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Essays on theories and applications of spatial econometric models.

機譯：關于空間計量經(jīng)濟模型的理論和應用的論文。

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My dissertation is about theories and applications of spatial autoregressive (SAR) models. As an effective method in analyzing interdependence among the observations, the SAR models have witnessed ever-increasing applications in various areas, including Economics, Sociology, Geography and others. This dissertation intends to extend the spatial theoretical literature by studying a more general SAR model and to enrich the social interaction studies by introducing a special SAR model to confront the identifications issues. Chapter 2 studies peer effects in student academic achievement. It is well known that disentangling peer effects from other confounding effects is difficult, and separately identifying endogenous and contextual social effects is impossible for the linear-in-means social interaction model. This study confronts these conceptual problems by using a SAR model with group fixed effects. The nonlinearity introduced by the variations in the peer measurements in the SAR model provides information to identify both endogenous and contextual effects, thus resolving the "reflection problem". The group fixed effects term in the model captures the confounding effects of the common variables faced by the same group members. I use datasets from the National Longitudinal Study of Adolescent Health (Add Health) survey and specify peer groups as friendship networks. I find evidence for both endogenous and contextual effects in student academic achievement, even after controlling for school-grade fixed effects. The result indicates that students benefit from the presence of high quality peers, and that associating with peers living with both parents helps improve a student's GPA, while associating with peers whose mothers receive welfare has a negative effect. Chapter 3 considers GMM estimation of spatial autoregressive (SAR) models with unknown heteroskedasticity. In the presence of heteroskedastic disturbances, the maximum likelihood estimator (MLE) for the SAR models without taking into account the heteroskedasticity is generally inconsistent. In contrast, GMM estimators obtained from certain moment conditions can be robust against unknown heteroskedasticity. Asymptotically valid inferences can be drawn with consistently estimated covariance matrices. Furthermore, efficiency can be improved by constructing the optimal weighted GMM estimation. Tests for heteroskedasticity are investigated. Monte Carlo experiments are designed to study the finite sample properties of the GMM and other estimators such as MLE and 2SLSE, and the test statistics. The Monte Carlo results show that even though 2SLS estimates shall be consistent in the presence of unknown heteroskedasticity, they can have large variances and biases in finite samples for cases where regressors do not have strong effects. The robust GMM estimator has desirable properties while the biases associated with MLE and non-robust GMME may remain in large sample, especially, for the spatial effect coefficient and the intercept term. However, the magnitudes of biases are only moderate. With moderate large sample sizes, those biases may be statistically insignificant. The various approaches are applied to the study of county teenage pregnancy rates. The empirical results show a strong spatial convergence among county teenage pregnancy rates with a significant spatial effect.
機譯：我的論文是關于空間自回歸（SAR）模型的理論和應用。 SAR模型作為一種有效的分析結果之間相互依存關系的方法，在經(jīng)濟，社會學，地理等各個領域得到了越來越多的應用。本文旨在通過研究更一般的SAR模型來擴展空間理論文獻，并通過引入特殊的SAR模型來面對識別問題來豐富社會互動研究。第2章研究學生學習成績中的同伴效應。眾所周知，很難將同伴效應與其他混雜效應區(qū)分開來，并且對于均分線性社會互動模型而言，單獨識別內生性和背景性社會效應是不可能的。本研究通過使用具有群體固定效應的SAR模型來面對這些概念性問題。 SAR模型中對等測量值的變化引入的非線性為識別內源性效應和上下文效應提供了信息，從而解決了“反射問題”。模型中的組固定效應項捕獲了相同組成員面臨的公共變量的混雜效應。我使用了《全國青少年健康縱向研究（補充健康）》的數(shù)據(jù)集，并將同伴群體指定為友誼網(wǎng)絡。我發(fā)現(xiàn)即使在控制了學校等級的固定效應之后，學生學習成績中的內源性和情境效應的證據(jù)。結果表明，學生可以從高品質的同伴中受益，與同居父母的同伴交往有助于提高學生的GPA，而與母親得到福利的同伴交往則具有負面影響。第3章考慮了具有未知異方差性的空間自回歸（SAR）模型的GMM估計。在存在異方差干擾的情況下，SAR模型的最大似然估計器（MLE）在不考慮異方差的情況下通常是不一致的。相反，從某些矩條件獲得的GMM估計器可以抵抗未知的異方差?？梢允褂靡恢鹿烙嫷膮f(xié)方差矩陣得出漸近有效的推論。此外，可以通過構建最佳加權GMM估計來提高效率。研究了異方差測試。蒙特卡洛實驗旨在研究GMM和其他估計量（例如MLE和2SLSE）的有限樣本屬性以及測試統(tǒng)計量。蒙特卡洛結果表明，即使在存在未知異方差的情況下2SLS估計值將保持一致，但對于回歸變量不具有強影響的情況，它們在有限樣本中可能具有較大的方差和偏差。健壯的GMM估計器具有理想的屬性，而與MLE和非健壯的GMME相關的偏差可能會保留在大樣本中，尤其是對于空間效應系數(shù)和截距項。但是，偏差的程度只是中等。在樣本量中等的情況下，這些偏差可能在統(tǒng)計上不明顯。各種方法都用于研究縣青少年懷孕率。實證結果表明，縣青少年懷孕率之間的空間收斂性強，具有顯著的空間效應。

著錄項

  • 作者

    Lin, Xu.;

  • 作者單位

    The Ohio State University.;

  • 授予單位 The Ohio State University.;
  • 學科 Education Sociology of.Economics Theory.
  • 學位 Ph.D.
  • 年度 2006
  • 頁碼 131 p.
  • 總頁數(shù) 131
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類
  • 關鍵詞

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