Jianxu Liu

TL;DR: This paper aims at estimating the dependency between the percentage changes of the agricultural price and agricultural production indices of Thailand and also their conditional volatilities using copula-based GARCH models and provides less restrictive models for dependency and the conditional volatility GARCH.

TL;DR: In this paper, the authors apply stochastic frontier analysis (SFA) to analyze the growth of agricultural total factor productivity (TFP) and its three components (technical change, technical efficiency change, and scale change) in 15 south and southeast Asian countries covering the period 2002 to 2016.

TL;DR: The main finding of this study is that the dependence between V and W is significant and cannot be ignored, and calls for a reappraisal of previous production efficiency studies using the SFM with independence assumption.

TL;DR: Wang et al. as mentioned in this paper used a stochastic frontier approach to identify determinants of agricultural technical efficiency (TE) scores, total-factor productivity (TFP), and its two components, technological change/progress (TC) and technical efficiency change (EC), using provincial-level panel data of 30 provinces from 2002 to 2017.

TL;DR: In this paper, the authors estimate the dependency between the growth rates of tourist arrivals of Thailand and Singapore from China, and also analyze their conditional volatilities using ARMA-GARCH model to fit monthly time series data.