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This study examines the nonlinear impact of tourism development on economic growth in Southeast Asian countries using the panel kink regression model. Due to the paucity of Southeast Asian data, we may face the overparameterization problem in our model. To deal with this problem, this study proposes the Generalized Maximum Entropy (GME) estimator to estimate the unknown parameters in this nonlinear model. Several important tourism development indicators consisting of the total international arrivals, international tourism expenditure, and tourism receipts are considered. In addition, we also consider the gross capital formation and real effective exchange rate as a control variable in our nonlinear model. Our findings show that the effect of international tourist arrivals on economic growth should be separated into two regimes, while other factors do not exhibit a nonlinear relationship with Southeast Asian economic growth. Thus, we construct the empirical model with the kink effect in the variable of international tourist arrivals. To confirm the performance of the GME estimator, we compare it to the ordinary least squares and the fixed effect estimators. According to the mean squared and root means squared errors, we find that our GME estimator performs better than the ordinary least squares and the fixed effect estimators. This indicates that GME estimation is an applicable method for estimating the nonlinear effect of tourism growth on economic growth. Our empirical results show that there are positive impacts of tourism growth on economic growth for regime 1 (low tourism demand) and regime 2 (high tourism demand) with the effect of the low tourist arrivals regime being relatively larger. We also find a positive influence of gross capital formation, real effective exchange rate, international tourism expenditure, and tourism receipts on Southeast Asian economic growth. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]