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摘要： This research aims to analyze large-truck involved crashes (i.e., having a gross vehicle weight rating more than 10,000 pounds) through the application of advanced econometric modeling techniques—namely, random parameter models (i.e., tobit regression, mixed logit). To achieve this, various national and state specific data sets are analyzed with the goal to provide an improved understanding of the complex interactions of contributing factors (e.g., factors related to drivers and occupants, vehicle, and road-environment) influencing large-truck crashes. Additionally, the modeling techniques considered in this research account for possible unobserved effects related to the data. The aforementioned econometric techniques provide an analytical foundation for exploring the contributing factors leading to large-truck involved crashes.

关键词： Spatial Econometrics   Specification Tests   Rao's Score   Growth Convergence   Spatial Auto-regressive Models  

摘要： A focus on location and spatial interaction has recently gained a more central place not only in applied econometrics but also in theoretical econometrics. The standard econometric techniques often fail in the presence of spatial autocorrelation, which is commonplace in geographic (cross-sectional) data sets, and thus giving misleading inference, and thereby wrong policy implications are derived from these econometric models. In this dissertation I have dealt with such model specification issues which arises due to spatial nature of the data. The main contributions of this dissertation are thus two folds: spatial model specification and specification tests for spatial panel data models. Each chapter provides econometric methods along with empirical examples to demonstrate the importance and utility of the proposed methods. In Chapter 2, I propose an estimation strategy for popular spatial weight matrix. The spatial lag dependence in a regression model is similar to the inclusion of a serially autoregressive term for the dependent variable in time-series context. However, unlike in the time series model, the implied covariance structure matrix from the spatial autoregressive model can have a very counterintuitive and improbable structure. However, if the weight matrix can capture the underlying dependence structure of the observations then this unintuitive behavior of implied correlation gets corrected to a large extent. Thus in Chapter 2, I explore the possibility of constructing the weight matrix (or the overall spatial dependence in the data) that is consistent with the underlying correlation structure of the dependent variable. Specification of a model is one of the most fundamental problems in econometrics. However, in most cases, specification tests are carried out in a piecemeal fashion, for example, testing the presence of one-effect at a time ignoring the potential presence of other forms of misspecification. In Chapter 3, I overcome these difficulties by pr

ISBN: (纸本)9787301212226

摘要： My dissertation consists of three chapters that concerns topics in econometric theory and applied econometrics. The first two chapters analyze identification and estimation in models with endogeneity. The second chapter is joint work with Edward Vytlacil. The final chapter reexamines methods for dealing truncated dataset in economic history. The first chapter of my dissertation analyzes the effect of weak instruments on identification, estimation, and inference in a nonparametric setting. This analysis is considered to be the first attempt in the literature. The consequences of weak instruments have been extensively studied in the literature with linear simultaneous equations models. One might conjecture that the problem of weak instruments becomes even more important when studying endogenous explanatory variables in nonparametric models, as more flexible models generally require stronger identification power, and hence plausibly stronger instruments. To address this problem, in this chapter, we consider a triangular simultaneous equations model, and follow the control function approach for identification and estimation. We derive a necessary and sufficient rank condition for identification, based on which weak identification is established. Then, nonparametric weak instruments are defined as a sequence of reduced form functions that converges to a constant function. We characterize weak instruments as a multicollinearity problem or, more generally, as an inverse problem, which motivates the introduction of a regularization scheme. We propose a series estimation method with penalization to alleviate the effects of weak instruments. We derive the rate of convergence of the resulting penalized series estimator. Consistency and asymptotic normality are achieved with "mildly" weak instruments and a "rapidly" shrinking penalization parameter. Monte Carlo results show that the finite sample performance of the penalized estimator is appealing. The results of this chapter a

关键词： 空间计量经济学   横截面数据   空间线性模型   思考  

摘要： 空间计量经济学在研究方面得到了迅速的发展,主要是通过建立数学模型研究横截面数据和面板数据在回归模型中的空间相互作用的空间自相关以及空间结构的不均匀性。因此,我们在研究空间计量经济学的时候,首先要有大量的数据,通过这些数据的空间分布,建立数学模型,研究它们之间的关系和规律,进而把研究的结果应用到经济学领域当中。本文主要介绍了空间回归分析的相关问题,研究了横截面数据空间线性模型的通用模式。

ISBN: (纸本)9787300147277

ISBN: (纸本)9787543220584

关键词： 出口总额   汇率   GDP进口总额   模型  

摘要： 目前影响我国经济增长的一个重要因素是出口,但因人民币升值等原因,我国出口遇到了屏障.研究出口总额受到哪些因素的影响,影响程度及如何影响是必要的.运用计量经济学思想建立、估计、检验和预测模型,目的是运用分析结论引导我国出口的平稳健康增长.本文在相关理论研究的基础上,采用计量分析方法(最小二乘法,NLS法,设定虚拟变量等),对其影响因素进行实证分析,建立多元线性回归模型,结果证实设定的GDP,进口总额与汇率因素有意义,符合初始猜想.

关键词： 可信性革命   随机性设定   因果效应识别   统计适切性  

摘要： 可信性是计量经济学应用研究的重要问题，其核心在于实现经济理论、统计学、数学在实证研究中的科学结合。本文基于国际计量经济学界对可信性问题的三次大讨论取得的重要进展，厘清了计量经济学探索客观经济世界过程的本质特征；进而针对应用研究中存在的滥用和错用现象，从计量经济模型的随机性设定、经济变量之间的因果关系识别以及模型的统计适切性评价等三个方面阐述计量经济学应用研究的可靠性来源。我国计量经济学的应用研究面临进一步提高可信性的重要问题，需要全面吸收和借鉴国际计量经济学界对于可信性问题的成果，改变研究模式和教学模式。

ISBN: (纸本)9787303140510