臺灣博碩士論文加值系統

由於公司提列資產減損額度為設限資料(censoring data)，過去的研究者均以Tobit模式分析，此模式受限於假設決策者在預估是否提列資產減損及減損額度的大小皆以相同形式的模式來考量；然不同決策間有其不同的決策模式，此無差異性的決策模式是否合於實際決策者或決策情境令人質疑。目前計對於設限資料的處理除Tobit模式外，尚有不同假設的模式，如two-part模式（分別處理選擇及及觀察到提列之額度模式）及sample selection模式（除分別考慮選擇模式與額度模式規格外，進一步考慮兩模式誤差間的相關性）。本研究分別應用上述三種模式，分析我國最近之資產減損提列的資料，並比較分析結果，以探究我國上市櫃公司管理當局提列資產減損的可能決策形式。
文中應用2005年至2008年上市(櫃)公司的資產減損額度資料，首先分析two-part模式及sample selection模式於此問題的適用性；再進一步與Tobit模式比較預測績效。實證結果顯示，two-part模式比sample selection模式較適合於目前資產減損額度的樣本；對於預測績效則以Tobit模式最差，two-part模式略優於sample selection模式；此結果可提供診斷資產減損分析者參考。而two-part模式較接近目前分析的樣本，顯示國內在決定提列資產減損與該提多少額度的考量上相關性很低。

Since the sample of assets write-off magnitude is censoring data type, most of previous researches used Tobit model for the analysis. The model is limited by the assumption that uses same specification for both assets write-off decision model and magnitude regression model. Nevertheless, it is in doubt that whether the indiscriminate decision mode will agree to the actual behavior of the decision makers or even the real scenarios. In addition to the Tobit model for the censoring data analysis, there are other models that relax single model specification limit, such as two-part model (considers selection model specification and observed magnitude model specification separately) and sample selection model (beside different model specifications, also considers correlation between errors distributions of the models). In this work, collected samples of listed companies in Taiwan from year 2005 to year 2008 are used to study the fitness of two-part model and sample selection model for assets write-off and compare the prediction performances of these models to the result of Tobit model. The empirical results demonstrate that two-part model is more suitable than sample selection model in the assets write-off sample. For the prediction performance, the result of Tobit model is the worst and the result of two-part model is slightly superior to the sample selection model.

目 錄
中文摘要I
Abstract III
目錄IV
圖表目錄VI
第一章 緒論1
第一節 研究動機與目的1
第二節 研究架構與流程3

第二章 文獻探討5
第一節 我國35號公報資產減損之介紹5
第二節 資產減損相關文獻探討 7
第三節 設限資料模型與應用12

第三章 研究方法20
第一節 樣本選取與資料來源20
第二節 變數定義22
第三節 實證模型建立27
第四節 預測績效指標29

第四章 實證分析32
第一節　樣本敘述統計量 32
第二節　Two-part模式與sample selection模式比較 34
第三節　模式預測比較 50

第五章　結論54
參考文獻57

圖表目錄
表目錄
表1 資產減損相關文獻彙總表10
表2 樣本統計20
表3 產業與減損家數分佈表21
表4 各變數定義彙總表26
表5 模式績效評估指標定義31
表6 相關變數敘述性統計量33
表7 Two part與sample selection(2-step)模式參數估計(case 1)36
表8 Two part與sample selection(ML)模式參數估計(case 1)37
表9 以 EMSEP比較SS(2-step) 及2PM的模式傾向(case 1)39
表10 以 EMSEP比較 SS(ML) 及2PM的模式傾向(case 1)40
表11 Two-part 與sample selection(2-step)模式參數估計(case 2)43
表12 Two-part 與sample selection(ML)模式參數估計(case 2)44
表13 以 EMSEP比較 SS(2-setp) 及 2PM的模式傾向(case 2)45
表14 以EMSEP比較 SS(ML) 及 2PM的模式傾向(case 2)45
表15 Two-part sample selection(2-step)模式參數估計(case 3)47
表16 以EMSEP比較SS(2-step)及2PM的模式傾向(case 3)48
表17 Two-part sample selection(ML)模式參數估計(case 3)49
表18 以EMSEP比較SS(ML) 及 2PM的模式傾向(case 3)49
表19 資產減損額度預測績效51
表20 資產減損額度預測績效比較p-value51
表21 資產減損額度預測績效(調整設限模式規格後)52
表22 資產減損額度預測績效比較p-value(調整設限模式規格)52
表23 對數額度比較轉回額度比率的績效53
圖目錄
圖1研究流程圖4

