Workers of different generations often complain about one another as their opinions, values, attitudes, and approaches to work differ. This might lead to a reduction in labor productivity that can negatively impact the economic growth of any nation. In this paper, we used generation mix indices to analyze whether generation gap has any impact on economic growth. Using Thailand's data between 1961 to 2019, we found that when generations were intensely mixed, economic growth did suffer.

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The research aims to investigate the relationship between the exchange rate of Thai Baht against USD and oil price using daily data from January 1999 to March 2019. To test whether there is the long-run relationship between selected variables, the Johansen cointegration method is employed. The results indicate the evidence of long-run relationship between oil price and the Thai Baht. Then, Artificial Neural Networks (ANN) technique is employed for estimation. For ANN estimation, the results suggest that the most influential variables for the Thai Baht is gold price and oil price is the third influential variable for Thai Baht. The research applies the mean squared error, root mean square error and the mean absolute percentage error to measure the error. The results suggest that ANN estimation is more efficient than linear model in term of error estimation.

This paper tries to investigate empirical results of the relationship between stock prices and oil prices over the period from January 2000 to December 2017 using the daily Thailand stock market index (SET) and West Texas Intermediate spot oil prices (WTI). We perform the unit root and cointegration tests for a long run relationship between these two variables. Next, we perform the causal relationship tests between stock prices and oil prices. Due to the limited power of traditional linear Granger causality test, the results may be misleading. We, then, conduct both the tests of linear Granger (1969)'s Granger and nonlinear Granger causality developed by Hiemstra and Jones (1993) and Diks and Panchenko (2006) to identify the possible nonlinear causality between stock prices and oil prices. The results show nonexistence of long run relationship between stock prices and oil prices, but there are both linear and nonlinear Granger causal relationship. Additionally, linear and nonlinear Granger causality tests show significant unidirectional causality from spot oil prices to stock prices. The results support the conservation hypothesis that oil prices lead stock prices, especially for the oil-importing country as in our case. Furthermore, the nonlinear causality test implies the structure breaks caused by some significant economic events which cannot be identified from linearity causality test.

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