本文使用1987年至2011年的失業率季資料, 實證探討與台灣貿易往來頻繁之國家的失業率是否具有磁滯效應, 以作為政府制定政策的依據。

　　在一般的情況下時, 傳統的單根檢定(ADF、PP、KPSS、DF-GLS 以及NP) 會出現實證結果不一致的情況, 其可能原因為在小樣本下造成每一個序列(series) 的檢定力(power) 較低, 和未考慮結構性變化(Structural Break) 而使得序列產生型I 誤差的問題(type I error)。Kyung So Im, Junsoo Lee, and Margie Tieslau為改善此現象建構出一套新的追蹤資料單根檢定(ILT test),ILT檢定必須考慮截距項(level) 和趨勢項(trend)的結構性改變, 以及利用Pesaran 的CA 步驟(cross-sectionally augmented), 此方法為考慮每個序列間的相關性, 進而改善傳統單根檢定其檢定力不足的問題。

　　「ILT 檢定」結果顯示, 失業率序列不拒絕(non-reject) 虛無假設, 亦即表示所有國家皆具有磁滯效應(hysteresis effect)。磁滯效應表示結構性改變會對失業率造成持續性的影響, 此時政府必須干預失業並制定相關政策, 使失業率回復至自然失業率。

　　This paper uses the unemployment rate in quarterly data from 1987 to 2011, empirical exploration whether unemployment rate of with Taiwan’s frequent trading countries has a hysteresis effect, as government policy on the basis.

　　In general circumstances, there are different results by using traditional unit root tests (ADF, PP, KPSS, the DF-of GLS and NP). And the possible reasons are getting the lower power of individual series because of the small simples and without considering about the structural break results in the series with problem of type I error. Therefore, in order to solve the problem, Kyung So, Im, Junsoo Lee, and the Margie Tieslau (2010) constructed an unit root test of panel data which needs to consider the structural break of level and trend. Moreover, the correlation between the inter-individual series by using the cross-sectionally augmented (CA) method from Pesaran (2007) also needs to be considered.

　　As the results of panel data unit root tests, series of unemployment rate do not reject the null hypothesis, that is, with hysteresis effect which breaks natural unemployment rate continuously. In this case, the government must set up policies to intervene to recover the unemployment rate.

1 緒論 1
　　1.1 研究動機與目的.. 1
　　1.2 研究架構.. 2
2 文獻回顧 3
　　2.1 單根檢定演進之文獻回顧.. 3
　　2.2 應用在失業率上的單根檢定之文獻回顧.. 5
　　　　2.2.1 國外文獻回顧.. 5
　　　　2.2.2 國內文獻回顧.. 6
3 研究方法 8
　　3.1 單根檢定.. 8
　　　　3.1.1 ADF單根檢定(Augmented Dickey and Fuller, 1984).. 8
　　　　3.1.2 PP單根檢定(Phillips and Perron, 1988).. 9
　　　　3.1.3 KPSS單根檢定(Kwiatkowski, Phillips, Schmide and Shin, 1992) .. 10
　　　　3.1.4 DF-GLS單根檢定(Dickey-Fuller Test with GLS, 1996).. 11
　　　　3.1.5 NP單根檢定(Ng and Perron, 2001) .. 12
　　3.2 ILT單根檢定.. 13
　　　　3.2.1 模型假設.. 13
　　　　3.2.2 單變量型式的估計與檢定.. 14
　　　　3.2.3 統計量的極限分配推導.. 15
　　　　3.2.4 追蹤資料(panel data) 的估計和檢定.. 17
4 實證結果與分析 20
　　4.1 資料來源與分析.. 20
　　4.2 單根檢定實證結果.. 22
　　4.3 新追蹤資料單根檢定實證結果.. 23
5 結論　27
參考文獻　29

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