博碩士論文 92424018 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:47 、訪客IP:18.219.200.80
姓名 程嵩硯(Sung-Yan Cheng)  查詢紙本館藏   畢業系所 產業經濟研究所
論文名稱 經驗概似法之理論與蒙地卡羅模擬
(Empirical Likelihood: Theories and Monte Carlo Simulations)
相關論文
★ 上市公司財務主管異動宣告對股價報酬與企業經營績效之影響★ 巢式與非巢式資產定價理論之比較與檢定
★ 小數化、市場流動性與交易時距★ 套利訂價模型中未知因子之分析:全球實證研究
★ Copula-based GARCH模型於期貨避險之應用★ 消費財富效果不對稱分析: 馬可夫轉換共同趨勢模型之應用
★ 股票市場報酬與波動性外溢效果分析★ 中國大陸勞動合同法與企業所得稅法對台商的衝擊與因應
★ 結構FAVAR模型與台灣貨幣政策分析★ 通貨膨脹率預測:考慮結構變動之動態因子模型應用
★ 匯率因子與市場基要之預測表現★ 台灣大小公司報酬與流動性之實證研究
★ 台灣外匯暨股票市場流動性與景氣循環關係之探討★ 台灣經濟成長率之混合頻率預測-MIDAS迴歸應用
★ 油價對匯率預測能力之分析★ 企業組織再造之分析-以某醫材業公司為例
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 經濟理論的動態最適化架構了所謂的動差條件模型。一般化動差估計法~(GMM), 是目前文獻上相當普遍使用的動差條件估計技術。 然而,在許多的模擬與實証研究已發現, GMM 在小樣本的表現上不盡理想: 點估計式存在著嚴重的偏誤~(bias),以及相關的檢定統計量具有相當的型一誤差扭曲現象。 在本篇論文當中,我們將探討一個由無母數概似法所發展出來的方法,empirical likelihood 架構於動差條件模型之使用。 本文主要有底下兩個特色: 在動差條件模型的架構之下, 一、 我們完整的闡釋了 empirical likelihood 估計與統計推論的理論性質, 並且我們的討論重點主要將著眼於與既有的 GMM 估計式做比較性探討。 二、 透過蒙地卡羅模擬,我們試驗了幾種計量模型,討論 empirical likelihood 點估計式與其相關的檢定在小樣本上的表現,並與文獻上其他各種的動差條件估計式做比較分析。 從我們大部分的模擬結果可發現,傳統上的 GMM 估計式並不能提供令人滿意的小樣本表現; empirical likelihood 估計式可以提供相當準確的小樣本點估計與較可信的統計推論結果。
摘要(英) Moment condition models arise naturally from the dynamic economic theory with optimizing agents. The generalized method of moments (GMM) estimation proposed by Hansen (1982) has been a popular estimation technique for moment condition models in the literature. However, many Monte Carlo and empirical evidences found that the GMM estimator may be severely biased and the associated tests may have substantial size distortions in small samples. In this thesis, we explore a method originally developed in nonparametric likelihood framework. The usefulness of the empirical likelihood estimation and inferences are investigated under unconditional moment condition models. In particular, we focus on the over-identified moment condition models. Two emphases are comprehended in the thesis. First, we clarify the theoretical aspects of empirical likelihood, including both estimation and tests. Our emphasis is specifically put on the comparisons with the conventional GMM framework. Second, using Monte Carlo simulations we examine the small-sample performances of the empirical likelihood estimator and compare with several competitive estimators in different well-known econometric models. In most of our Monte Carlo experiments, we confirm the poor small-sample performances of the conventional GMM estimator, and the empirical likelihood estimator can provide less biased estimates and more reliable inferences in small samples.
