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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 管中閔 | |
dc.contributor.author | Wen-Yin Chen | en |
dc.contributor.author | 陳玟吟 | zh_TW |
dc.date.accessioned | 2021-06-15T01:16:08Z | - |
dc.date.available | 2014-08-22 | |
dc.date.copyright | 2011-08-22 | |
dc.date.issued | 2011 | |
dc.date.submitted | 2011-08-22 | |
dc.identifier.citation | Abadie, A., J. Angrist, and G. W. Imbens (2002). Instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings, Econometrica, 70, 91-117.
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/42553 | - |
dc.description.abstract | 具內生性的回歸分析是計量方法研究上重要議題。 在現有的計量研究方法中,解決內生性的估計偏誤的方法主要分為三類,包括替代變數法、控制變數法與工具變數法。 這三種方法已被廣泛使用在實證議題的研究,但三種估計方法的比較缺乏完整的模擬分析。因此,本論文藉由不同模型的模擬估計結果,比較不同的具內生性的分量回歸估計方法之間的差異,並將具內生性的分量回歸分析應用在家計單位的儲蓄行為研究。
在第一章中, 我們介紹三種具內生性的分量回歸方法, 包括二階段分量回歸法,控制變數法與工具變數分量回歸法,並以不同模型的模擬估計結果來比較三種方法在有限樣本之下的表現。從模擬結果中發現, 當我們用不同的估計方法估計內生變數的回歸模型, 二階段分量回歸法與控制變數法的有限樣本的表現會出現明顯的差異, 而工具變數分量回歸法的估計表現較不受第一階段估計方法的影響。當我們以變異較小的估計方法估計內生變數的回歸模型,控制變數法的變異會小於二階段分量回歸法與工具變數分量回歸法。此外,在模擬中我們考慮內生變數具有不同程度的內生性,當內生性愈強時, 三種具內生性的分量回歸法都產生較大的估計偏誤與變異。在異質變異的模型中, 當異質變異的程度愈強,制變數法與工具變數分量回歸法都會產生較大的估計偏誤與變異。 在第二章中,我們以工具變數分量回歸法分析台灣的家計單位的的儲蓄行為。 在過去有關家計單位儲蓄行為的實證研究中, 大部分都忽略儲蓄行為與購屋的選擇是相互影響的,因而造成估計結果的偏誤。當我們比較傳統的分量回歸法與工具變數分量回歸法的估計結果, 發現傳統的分量回歸法有產生明顯的估計偏誤。在工具變數分量回歸法的實證結果中,我們發現當租屋者有購屋計劃時, 其儲蓄率會高於擁屋者, 並且當租屋者的購屋意願愈強時, 其儲蓄率會愈高。此外, 我們發現房價愈高時,租屋者的購屋意願會降低而減少儲蓄,對擁屋者也會產生財富效果而減少儲蓄。在儲蓄行為的異質性檢定與購屋選擇的內生性檢定的結果中, 我們進一步證實家計單位的儲蓄行為具 有異質性,並且購屋的選擇具有內生性。 | zh_TW |
dc.description.abstract | The analysis of regression with endogeneity has been an important research direction in econometrics. There exist the fitted value approach, the control function approach, and the instrumental variable approach to regression with endogeneity. Yet, there exists little simulation evidence for the three approaches. In the thesis, we first compare the three approaches to quantile regression with endogeneity using sumulations. Next, we apply the instrumental variable approach to analysis of the houshold's saving behavior in Taiwan.
