由靜態到動態之依時拆分--臺灣工業部門實質GDP之按月推估_

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題名：	由靜態到動態之依時拆分--臺灣工業部門實質GDP之按月推估
書刊名：	臺灣經濟預測與政策
作者：	劉瑞文
作者(外文)：	Liou, Ruey-Wan
出版日期：	2007
卷期：	38:1
頁次：	頁75-125
主題關鍵詞：	依時拆分；依時加總；Temporal disaggregation；Temporal aggregation
原始連結：	連回原系統網址
相關次數：	被引用次數:期刊(2) 博士論文(0) 專書(0) 專書論文(0) 排除自我引用:2 共同引用:0 點閱:14

時間數列資料由低頻依時拆分(temporally disaggregate)為高頻的形式，雖不是當下最時髦的計量課題，卻是實務上經常被過到、亟需審慎處理的問題，其重要性不可等閒視之。本文探討10種依時拆分的方法，並劃分成三類：一類是只利用自身低頻數列，如Boot et al.(1967)；另一類則是輔以相關高頻參考指標數列，如Denton (1971)、Ginsburgh (1973)、Guerrero (1990)、chow and Lin (1971)、Fernández (1981)、Litterman (1983)、 Salazar et al.(1997a, 1997b)、Santos Silva and Cardoso (2001)等；第三類則是應用狀態空間(State-space)法，如Harvey and Pierse (1984)、Harvey (1989)以及Moauro and Savio (2005)。其中特別值得弦調的是，傳統依時拆分在設定模型時，礙於變數取對數後加總不等於變數加總後再取對數，對所要推估之高頻變數並不取其對數，也不考慮自我遞延項的影響，雖其拆分過程簡便，但可能引起變異數不齊一問題，而且模型停留在靜態的形式。Salazar et al. (1997a, 1997b)及Santos Silva and Cardoso (200l)引人入勝之處為導入動態設定，前者更巧妙地利用泰勒展開法化解對數加總的障礙，使其模型更符合當代計量理論之要求。對於前9種方法，本文將依序套用，對行政院主計處按季編布之工業部門實質GDP數列依時拆分為按月的形式，並做比較。

以文找文

This paper reviews ten kinds of universally applied temporal disaggregation methods. These ten methods can be classified into three categories. The first uses the unique information of the low-frequency series during disaggregation, e.g., Boot, Feibes and Lisman (1967); The second requires the help of related high-frequency indicators to disaggregate the low-frequency data, such as Denton (1971), Ginsburgh (1973), Guerrero (1990), Chow and Lin (1971), Fernández (1981), Litterman (1983), Salazar et al. (1997a, 1997b), Santos Silva and Cardoso (2001), etc. The last utilizes the state-space approach like Harvey and Pierse (1984), Harvey (1989), and Moauro and Savio (2005). A crucial aspect worthy of attention is that the traditional static disaggregation method may lead to a heteroscedasticity problem, because the dependent variable is repressed in level form. The major reason is that the logarithms of high-frequency estimates do not add up to the logarithm of their aggregate. Salazar et al.(1997a, 1997b) and Santos Silva and Cardoso (2001) first introduce the dynamic structure linking the indicator variables to the interpoland. Salazar et al. also employ Taylor's approximation to tackle the adding-up obstacle of the logarithm transformation. In the paper, I'll utilize the first nine methods to derive the monthly real GDP estimate of the industrial sector for Taiwan and compare the results.

以文找文

期刊論文
1.	Chow, G. C.、Lin, A. L.(1971)。Best Linear Unbiased Interpolation, Distribution, and Extrapolation of the Time Series by Related Series。The Review of Economics and Statistics，53(4)，372-375。
2.	Ginsburgh, V. A.(1973)。A Further Note on the Derivation of Quarterly Figures Consistent with Annual Data。Applied Statistics，22(3)，368-374。
3.	Stram, D. O.、Wei, W. W. S.(1986)。A Methodological Note on the Disaggregation of Time Series Totals。Journal of Time Series Analysis，7(4)，293-302。
4.	Fernández, R. B.(1981)。A Methodological Note on the Estimation of Time Series。The Review of Economics and Statistics，63(3)，471-478。
5.	Salazar, E. L.、Smith, R. J.、Weale, M. R.、Wright, S.(1997)。A Monthly Indicator of GDP。National Institute Economic Review，161，84-89。
6.	Litterman, R. B.(1983)。A Random Walk, Markov Model for the Distribution of Time Series。Journal of Business & Economic Statistics，1(2)，169-173。
7.	Denton, F. T.(1971)。Adjustment of Monthly or Quarterly Series to Annual Totals: An Approach Based on Quadratic Minimization。Journal of the American Statistical Association，66(333)，99-102。
8.	Wei, W. W. S.、Stram, D. O.(1990)。Disaggregation of Time Series Model。Journal of the Royal Statistical Society, Series B: Methodological，52(3)，453-467。
9.	Mitchell, J.、Smith, R. J.、Weale, M. R.、Wright, S.、Salazar, E. L.(2005)。An Indicator of Monthly GDP and an Early Estimate of Quarterly GDP Growth。The Economic Journal，115(501)，108-129。
10.	Tiao, G. C.、Wei, W. S.(1976)。Effect of Temporal Aggregation on the Dynamic Relationship of Two Time Series Variables。Biometrika，63(3)，513-523。
11.	Harvey, A. C.、Pierse, G. R.(1984)。Estimating Missing Observations in Economic Time Series。Journal of the American Statistical Association，79(385)，125-131。
12.	Boot, J. C. G.、Feibes, W.、Lisman, J. H. C.(1967)。Further Methods of Derivation of Quarterly Figures from Annual Data。Applied Statistics，16(1)，65-75。
13.	Abeysinghe, T.(2000)。Modeling Variables of Different Frequencies。International Journal of Forecasting，16(1)，117-119。
14.	Stram, D. O.、Wei, W. W. S.(1986)。Temporal Aggregation in the ARIMA Process。Journal of Time Series Analysis，7(4)，279-292。
15.	Guerrero, V. M.(1990)。Temporal Disaggregation of Time Series: An ARIMA-based Approach。International Statistical Review，58(1)，29-46。
16.	Di Fonzo, T.(2003)。Temporal Disaggregation Using Related Series: Log-transformation and Dynamic Extension。RISEC，50(3)，371-400。
17.	Moauro, F.、Savio, G.(2005)。Temporal Disaggregation Using Multivariate Structural Time Series Models。Econometrics Journal，8(2)，214-234。
18.	Santos Silva, J. M. C.、Cardoso, F. N.(2001)。The Chow-lin Method Using Dynamic Models。Economic Modelling，18(2)，269-280。

會議論文
1.	Abeysinghe, T.、Tay, A. S.(2000)。Dynamic Regressions with Variables Observed at Different Frequencies。0。

研究報告
1.	Salazar, E. L.、Smith, R. J.、Weale, M. R.(1997)。Interpolation Using a Dynamic Regression Model: Specification and Monte Carlo Properties。0。

圖書
1.	Eurostat(1999)。Handbook on Quarterly National Accounts。Handbook on Quarterly National Accounts。Luxembourg：Office for Official Publications of the European Communities。
2.	Harvey, A. C.(1989)。Forecasting, Structural Time Series Models and the Kalman Filter。Cambridge：Cambridge University Press。
3.	Di Fonzo, T.(2003)。Temporal Disaggregation of Economic Time Series: Towards a Dynamic Extension。Temporal Disaggregation of Economic Time Series: Towards a Dynamic Extension。Luxembourg。

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