關鍵字(中) ★ 經驗概似法
★ 蒙地卡羅
★ 動差條件模型
★ 一般化經驗概似法
★ 工具變數
★ 小樣本性質
★ 一般化動差法
關鍵字(英) ★ small sample properties
★ empirical likelihood
★ Monte Carlo
★ generalized empirical likelihood
★ instruments
★ GMM
★ moment condition models
論文目次 1 Introduction 1
2 Conventional Method of Moments Estimation 5
2.1 Generalized Method of Moments Estimator . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 The Problems of Efficient GMM Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Empirical Likelihood Estimation 11
3.1 Maximum Empirical Likelihood Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.2 Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.2.1 Data-Driven Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.2.2 Invariance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.2.3 Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.2.4 Computational Aspect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3 Asymptotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3.1 First-Order Asymptotic Equivalence . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3.2 Higher-Order Asymptotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.4 Generalized Empirical Likelihood Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.4.1 Other Interpretations and Generalization . . . . . . . . . . . . . . . . . . . . . . . 26
3.4.2 Generalized Empirical Likelihood Estimator . . . . . . . . . . . . . . . . . . . . . 30
4 Statistical Inferences 35
4.1 Overidentifying Restrictions Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.1.1 Empirical Likelihood Ratio Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.1.2 Average-Moment Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.1.3 Lagrange Multiplier Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.2 Tests of Parameter Restrictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5 Stationary Dependent Processes 45
5.1 A Difficulty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.2 Blockwise Empirical Likelihood Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5.3 Kernel Smoothing Sample Counterparts . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
6 Monte Carlo Simulations 53
6.1 Simultaneous Equations Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
6.2 Dynamic Panel Data Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
6.3 Time Series Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
7 Concluding Remarks 73
7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
7.2 Suggestions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
References 76
參考文獻 [1] Altonji, J.G., and L.M. Segal, 1996, Small-Sample Bias in GMM Estimation of Covariance
Structures," Journal of Business and Economic Statistics, 14, 353{366.
[2] Anatolyev, S., 2005, GMM, GEL, Serial Correlation and Asymptotic Bias," Econometrica, 73,
983{1002.
[3] Andrews, D.W.K., 1991, Heteroskedasticity and Autoclrrelation Consistent Covariance Matrix
Estimation," Econometrica, 59, 817{858.
[4] Akkeren, M., G. Judge, and R.C. Mittelhammer, 2002, Generalized Moment Based Estimation
and Inference," Journal of Econometrics, 107, 127{148.
[5] Arellano, M., and S. Bond, 1991, Some Tests of Speci cation for Panel Data: Monte Carlo
Evidence and an Application to Employment Eequations," Review of Economics and Statistics,
58, 277{298.
[6] Bera, A.K., and Y. Bilias, 2002, The MM, ME, ML, EL, EF and GMM approaches to estimation:
a synthesis," Journal of Econometrics, 107, 51{86.
[7] Blundell, R.W., and S.R. Bond, 1998, Initial Conditions and Moment Restrictions in Dynamic
Panel Data Models," Journal of Econometrics, 87, 115{143.
[8] Bond, S., C. Bowsher, and R. Windmeijer, 2001, Criterion-based Inference for GMM in Autoregressive
Panel Data Models," Economic Letter, 73, 379{388.
[9] Bond, S., and F. Windmeijer, 2005, Reliable Inference For GMM Estimator? Finite Sample
Properties of Alternative Test Procedures in Linear Panel Data Models," Econometric Reviews,
24, 1{37.
[10] Bound, J., D. Jaeger, and R. Baker, 1995, Problems with Instrumental Variable Estimation
When The Correlation Between The Instruments and The Endogenous Variables IsWeak," Journal
of the American Statistical Association, 90, 443{450.
[11] Burnside, C., and M. Eichenbaum, 1996, Small-Sample Properties of GMM-Based Wald Tests,"
Journal of Business and Statistics, 14, 294{308.
77
[12] Bravo, F., 2005, Blockwise Empirical Entropy Tests for Time Series Regressions," Journal of
Time Series Analysis, 26, 185{210.
[13] Brown, B.W., and W.K., Newey, 1998, E cient Semiparametric Estimation of Expectations,"
Econometrica, 66, 453{464.