In Chapter 1, we introduce the two-stage quantile regression, the control function, and the instrumental variable quantle regression estimation , and compare these three approaches by extensive simulations. From the simulation resutls, it is found that the performance of the two-stage quantile regression and control function estimators depends on the method used for estimating the reduced-form equation of the endogenous variable. In contrast, the performance of the instrumental variable quantile regression estiamtor is more robust to the method used for estmating the instrument. In a homoskedastic regression model with a continuous endogenous regressor, when the reduced-form equantion of the endogenous variable is estimated using a more efficient method, it is shown that the control function estimator has smaller standard errors and mean squared errors than the two-stage quantile regression and instrumental variable quantile regression estimators. Also, it is observed that when the correlation of the endogenous variable and the instrument is lower, these estimators have larger standard errors and mean squared errors. Moreover, the simulation results of the heteroskedastic regression model imply that a higher degree of heteroskedasticity yields larger standard errors and mean squared errors of the control function and instrumental variable quantile regression estimators. In Chapter 2, we provide new evidence on the relationship between the household's saving behavior and housing price in Taiwan through the instrumental variable quantion regression approach. The correlation between household wealth accumulation and homeownership results in the simultaneous bias in the conventional mean regression or quantile regression estimation of the household's saving function. Most of the existing literature of household saving behavior ignores the endogeneity of homeownership. The object of this chapter is to employ an estimation approach to quantile regression with endogeneity to consistently estimate the household's saving function for both the consideration of endogeneity and heterogeneity. In the instrumental variable quantile regression estimation of the houshold's saving function, the family structure variables are used as instrumental variables for the determinants of homeownership. The estimation results provide strong evidence on the endogeneity of homeownership decision. From the instrumental variable quantile regression estimation, when the renter has a higher saving rate than the homeowner, it is implied that the renter intends to save more for a housing purchase plan. When the renter is more inclinded to own a house, it is found that a higher housing price produces a larger discouragement effect on saving. Furthermore, the high housing price produces the wealth effect on the homeowner's saving rate. In addition, when the renter saves more than the homeowner, it is found that the renter's discouragement effect is larger than the homeowner's wealth effect, and vice verse. From the constant effect test and exogeneity test, the results confirm the heterogeneity of the household's saving behavior and endogeneity of homeownership. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T01:16:08Z (GMT). No. of bitstreams: 1 ntu-100-D91323002-1.pdf: 972419 bytes, checksum: a6607c31e1187f64876f8e8e12aa821b (MD5) Previous issue date: 2011 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 摘要 iii Abstract iv 1 A Monte Carlo Study of Quantile Regression with Endogeneity 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Quantile Regression with Endogeneity . . . . . . . . . . . . . . . . . . 5 1.2.1 The Conventional Quantile Regression Estimation . . . . . . . . 7 1.2.2 The Fitted Value Approach . . . . . . . . . . . . . . . . . . . . 9 1.2.3 The Control Function Approach . . . . . . . . . . . . . . . . . . 12 1.2.4 The Instrumental Variable Approah . . . . . . . . . . . . . . . . 18 1.3 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.3.1 The Homoskedastic Regression Model . . . . . . . . . . . . . . . 23 1.3.2 The Heteroskedastic Regression Model with the Error Term Interacting with Endogenous Regressors . . . . . . . . . . . . . . 26 1.3.3 The Heteroskedastic Regression Model with an Additive Error Term Correlated with continuous Regressors . . . . . . . . . . . 28 1.4 Monte Carlo Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . 29 1.4.1 A Homoskedastic Regression Model . . . . . . . . . . . . . . . . 32 1.4.2 A Heteroskedastic Regression Model with an Additive Error Term Correlated with a Continuous Regressor . . . . . . . . . . . . . 36 1.4.3 A Heteroskedastic Regression Model with the Error Term Interacting with an Endogenous Regressor . . . . . . . . . . . . . . . 37 1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2 Quantile Regression Analysis of Household Saving and Housing Behavior 78 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 2.2 Previous Empirical Studies . . . . . . . . . . . . . . . . . . . . . . . . . 81 2.3 Empirical Estimation Method . . . . . . . . . . . . . . . . . . . . . . . 83 2.4 Data and Empirical Model . . . . . . . . . . . . . . . . . . . . . . . . . 88 2.4.1 Empirical Model . . . . . . . . . . . . . . . . . . . . . . . . . . 88 2.4.2 Data and Summary Statistics of Variables . . . . . . . . . . . . 91 2.5 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 2.5.1 The Probit Estimation of the Selection Equation . . . . . . . . . 93 2.5.2 Mean Regression Estimation of the Saving Function . . . . . . . 94 2.5.3 Quantile Regression Estimation of the Saving Function . . . . . 95 2.5.4 Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . . . 98 2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 | |
dc.language.iso | en | |
dc.title | 具內生性的分量回歸:模擬與實證 | zh_TW |
dc.title | QUANTILE REGRESSIONs with Endogeneity: Simulations and Empirical Application | en |
dc.type | Thesis | |
dc.date.schoolyear | 99-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 陳建良,徐之強,黃裕烈,林馨怡 | |
dc.subject.keyword | 內生性,工具變數,分量回歸,儲蓄行為,房價, | zh_TW |
dc.subject.keyword | Endogeneity,Instrumental Variables,Quantile Regression,Saving,Housing Prices, | en |
dc.relation.page | 119 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2011-08-22 | |
dc.contributor.author-college | 社會科學院 | zh_TW |
dc.contributor.author-dept | 經濟學研究所 | zh_TW |
顯示於系所單位: | 經濟學系 |
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