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Many instruments, sample selection and treatment effects in econometrics.

機(jī)譯：計(jì)量經(jīng)濟(jì)學(xué)中的許多儀器，樣本選擇和處理效果。

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This dissertation presents research undertaken in three distinct areas of econometrics. In chapter one I construct new variance adjustments for the Two-Stage Least Squares and Maximum Likelihood estimators of a linear structural equation, in which the regressors are endogenous. Variance estimates based on standard large sample analysis are often too small, which may result in unreliable inference. I derive the variance adjustments by considering a more general asymptotic approximation. The limiting distributions are valid under weak assumptions about the errors. The usefulness of the approximations is assessed in a simulation experiment.; In chapter two I develop Markov Chain Monte Carlo sampling algorithms for the parameters of the sample selection and two-part models. Both models can be used to describe an outcome variable with a limited range. The sample selection model focuses on potential outcomes that are only partially observed. The two-part model focuses on the observed outcomes. I analyze both models from a Bayesian perspective and develop several Gibbs sampling algorithms that can be used to approximate the posterior distribution of the model parameters. The output of the algorithms forms a basis, through the Bayes factor, for determining which model is more effective in describing the data. The different techniques are evaluated and compared in a simulation experiment.; In chapter three I consider the problem of conducting a hypothesis test on the coefficient of a binary endogenous variable. Binary variables are used to capture the effect of different regimes or treatments on the outcome variable of interest. When the assignment of individuals to treatments is nonrandom, an endogeneity problem may occur. To overcome this problem, I consider likelihood and instrumental variables based methods that can be used to test whether the treatment has a significant impact on the outcome. A simulation experiment shows that as the instruments become less relevant, most commonly used testing procedures either become size distorted or lose power.
機(jī)譯：本文介紹了在計(jì)量經(jīng)濟(jì)學(xué)的三個(gè)不同領(lǐng)域進(jìn)行的研究。在第一章中，我為線性結(jié)構(gòu)方程的兩階段最小二乘和最大似然估計(jì)量構(gòu)造了新的方差調(diào)整，其中回歸變量是內(nèi)生的?；跇?biāo)準(zhǔn)大樣本分析的方差估計(jì)值通常太小，可能導(dǎo)致推斷不可靠。我通過考慮更一般的漸近近似來推導(dǎo)方差調(diào)整。在關(guān)于誤差的弱假設(shè)下，極限分布是有效的。在模擬實(shí)驗(yàn)中評(píng)估了近似的有用性。在第二章中，我針對(duì)樣本選擇和兩部分模型的參數(shù)開發(fā)了馬爾可夫鏈蒙特卡洛采樣算法。兩種模型都可用于描述范圍有限的結(jié)果變量。樣本選擇模型側(cè)重于僅部分觀察到的潛在結(jié)果。該模型分為兩部分，著重于觀察到的結(jié)果。我從貝葉斯角度分析了這兩個(gè)模型，并開發(fā)了幾種吉布斯采樣算法，可用于近似模型參數(shù)的后驗(yàn)分布。這些算法的輸出通過貝葉斯因子構(gòu)成了確定哪種模型更有效地描述數(shù)據(jù)的基礎(chǔ)。在模擬實(shí)驗(yàn)中評(píng)估并比較了不同的技術(shù)。在第三章中，我考慮了對(duì)二進(jìn)制內(nèi)生變量的系數(shù)進(jìn)行假設(shè)檢驗(yàn)的問題。二元變量用于捕獲不同方案或治療對(duì)目標(biāo)結(jié)果變量的影響。當(dāng)個(gè)體的治療分配不是隨機(jī)的時(shí)，可能會(huì)發(fā)生內(nèi)生性問題。為了克服這個(gè)問題，我考慮了基于可能性和工具變量的方法，這些方法可用于測(cè)試治療是否對(duì)結(jié)果產(chǎn)生重大影響。仿真實(shí)驗(yàn)表明，隨著儀器的相關(guān)性降低，最常用的測(cè)試程序會(huì)導(dǎo)致尺寸失真或功耗降低。

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