Suchergebnisse

This paper extends the Common Correlated Effects (CCE) approach developed by Pesaran (2006) to heterogeneous panel data models with lagged dependent variable and/or weakly exogenous regressors. We show that the CCE mean group estimator continues to be valid but the following two conditions must be satisfied to deal with the dynamics: a sufficient number of lags of cross section averages must be included in individual equations of the panel, and the number of cross section averages must be at least as large as the number of unobserved common factors. We establish consistency rates, derive the asymptotic distribution, suggest using covariates to deal with the effects of multiple unobserved common factors, and consider jackknife and recursive de-meaning bias correction procedures to mitigate the small sample time series bias. Theoretical findings are accompanied by extensive Monte Carlo experiments, which show that the proposed estimators perform well so long as the time series dimension of the panel is sufficiently large.

Abstract

Research background
Geopolitical risk is currently recognized as a worldwide concern that significantly affects various economic sectors. As a consequence, rising geopolitical risks can cause a decline in the number of tourist arrivals.


Purpose
The aim of this paper is to examine the applicability of a commonly used dynamic model, the autoregressive distributed lag model (ARDL), in a panel data context and the effect of geopolitical risk on explaining tourist demand in chosen countries across long and short time periods.


Research methodology
On 18 developing economies, the Cross-sectionally Augmented Distributed Lag (CS-ARDL) technique is used.


Results
The results of the test show interesting insights. Although the consequences of geopolitical threats in the short term are significant and have a negative impact on tourist arrivals, in the long term, results show there is no effect. Specifically, a rise in geopolitical risk decreases foreign visitor visits in the short term but has no lasting impact.


Novelty
Using an econometric model, this study contributes to the limited research on the link between geopolitical risk and tourist arrivals.

ABSTRACTThe classic newsvendor model was developed under the assumption that period‐to‐period demand is independent over time. In real‐life applications, the notion of independent demand is often challenged. In this article, we examine the newsvendor model in the presence of correlated demands. Specifically under a stationary AR(1) demand, we study the performance of the traditional newsvendor implementation versus a dynamic forecast‐based implementation. We demonstrate theoretically that implementing a minimum mean square error (MSE) forecast model will always have improved performance relative to the traditional implementation in terms of cost savings. In light of the widespread usage of all‐purpose models like the moving‐average method and exponential smoothing method, we compare the performance of these popular alternative forecasting methods against both the MSE‐optimal implementation and the traditional newsvendor implementation. If only alternative forecasting methods are being considered, we find that under certain conditions it is best to ignore the correlation and opt out of forecasting and to simply implement the traditional newsvendor model.