Aree Wiboonpongse

Modeling volatility and interdependencies of Thai rubber spot price return with climatic factors, exchange rate and crude oil markets

Thailand is a leading producer and exporter of rubber in the world market. The interdependencies and volatility of Thai rubber price return with climatic factors (precipitation and temperature), exchange rate, and crude oil market returns are determined in this paper. Vector autoregressive moving average process with generalized autoregressive conditional heteroscedasticity (VARMA-GARCH), VARMA with generalized autoregressive conditional heteroscedasticity (VARMA-AGARCH), and copula-based GARCH models were employed for the analyses. The results demonstrated the interdependencies of Thai rubber price return with dollar and crude oil returns as well as with crude oil return and climatic factors in the VARMA-AGARCH and the copula-based GARCH models, respectively. We conclude that the volatility of Thai rubber price return is linked with volatility in the exchange rate and crude oil markets as well as climatic factors. Thus, stakeholders in the rubber industry should consider movements in those markets when forecasting Thai rubber price returns. Using a set of robust approaches is also recommended to obtain a complete picture of the volatilities and interdependencies of the asset markets.

Joint determination of the choice of planting season and technical efficiency of potato in northern Thailand: A comparison of Greene's versus Heckman's sample selection approach

The potato sector in Thailand has undergone significant changes with rapid expansion of the contract farming system, thereby, leading farmers to grow potato either early or late in the season. The present paper addresses two interrelated issues: (a) jointly determine the choice of planting season, productivity and technical efficiency of potato cultivation in northern Thailand; and (b) compare suitability of the two competing methods to analyze the issues, that is, Heckman’s switching regression versus Greene’s sample selection in stochastic frontiers. Results obtained are different but Greene’s model fare better in identifying selectivity bias and model fit. The decision to plant potato early in the season is significantly determined by experience gained through contract farming and expected price, consistent with expectation. Land area and seed are the significant determinants of potato productivity and foliage hormone reduces productivity. The mean technical efficiency (MTE) in Greene’s model is lower by 5 points (p<0.01) than Heckman’s (that is, 0.80 versus 0.85). The main difference is at the upper end of the efficiency distribution. Policy recommendation includes keeping potato price high, promotion of contract farming system and distribution of potato seeds which will synergistically increase adoption and productivity of early season potato and raise farmers’ income. n n xa0 n n Key words:xa0Technical efficiency, stochastic frontier, sample selection, potato, Thailand, contract farming, switching regression.