  
Direct Investment ◽  
Dynamic Panel Data ◽  
Wage Setting ◽  
Dynamic Panel ◽  
Explanatory Variables ◽  
Long Run ◽  
Short Run ◽  
Wage Increases

<div><em>This paper aims at investigating the behavior of foreign direct investment (FDI) and domestic direct investment (DDI) in Indonesia, which is expected to be explained by several explanatory variables including the setting of regional minimum wage, inflation, as well as regional domestic product. More specifically, the investigation is focused on the effect of annual increase in the minimum regional wage, provided that it is a sensitive issue for investors. Using 33 provincial level data in a period from 2004 to 2012, this paper uses a dynamic panel data which allows us to see the behavior of direct investment in the short run as well as in the long run. The result shows that an increase in the regional minimum wage setting reduces both DDI and FDI in the short run. However, in the long run, an increase in the regional minimum wage is likely to increase both DDI and FDI. This is likely indicating that in the long run an increase in wage is expected to be accompanied by higher productivity, eventhough in the short run higher wage increases cost of production which will undermine investment.</em></div>

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