• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.173-179
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• 2006

In order to explain a U-shape pattern of stock returns, Fama and French(1988) suggested the state-space model consisting of I(1) permanent component and AR(1) stationary component. They concluded the autoregression coefficient induced from the state-space model follow the U-shape pattern and the U-shape pattern of stock returns was due to both negative autocorrelation in returns beyond a year and substantial mean-reversion in stock market prices. However, we found negative autocorrelation is induced under the assumption that permanent and stationary noise component are independent in the state-space model. In this paper, we derive the autoregression coefficient based on ARIMA process equivalent to the state-space model without the assumption of independency. Based on the estimated parameters, we investigate the pattern of the time-varying autoregression coefficient and conclude the autoregression coefficient from the state-space model of ARIMA(1,1,1) process does not follow a U-shape pattern, but has always positive sign. We applied this result on the data of 1 month retums for all NYSE stocks for the 1926-85 period from the Center for Research in Security Prices.

• Journal of the Korean Statistical Society
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• v.26 no.3
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• pp.383-395
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• 1997

This paper proves that the standard bootstrap approximation for the least absolute deviation (LAD) estimate of .beta. in AR(1) processes with infinite variance error terms is asymptotically valid in probability when the bootstrap resample size is much smaller than the original sample size. The theoretical validity results are supported by simulation studies.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.33-42
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• 2021

This study aims to investigate the relationship between foreign direct investment (FDI) and economic growth at the provincial level by using time-series data in Binh Dinh from 1997 to 2019. We applied the quantitative approaches Vector Autoregression (VAR) and Autoregressive Distributed Lags (ARDL) in the model, which includes economic growth, real foreign direct investment capital, ratio of trained workers, and infrastructure. The results show that all these variables are stationary at the first difference. In ARDL analysis, we found that the economic growth positively affects FDI attraction. However, there is no evidence of the effect of FDI on economic growth in the condition of low capital implemented. Moreover, findings also show that the impact of FDI on economic growth is influenced by two factors: infrastructure and human capital. The lack of human capital, which is trained personnel and infrastructure, is the main barrier hindering and inhibiting FDI's contribution to local economic growth. In order to improve the efficiency of FDI on economic growth in the future, it is suggested that the Binh Dinh government should have proper policies in terms of the infrastructure, the human capital investment. They would allow Binh Dinh to enhance the capital absorptive capacity and capital efficiency.