| dc.contributor.advisor | 江振東 | zh_TW |
| dc.contributor.author (Authors) | 莊安婷 | zh_TW |
| dc.contributor.author (Authors) | Jhuang, An Ting | en_US |
| dc.creator (作者) | 莊安婷 | zh_TW |
| dc.creator (作者) | Jhuang, An Ting | en_US |
| dc.date (日期) | 2011 | en_US |
| dc.date.accessioned | 30-Oct-2012 10:40:53 (UTC+8) | - |
| dc.date.available | 30-Oct-2012 10:40:53 (UTC+8) | - |
| dc.date.issued (上傳時間) | 30-Oct-2012 10:40:53 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0099354003 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/54298 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計研究所 | zh_TW |
| dc.description (描述) | 99354003 | zh_TW |
| dc.description (描述) | 100 | zh_TW |
| dc.description.abstract (摘要) | 工具變數為處理非隨機試驗所面臨問題的方法之一，近來廣泛應用於計量經濟及流行病學領域；其主要目的在於控制不可觀測的干擾因素，使資料經過調整後「近似」於隨機試驗所得的資料，進而求出處理效果的一致估計值。由於先前研究大多探討連續型變數的情形，本篇論文將透過模擬與實證分析，針對二元之工具變數、反應變數及處理變數，比較一階段廣義線性估計量，two-stage predictor substitution (2SPS)，two-stage residual inclusion (2SRI)，及two-stage residual inclusion-Taylor expansion (2SRI-T) 這四種估計方法。 模擬結果顯示，當偏誤為主要考量時，2SPS與2SRI有較好的表現；然而，同時考慮偏誤及變異的情況下，2SRI-T則為較適合的估計方法。值得注意的是，模擬試驗所得出的結果與Terza等(2008)不同，2SRI並未優於2SPS。另外，將此四種方法套用至探討有小孩與否對生活的滿意度的影響之實際資料，其表現結果與模擬試驗結果一致。 | zh_TW |
| dc.description.abstract (摘要) | Instrumental variable (IV) analysis, one of the techniques to solve problems generated from non-random experiments, has been increasingly applied in many fields such as econometrics and epidemiology. Its utility stems from the belief that IV, if correctly selected, can potentially mimic randomization by adjusting for unmeasured confounders. However, because of less concern about IV analysis on categorical data, we center our discussion on binary outcome, treatment, and IV in this study. Four methods are compared: the one-stage generalized linear model (GLM), two-stage predictor substitution (2SPS), two-stage residual inclusion (2SRI), and two-stage residual inclusion considering Taylor expansion (2SRI-T). We conduct both the simulation and the empirical study to evaluate the performances of these four estimators. The simulation results indicate that, while 2SPS and 2SRI have better performances than the other two estimators with respect to the bias, they suffer from larger variability. On the other hand, 2SRI-T generally has smaller standard error than 2SPS and 2SRI, and hence might be preferred if MSE is the main concern. Noticeably, it also suggests that 2SRI does not outperform 2SPS which was inversely shown in Terza et al. (2008). The same conclusion is also found when implementing these methods on a real dataset to investigate whether having children has significant effect on one’s life satisfaction. | en_US |
| dc.description.tableofcontents | 1 Introduction 12 Statistical Models and Estimation Methods 5 2.1 Underlying Assumptions2.2 IV Methods 553 Simulation and Results 9 3.1 Simulation Design3.2 Results 9114 Empirical Study and Results 18 4.1 Data Description4.2 Descriptive Analysis4.3 Results 1818215 Conclusion and Discussion 22References 24Appendix 26 A: Programming Code of SimulationB: Histograms of Estimated Coefficients under Different Values of aC: Tables of Simulation Results under Different β_0and nD: Questions Used in the WVS Questionnaire in the Empirical Analysis 27323444 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0099354003 | en_US |
| dc.subject (關鍵詞) | 工具變數 | zh_TW |
| dc.subject (關鍵詞) | 二元反應變項 | zh_TW |
| dc.subject (關鍵詞) | 觀察性研究 | zh_TW |
| dc.title (題名) | 兩階段工具變數估計量應用於二元反應變數之比較與實證研究 | zh_TW |
| dc.title (題名) | The performance of different two-stage Instrumental Variable methods for binary outcomes | en_US |
| dc.type (資料類型) | thesis | en |
| dc.relation.reference (參考文獻) | References 1. Angrist JD, Imbens G, Rubin DB. Identification of causal effects using instrumental variables. Journal of the American Statistical Association. 1996; 94(434): 444-455.2. Brookhart MA, Wang PS, Solomon DH, Schneeweiss S. Evaluating short-term drug effects using a physical-specific prescribing preference as an instrumental variable. Epidemiology. 2006; 17(3): 268-275.3. Brookhart MA, Rassen JA, Schneeweiss S. Instrumental variable methods in comparative safety and effectiveness research. Pharmacoepidemiology and Drug Safety.2010; 19(6): 537-554.4. Greene WH. Econometric Analysis. 5th ed. Upper Saddle, River, NJ: Prentice Hall; 2003.5. Greenland S. An introduction to instrumental variables for epidemiologists. International Journal of Epidemiology. 2000; 29: 722-729.6. Hausman JA. Specification tests in econometrics. Econometrica. 1978; 46:1251-1271.7. Hernán MA, Robins JM. Instruments for causal inference: an epidemiologist’s dream? Epidemiology. 2006; 17(4): 360-372.8. Johnston KM, Gustafson P, Levy AR, Grootendorst P. Use ofinstrumental variables in the analysis of generalized linear models in the presence of unmeasured confounding with applications to epidemiological research. Statistics in Medicine. 2008;27: 1539-1556.9. Kennedy P. A guide to Econometrics. 5th ed. Cambridge, MA: MIT Press; 2003.10. McClellan M, McNeil BJ, Newhouse JP. Does more intensive treatment of acute myocardial infarction in the elderly reduce mortality? Analysis using instrumental variables. Journal of the American Medical Association. 1994; 272(11): 859-866.11. Rassen JA, Schneeweiss S, Glynn RJ, Mittleman MA, Brookhart MA. Instrumental variable analysis for estimation of treatment effects with dichotomous outcomes. American Journal of Epidemiology. 2009; 169(3):273-284. 12. Schneeweiss S, Solomon DH, Wang PS, Rassen JA, Brookhart MA. Simultaneous assessment of short-term gastrointestinal benefits and cardiovascular risks of selective cyclooxygenase 2 inhibitors and nonselective nonsteroidal antiinflammatory drugs: an instrumental variable analysis. Arthritis Rheumatism. 2006; 54(11): 3390-3398.13. Small DS. Sensitivity analysis for instrumental variables regression with overidentifying restrictions. Journal of the American Statistical Association. 2007; 102(479): 1049-1058.14. Stukel TA, Fisher ES, Wennberg DE, Alter DA, Gottlieb DJ, Vermeulen MJ. Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods. Journal of the American Medical Association. 2007; 297(3): 444-455.15. Terza JV, Basu A, Rathouz PJ. Two-stage Residual inclusion estimation: addressing endogeneity in health econometric modeling. Journal of Health Economics. 2008; 27(3): 531-543.16. Wang PS, Schneeweiss S, Avorn J, Fiescher MA, Mogun H, Solomon DH, Brookhart MA. Risk of death in elderly users of conventional vs. atypical antipsychotic medications. New England Journal of Medicine. 2005; 353(22): 2335-2341. | zh_TW |
