In financial economics studies, the autoregressive model has been a workhorse for a long time. However, the model has a fixed value on every parameter and requires the stationarity assumptions. Time-varying coefficient autoregressive model that we use in this paper offers some desirable benefits over the traditional model such as the parameters are allowed to be varied over-time and can be applies to nonstationary financial data. This paper provides the Monte Carlo simulation studies which show that the model can capture the dynamic movement of parameters very well, even though, there are some sudden changes or jumps. For the daily data from January 1, 2015 to February 12, 2020, our paper provides the empirical studies that Thailand, Taiwan and Tokyo Stock market Index can be explained very well by the time-varying coefficient autoregressive model with lag order one while South Korea’s stock index can be explained by the model with lag order three. We show that the model can unveil the non-linear shape of the estimated mean. We employ GJR-GARCH in the condition variance equation and found the evidences that the negative shocks have more impact on market’s volatility than the positive shock in the case of South Korea and Tokyo.

In this study the effect caused by limited storage lift of agricultural products for determining shipping amount can be analyzed by lst order autoregressive model based on cobweb theorem. Carrying capacity and auction price of upland-grown cabbage and garlic from 2000 to 2003 in wholesale markets were used for analysis. In result regression models of cabbage can not be used in verification periods although those of garlic approximately predicted shipping amounts in verification periods. It can be inferred that it is hard to control shipping amounts depending on price fluctuation for agricultural products which have limited storage life so cultivated areas and meteorological risk should be managed for stable price.