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計量經濟學還能做什麼 ? 管中閔 中央研究院經濟研究所. 計量模型與方法的應用 分析並呈現資料的特性 解釋過去的經濟現象 預測未來 追蹤資料 (panel) 資料 : 資料 家庭收支調查 橫斷面 (cross-section) 資料 : 時間序列 (time series) 資料 : 總體經濟變數.

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Presentation on theme: "計量經濟學還能做什麼 ? 管中閔 中央研究院經濟研究所. 計量模型與方法的應用 分析並呈現資料的特性 解釋過去的經濟現象 預測未來 追蹤資料 (panel) 資料 : 資料 家庭收支調查 橫斷面 (cross-section) 資料 : 時間序列 (time series) 資料 : 總體經濟變數."— Presentation transcript:

1 計量經濟學還能做什麼 ? 管中閔 中央研究院經濟研究所

2 計量模型與方法的應用 分析並呈現資料的特性 解釋過去的經濟現象 預測未來 追蹤資料 (panel) 資料 : 資料 家庭收支調查 橫斷面 (cross-section) 資料 : 時間序列 (time series) 資料 : 總體經濟變數 華人家庭動態調查

3 GDP 成長率 主計處 中研院經濟所 5.21 (2.38*) (1.06*) (0.73)(2.52)(5.08) 台灣經濟研究院 6.01* (4.75) (3.33)(3.91)(5.14)(6.53) 中華經濟研究院 Before s QI 2001 QII 2001 QIII 2001 QIV 年預測值

4 經濟預測為何如此離譜 ? 計量模型失靈 ? 計量學家失靈 ? 計量經濟學整個失靈 ? Who cares?

5 計量方法的演變 Specification Y i =β 1 +β 2 X 2i +…+ β k X ki + e i,i = 1,…,N linear model  nonlinear model  nonparametrics least squares (least absolute deviation), QMLE, GMM, computationally intensive methods, etc. single equation  multiple equations (simultaneous-equation model, VAR) Method Structure

6 Does the era of linearity and least-squares come to an end? Legendre (1798): “balance the errors in such a way that they are borne equally by all equations” Legendre (1805): “distributing the errors among the equations” Minimizing sum of squared errors Least squares: Euler  Laplace  Legendre Key: “averaging” the errors Least absolute deviation:Boscovich, 1755 Minimizing sum of absolute errors

7 BoscovichLegendre

8 When a random structure is imposed We are interested in the random behavior of Y, given the information contained in X 2,…,X k. Least squares: β 1 +β 2 X 2 +…+ β k X k as the conditional mean function. Least absolute deviation: β 1 +β 2 X 2 +…+ β k X k as the conditional median function. Both describe the “averaging” behavior of Y

9 Other Aspects of Conditional Distribution Conditional variance: volatility models Conditional higher moments Conditional quantiles ( 分位數, 分量 )

10 Quantile Regression ( 分量迴歸 ) Koenker and Bassett (1978) For 0 < θ < 1, minimize weighted sum of absolute errors: β 1 +β 2 X 2 +…+ β k X k now can characterize the tail behaviors of Y. β 1 …,β k vary with θ ; compare the behaviors with different θ (e.g. θ =0.05 vs. θ =0.95).

11 Example 1: (Koenker and Hallock, 2001) A study of infant birth weights For a girl born to an unmarried, white mother with below-high-school education Least squares: 3350g 5% quantile: 2500g;95% quantile 4100g Difference between boys and girls Least squares: 100g 5% quantile: 45g;95% quantile: 130g Difference between black and white mothers Least squares: -200g 5% quantile: -330g;95% quantile: -175g

12 Other Applications Economic growth of bad years Value at Risk ( 涉險值 ) Wage differentials Demand analysis High risk group of an insurance policy Productivity of firms Schooling years

13 Example 2: Index of Financial Crisis Eichengreen et al. (1996) EMP t =  e t +  i t -  r t Crisis t = 1,if EMP t >  + n  Quantile Regression EMP on important financial and economic variables Focusing on the quantiles of the right tail

14 A Summary Least squares and linear models will still prevail. After almost 200 years, we may start paying more attention to other methods. We may now go beyond the models for conditional mean (median) and study aberrant behaviors.

15 Do we really need a large system of equations? From one variable to many variables Single equation model  Simultaneous-equation model (Cowles Commission since 50’s ) Univariate time-series model  multivariate model (VAR; Sims, 1980)

16 Drawbacks of a large system Simultaneous-equation model Model identification Model estimation: Estimating each equation by LS is meaningless VAR Too many parameters Difficult to impose prior restrictions Others Data must be of the same frequency (e.g. quarterly) Model specification error

17 A large information set but not necessary a large system Include as many economic variables as possible. Extract information from these variables without specifying many models. Adopt data dimension reduction techniques (e.g. principal component analysis).

18 Example 1: Diffusion Index (Stock and Watson, 1998) NBER Diffusion indexes as dynamic factors Reduce a large information set to a small number of factors. No parametric model is specified. Can handle mixed-frequency data (e.g. monthly and quarterly).

19 A Preliminary Try 2001 QI 2001 QII 2001 QIII 2001 QIV QI 2002 QII 2002 QIII 實際值 預測 Q Q Q Q Q Q Q

20 Example 2: Multivariate Volatility model Dynamics of conditional variances Dynamics of conditional correlations How can we capture these dynamic patterns without specifying a complex model?

21 A Summary Single-equation models and small systems will still prevail. A very large system need not work better. Utilizing all the information efficiently is an important but challenging task.

22 結論 計量分析的結果是「從數字上管理」的 重要基礎。 計量方法與時俱進;我們不應囿於舊觀 念與舊作法。 計量方法常有其侷限性,但這些侷限正 好是促使我們繼續發展更有效方法的動 機。

23 Don’t just ask what econometrics can do for you; Ask what you can do for econometrics.

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