1. 漢珍 2008 商學研究與數位資源 應用研討會 分量迴歸的介紹與應用  降息刺激經濟成長？  如何增加公司利潤？  漲時看勢，跌時看質？

2. How Many Years for One SSCI Publication?

4. 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 橫斷面 (cross-section) 資料 : 時間序列 (time series) 資料 :

5. 不同實證結果的爭論 ? 計量模型失靈 ? 計量學家失靈 ? 實證資料不對 ? 瞎子摸象？

6. 不同實證結果的爭論 ? Certain puzzles of the relations between firm profitability and its determinants: –R&D expense –Size effect on the profitability –Debt ratio puzzle

7. 不同實證結果的爭論 ? R&D expense –Branch (1974); Grabowski and Mueller (1978); Reinhard (1985); Guerard, et al. (1987); Brown (1988); Kraft (1989): Chan, et al. (1990); Morbey and Reithner (1990); Sougiannis (1994); Deng, et al. (1999) and Schoenecker and Swanson (2002)

8. 不同實證結果的爭論 ? The effect of size on the profitability – Ferri and Jones (1979), Smith and Watts (1992), Panzar and Willig (1979), Eckard (1990) and Paul (2001) –Williamson (1967), Holmes, et al. (1991); Lever (1996); Chuang (1999); Pull (2003)

9. 不同實證結果的爭論 ? Capital structure (Debt ratio): –Kim (1978), Schneller (1980) and Bradley, et al. (1984) –Titman and Wessels (1988) and Baskin (1989)

10. 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

11. Does the era of linearity and least- squares come to an end? Legendre: –Least squares: –Minimizing sum of squared errors Boscovich: – Least absolute deviation –Minimizing sum of absolute errors Key: “averaging” the errors

12. BoscovichLegendre

13. An Example: Simple Regression

14. An Example: Multiple Regression

15. Regression Line

16. OLS and LAD

17. 平均數具有代表性嗎？ 應用和計算平均數的前提條件是什麼？ – 同質性的分配 – 因為只有是同質性的分配，計算平均數才有意 義

18. The Perspective of Quantile Regression (QR)

20. How to Enhance Firm Profit?

21. Motivations Limitations of OLS and LAD methods –Central behaviors only –Conditional mean Qunantitle Regression (QR) Model –Whole distribution –Conditional distribution

22. Key Question The impacts of the identified determinants of profitability performance on firms are consistent with different levels of firm’s profitability quantiles?

23. Motivations Certain puzzles of the relations between firm profitability and its determinants: –R&D expense –Size effect on the profitability –Debt ratio puzzle

24. Motivations Firm life cycle Adizes (1988): –Business strategies and organizational structures of firms vary according to the problems faced at different life cycle stages of the organization

25. Prior Studies on Life-cycle Theory The concept of corporate life-cycle stage has generated considerable applied interest –Dodge et al. (1994) on the relationship between operation strategies and life cycles – Beldona et al. (1997) and Robinson (1998) on effects of operation strategies on performance at various firm life cycle stages – Kimberley and Miles (1980) and Dodge and Robins (1992) positing that organization structure reflects current life cycle stage – Adizes (1979); Miller and Friesen (1984); Alexander et al. (1993); and Maturi (1999) examining the relationship between CEO leadership styles and life cycle stages – Anthony and Ramesh (1992), Black (1998) and Jorion and Talmor (2001) testing the impact of life-cycle on corporate earnings.

26. Key Limitation of Prior Studies Segment sample companies into various subsets –Use criteria such as earnings and/or age –Apply traditional optimization techniques such as ordinary least squares (OLS) and least absolute deviation (LAD) to fit their subsets

27. Key Limitation of Prior Studies The analytical framework in these studies was based on unconditional distribution of firm samples This form of “truncation of samples” may yield invalid results As demonstrated by Heckman (1979), such methods often exhibit sample selection bias

28. Key Advantage of QR A valid alternative is the quantile regression framework, which segments the sample into subsets defined by conditioning covariates Moreover, in comparison with the least square method, quantile regression offers a relatively rich description of the conditional mean for extreme cases in the samples

29. Empirical Methods No quantile models: OLS and LAD

30. Empirical Methods Quantile regression model

31. Empirical Methods Quantile regression model

32. Data Sample S&P500 firms over the 10-year period from 1996 to 2005 were analyzed Financial firms were excluded Firms were also excluded from the analysis if the specified financial data were not available for the entire 10-year period The final sample included 2,078 firm observations All data were obtained from the Compustat database.

33. Data Return on equity (ROE) was selected as the proxy variable for firm profitability Five determinants of profitability were recorded: –R&D expense –Company size –Debt ratio –Total asset turnover –Current ratio

34. Empirical Results

42. Conclusions and Directions for Future Research A comparative analysis: QR, OLS and LAD estimates The nonlinearities derived from conditional QR: –size, significance and sign It should be noted that certain existing puzzles of the relations between firm profitability and its determinants could be satisfactorily accounted for in this study

43. Conclusions and Directions for Future Research Certain caveats –U.S. S&P 500 firms –Comparisons with models with other dynamic parameter designs –Proxy variable selection –Other determinants of firm profitability –Panel data

44. Application #2: 降息刺激經濟成長？

49. Application #3 漲時看勢，跌時看質？