林有志與廖宜鋒，2006，提前採用資產減損公報之公司特性及盈餘管理動機，文大商管學報，第11卷第1期：11-28。
武季蔚、陳慶隆與莊豐榮，2008，應用多層次迴歸模型於我國上市(櫃)公司資產減損幅度之預測，第九屆朝陽管理學域學術研討會，台中，朝陽科技大學主辦。
財團法人中華民國會計研究發展基金會，2004，財務會計準則公報第35號公報:資產減損之會計處理準則，台北，財務會計準則委員會。
張文瀞、汪瑞芝與林鴻儒，2008，未分配盈餘加徵10%營利事業所得稅對企業提前適用財務會計準則公報第35號之影響，會計評論，第47期：61-88。
陳慶隆，2007，選擇性新會計準則實施時點對策略性會計報導與資訊攸關性的影響，臺灣管理學刊，第7卷第1期：131-159。
謝宛庭與吳清在，2005，認列資產減損時點與金額之決定因素及其市場反應，中華會計學刊，第六期：59-95。
Belsley, D. A., E. Kuh, and R. E. Welsch. 1980. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York, NY: John Wiley & Sons.
Berndt, E. K, B. H. Hall, R. E. Hall, and J.A. Hausman. 1974. Estimation and inference in nonlinear structural models. Annals of Economic and Social Measurement 3(4):653-665.
Broyden, C. G. 1970. The convergence of a class of double-rank minimization algorithms. Journal of the Institute of Mathematics and Its Applications 6:76-90
Cameron, A.C. and Trivedi, P.K. 2005. Microeconometrics. Methods and Applications, Cambridge University Press.
Carn, N., J. Rabianski, R. Racster and M. Seldin 1998. Real estate market analysis - techniques and applications. Prentice-Hall.
Chia, F. 1995. An investigation into the causes and effects of asset write-offs in Australia. PhD dissertation, University of Western Australia
Cotter, J., Stokes, D. and Wyatt, A. 1998. An analysis of factors influencing asset writedown. Accounting and Finance 38(2):157-179.
Dow, W. H., and E. C. Norton. 2003. Choosing between and interpreting the heckit and two-part models for corner solutions. Health Serv. Outcomes Res. Methodol 4(1):5-18.
Duan, N., W. G. Manning, C. N. Morris, and J. P. Newhouse. 1983. A comparison of alternative models for the demand for medical care. Journal of Business and Economic Statistics 1(2):115-126.
Duan, N., W. G. Manning, C. N. Morris, and J. P. Newhouse. 1984. Choosing between the sample-selection model and the multi-part model. Journal of Business and Economic Statistics 2(3):283-289.
Elliott, J. and W. Shaw. 1988. Write-offs as accounting procedures to manage perceptions. Journal of Accounting Research 26(3):91-119.
Fletcher, R. 1970.A new approach to variable metric algorithms. Computer Journal 13: 317-322.
Francis, J., J. Hanna and K. Vicent. 1996. Causes and effects of discretionary asset write-offs. Journal of Accounting Research 34(3):117-134.
Goldfarb, D. 1970. A family of variable metric updates derived by variational means. Mathematics of Computation 24 (109):23-26.
Heckman, J. J. 1979. Sample selection as a specification error. Econometrica 47:153-161.
Heflin, E., and T. Warfield. 1997. Managerial discretion on accounting for asset write-offs. Working paper, University of Wisconsin-Madison.
Leung, S. F. and S. Yu. 1996. On the choice between sample selection and two-part models. Journal of Econometrics 72(1-2):197-229.
Loh, A. L. C. and T. H. Tan. 2002. Asset write-offs -managerial incentive and macroeconomic factors. ABACUS 38(1):134-151.
Madden, D. 2008.Sample selection versus two-part models revisited: The case of female smoking and drinking. Journal of Health Economics 27(2): 300-307.
Manning, W. G., N. Duan, and W. H. Rogers. 1987. Monte Carlo evidence on the choice between sample selection and two-part models. Journal of Econometrics 35(1):59-82.
Norton, E. C., H. D. William, and K. D. Young. 2008. Specification tests for the sample selection and two-part models. Health Services Outcomes Research Methodol 8(4):201-208.
R Development Core Team. 2009. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
Reber B, B. Berry and S. Toms. 2005. Predicting mispricing of initial public offerings. Intelligent Systems in Accountin. Finance and Management, 13 (2005):41-59.
Rees, L., S. Gill, and R. Gore. 1996. An investigation of asset write-downs and concurrent abnormal accruals. Journal of Accounting Research 34:157-169.
Riedl, J. 2004. An examination of long-lived asset impairment. Accounting Review 79(3):823-852.
Shanno, D. F. 1970. Conditioning of quasi-newton methods for function minimization . Mathematics of Computation 24:647-656
Strong, J. and J. Meyer 1987. Asset Write downs: Managerial Incentives and Security Returns, Journal of Finance, 42:643-662.
Therneau, T and original R port by T. Lumley. 2009. Survival: Survival analysis, including penalised likelihood.. R package version 2.35-8. http://CRAN.R-project.org/package=survival
Toomet, O. and A. Henningsen. 2008. Sample selection models in R: Package sample selection. Journal of Statistical Software 27(7). URL http://www.jstatsoft.org/v27/i07/.
Zucca, L. and D. Campbell. 1992. A closer look at discretionary write downs of impaired assets. Accounting Horizons 6(3):30-41.