[14] Brown, B.W., and W.K. Newey, 2002, Generalized Method of Mements, E cient Bootstrapping,
and Improved Inference," Journal of Business and Economic Statistics, 20, 507{517.
[15] Chamberlian, G., 1987, Aaymptotic E ciency In Estimation with Conditional Moment Restrictions,"
Journal of Econometrics, 34, 305{334.
[16] Corcoran, S.A., 1998, Bartlett Adjustment of Empirical Discrepancy Statistics," Biometrika, 85,
967{972.
[17] Cressie, N., and T. Read, 1984, Multinomial Goodness-of-Fit Tests," Journal of the Royal
Statistical Society, Series B, 46, 440{464.
[18] Carlstein, E., 1986, The Use of Subseries Methods for Estimating The Variance of A General
Statistic from A Stationary Time Series," Annals of Statistics, 14, 1171{1179.
[19] Chen, N.H., and S. Ling, 2006, Empirical Likelihood for GARCH Models," Econometric Theory,
22, 403{428.
[20] Davidson, R., and J.G. MacKinnon, 2004, Econometric Theory and Methods," Oxford University
Press.
[21] DiCiccio, T.J., and J.P. Ramano, 1990, Nonparametric Con dence-Limits by Resampling Methods
and Least Favorable Families," International Statistical Review, 58, 59{76.
[22] Donald, S.G., G.W. Imbens, and W.K. Newey, 2002, Choosing The Number of Instruments for
GMM and GEL Estimators," Working Paper, Department of Economics, MIT.
[23] Donald, S.G., G.W. Imbens, and W.K. Newey, 2003, Empirical Likelihood Estimation and
Consistent Tests with Conditional Moment Restrictions," Journal of Econometrics, 117,55{93.
[24] Donald, S.G., and W.K., Newey, 2000, A Jackknife Interpretation of the Continuous Updating
Estimator," Economic Letters, 67, 239{243.
[25] Doran, H.E., and P. Schmidt, 2005, GMM estimators with improved nite sample properties
using principal components of the weighting matrix, with an application to the dynamic panel
data model," Journal of Econometrics, forthcoming.
[26] Golan, A., G. Judge, and D. Miller, 1996, Maximum Entropy Econometrics," New York: John
Wiley and Sons.
[27] Gospodinov, N., 2005, Robust Asymptotic Inference in Autoregressive Models with Martingale
Errors," Econometric Reviews, 24, 59{81.
78
[28] Guggenberger, P., 2003, Econometric Essays On Generalized Empirical Likelihood, Long-
Memory Time Series, and Volatility," Ph.D. thesis, Yale University.
[29] Guggenberger, P., and J. Hahn, 2005, Finite Sample Properties of The Two-Step Empirical
Likelihood Estimator," Econometric Reviews, 24, 247{263.
[30] Guggenberger, P., and R. Smith, 2005, Generalized Empirical Likelihood Estimators and Tests
under Partial, Weak, and Strong Identi cation," Econometric Theory, 21, 667{709.
[31] Gregory, A., J.F. Lamarche, and G.W. Smith, 2002, Information-Theoretic Estimation of Preference
Parameters: Macroeconomic Applications and Simulation Evidence," Journal of Economet-
rics, 107, 213{233.
[32] Hall, P., and J.L. Horowitz, 1996, Bootstrap Critical Values for Tests Based on Generalized
Method of Moments Estimators," Econometrica, 64, 891{916.
[33] Hahn, J., 1996, A Note on Bootstrapping Generalized Method of Moments Estimators," Econo-
metric Theory, 12, 187{197.
[34] Hansen, L.P., 1982, Large Sample Properties of Generalized Method of Moments Estimators,"
Econometrica, 50, 1029{1054.
[35] Hansen, L.P., J. Heaton, and A. Yaron, 1996, Finite Sample Properties of Some Alternative
GMM Estimators," Journal of Business and Economic Statistics, 262{280.
