系列二
時間：1/5(一) 15:00-17:00
主持人：嶺東科技大學財經學院 楊永列 院長
演講者：逢甲大學經濟學系 方文碩 教授
講題：Modeling the Volatility OF Real GDP Growth The Case of Japan Revisited
摘要：
Previous studies [e.g., Hamori, S., 2000. Volatility of real GDP: some evidence from the United States, the United Kingdom and Japan. Japan and the World Economy 12, 143–152; Ho, K.Y., Tsui, A.K.C., 2003. Asymmetric volatility of real GDP: some evidence from Canada, Japan, the United Kingdom and the United States. Japan and the World Economy 15, 437–445; Fountas, S., Karanasos, M., Mendoza, A., 2004. Output variability and economic growth: the Japanese case. Bulletin of Economic Research 56, 353–363] find high volatility persistence of economic growth rates using generalized autoregressive conditional heteroskedasticity (GARCH) specifications. This paper reexamines the Japanese case, using the same approach and showing that this finding of high volatility persistence reflects the Great Moderation, which features a sharp decline in the variance as well as two falls in the mean of the growth rates identified by Bai and Perron’s [Bai, J., Perron, P., 1998. Estimating and testing linear models with multiple structural changes. Econometrica 66, 47–78; Bai, J., Perron, P., 2003. Computation and analysis of multiple structural changemodels. Journal of Applied Econometrics 18, 1–22] multiple structural change
test. Our empirical results provide new evidence. First, excess kurtosis drops substantially or disappears in the GARCH or exponential GARCH model that corrects for an additive outlier. Second, using the outlier-corrected data, the integrated GARCH effect or high volatility persistence remains in the specification once we introduce intercept-shift dummies into the mean equation. Third, the Timevarying variance falls sharply, only when we incorporate the break in the variance equation. Fourth, the ARCH in mean model finds no effects of our more correct measure of output volatility on output growth or of output growth on its volatility.
檔案連結：方文碩教授.pdf
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系列一
時間：1/5(一) 9:30-12:00
主持人：嶺東科技大學財經學院 楊永列 院長
演講者：東海大學財務金融學系 王凱立 副教授
講題：1.時間序列模型於外匯市場之應用 2.教學研究經驗分享
摘要：
‧時間序列變數通常可分為定態/恆定(stationary)和非定態/非恆定(Nonstationary)
兩種數列。
‧一個定態數列對於任何外在衝擊僅具暫時性影響，亦即該變數受到干
擾後又會返回其平均值。反之，若經由隨機過程所產生的機率分配會隨時
間的變動而改變，則稱此一數列為非定態(nonstationary)之時間數列，且對
於外在衝擊有累積的效果，促使讓變數於時間演變過程中逐漸偏離其平均
值。
‧如果直接進行非定態變數迴歸分析，將造成Granger and Newbold (1974)
所謂的虛假迴歸(Spurious Regression)發生，因為過度拒絕虛無假設，致使
估計結果不具意義。因此在採用資料來進行分析前，必須保證資料為恆定
(Stationary)數列。
檔案連結：王凱立教授.pdf
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