[36] Hayashi, F., 2000, Econometrics," Princeton University Press.
[37] Imbens, G.W., 1997, One-Step Estimators for Over-Identi ed Generalized Method of Moments
Models," Review of Economic Studies, 64, 359{383.
[38] Imbens, G.W., R.H. Spandy, and P. Johnson, 1998, Information Theoretic Approaches to
Inference in Moment Condition Models," Econometrica, 66, 333{357.
[39] Imbens, G.W., and R.H. Spandy, 2006, The Performance of Empirical Likelihood and Its
Generlizations," Identi cation and Inference for Econometric Models : Essays in Honor of Thomas
Rothenberg, Cambridge University Press, 216{244.
[40] Imbens, G.W., and R.H. Spandy, 2002, Con dence Intervals In Generalized Method of Moments
Models," Journal of Econometrics, 107, 87{98.
[41] Imbens, G.W., 2002, Empirical Likelihood and Generalized Method of Moments," Journal of
Business and Economic Statistics, 20, 493{536.
[42] Inoue, A., and M. Shintani, 2005, Bootstrapping GMM Estimators for Time Series," Journal
of Econometrics, forthcoming.
[43] Jing, B.Y., and A.T.A. Wood, 1996, Exponential Empirical Likelihood Is Not Bartlett Correctable,"
Annals of Statistics, 24, 365{369.
79
[44] Kitamura, Y., 1997, Empirical Likelihood with Weakly Dependent Processes," Annals of Statis-
tics, 25, 2084{2102.
[45] Kitamura, Y., 2001, Asymptitic Optimality of Empirical Likelihood for Testing Moment Restrictions,"
Econometrica, 69, 1661{1672.
[46] Kitamura, Y., and M. Stutzer, 1997, An Information-Theoretic Alternative to Generalized
Method of Moment Estimation," Econometrica, 65, 861{874.
[47] Kitamura, Y., G.H. Tripathi, and H. Ahn, 2004, Empirical Likelihood-Based Inference in Conditional
Moment Restriction Models," Econometrica, 72, 1667{1714.
[48] Kitamura, Y., 2006, Empirical Likelihood Methods in Econometrics: Theory and Practice,"
Working Paper.
[49] Kleibergen, F., 2005, Testing Parameters in GMM Without Assuming That They Are Ideiti ed,"
Econometrica, 73, 1103{1124.
[50] Kocherlakota, N.R., 1990, On Tests of Representative Consumer Asset Pricing Models," Journal
of Monetary Economics, 26, 285{304.
[51] Kunsch, H.R., 1989, The Jackknife and The Bootstrap for General Stationary Observations,"
Annals of Statistics, 17, 1217{1241.
[52] Lin, l., and R. Zhang, 2001, Blockwise Empirical Euclidean Likelihood for Weakly Dependent
Processes," Statistics and Probability Letters, 53, 143{152.
[53] Mittelhammer, R., G. Judge, and R. Schoenberg, 2006, Empirical Evidence Concerning the
Finite Sample Performance of EL-Type Structural Equation Estimation and Inference Methods,"
Identi cation and Inference for Econometric Models : Essays in Honor of Thomas Rothenberg,
Cambridge University Press, 282{305.
[54] Monti, A.C., 1997, Empirical Likelihood Con dence Regions in Time Series Models," Biometrika,
84, 395{405.
[55] Mykland, P., 1995, Dual Likelihood," Annals of Statistics, 23, 396{421.
[56] Newey, W.K., and D. McFadden, 1994, Large Sample Estimation Hypothesis Testing," In R.
Engle and D. McFadden, Handbook of Econometrics, vol. 4, pp. 2113{2445. North{Holland.
[57] Newey, W.K., and R.J. Smith, 2004, Higher-Order Properties of GMM and Generalized Empirical
Likelihood Estimators," Econometrica, 72, 219{255.
[58] Newey, W.K., J.J.S. Ramalho, and R.J. Smith, 2006, Asymptotic Bias for GMM and GEL
Estimators with Estimated Nuisance Parameters," Identi cation and Inference for Econometric
Models : Essays in Honor of Thomas Rothenberg, Cambridge University Press, 245{281.
80
[59] Newey, W.K., and K.D. West, 1987, A Simple Positive Semi-De nite Heteroskedasticity and
Autocorrelation Consistent Covariance Matrix," Econometrica, 55, 703{708.
[60] Otsu, T., 2006, Generalized Empirical Likelihood Inference for Nonlinear and Time Series
Models under Weak Identi cation," Econometric Theory, 22, 513{527.
[61] Owen, A., 1988, Empirical Likelihood Ratio Con dence Intervals for A Single Functional,"
Biometrika, 36, 237{249.
[62] Owen, A., 1990, Empirical Likelihood Ratio Con dence Regions," Annals of Statistics, 18,
90{120.
[63] Owen, A., 1991, Empirical Likelihood for Linear Models," Annals of Statistics, 19, 1725{1747.
[64] Owen, A., 2001, Empirical Likelihood," Chapman and Hall/CRC.
[65] Qin, J., and J. Lawless, 1994, Empirical Likelihood and General Estimating Equations," Annals
of Statistics, 22, 300{325.
[66] Ramalho, J.J.S., 2005, Small Sample Bias of Alternative Estimation Methods for Moment
Condition Models: Monte Carlo Evidence for Covariance Stuctures and Instrumental Variables."
Studies in Nolinear Dynamics and Econometrics, Vol. 9: No. 1, Article 3.
[67] Smith, R., 1997, Alternative Semi-Parametric Likelihood Approaches to Generalized Method of
Moments Estimation," Economic Journal, 107, 503{519.
[68] Smith, R., 2001, GEL Methods for Moment Condition Models," Working Paper, Department
of Economics, University of Bristol.
[69] Smith, R., 2005, Automatic Positive Semide nite HAC Covariance Matrix and GMM Estimation,"
Econometric Theory, 21, 158{170.
[70] Smith, R., 2005, Weak Instruments and Empirical Likelihood: A Discussion of The Paper by
D.W.K. Andrews, J.H. Stock and Y. Kitamura," Working Paper.
[71] Schennach, S.M., 2004, Exponentially Tiled Empirical Likelihood,"Working Paper, Department
of Economics, University of Chicago.
[72] Sherlund, S.M., 2004, Quasi Empirical Likelihood Estimation of Moment Conditions Models,"
Working Paper.
[73] Stock, J.H., J.H., Wright, and M. Yogo, 2002, A Survey of Weak Instruments and Weak
Identi cation in Generalized Method of Moments," Journal of Business and Economic Statistics,
20, 518{529.
[74] Stock, J.H., and J.H. Wright, 2000, GMM with Weak Identi cation," Econometrica, 68, 1055{
1096.
81
[75] Tauchen, G., 1986, Statistical Properties of Generalized Method of Moments estimators of
Structural Parameters obtained from nancial market data," Journal of Business of Economic
Statistics, 4, 397{425.
[76] Whang, Y.J., 2006, Smoothed Empirical Likelihood Methods for Quantile Regressions Models,"
Econometric Theory, 22, 173{205.
[77] Windmeijer, F., 2005, A Finite Sample Correction for The Variance of Linear E cient Two-Step
GMM Estimators," Journal of Econometrics, 126, 25{51.
[78] White, H., 1982, Maximum Likelihood Estimation of Misspeci ed Models," Econometrica, 50,
1{25.
[79] White, H., 2001, Asymptotic Theory for Econometricians," Academic Press.
[80] Ziliak, J.P., 1997, E cient Estimation with Panel Data When Instruments Are Predetermined:
An Empirical Comparison of Moment-Condition Estimators," Journal of Business of Economic
Statistics, 15, 419{431.
指導教授 徐之強(Chih-Chiang Hsu) 審核日期 2006-7-22
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明