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Essays in Econometrics.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Essays in Econometrics./
作者:	Rafi, Ahnaf.
面頁冊數:	1 online resource (341 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Finance. -
電子資源:	click for full text (PQDT)
ISBN:	9798382757537

Essays in Econometrics.
Rafi, Ahnaf.

Essays in Econometrics. - 1 online resource (341 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--Northwestern University, 2024.

Includes bibliographical references

This dissertation studies three distinct problems in econometrics. Chapter 1 studies inference on a class of functionals in a nonparametric variant of the random coefficients logit model. The random coefficients logit model is widely used in choice analysis, empirical industrial organization, and transport economics among other fields. Many objects of interest in this model can be represented as functionals of the distribution of random coefficients (RC/RCs), specifically averages. Some examples are: welfare measures, choice probabilities and their derivatives. Chapter 1 provides a nonparametric estimator of the RC distribution under which implied plug-in estimators of such averages are asymptotically normal. This is the first formal limiting distribution result for a nonparametric plug-in estimator of such functionals in the RC logit model. For the particular functionals considered here, this asymptotic normality occurs at the parametric n −1/2 rate. A consistent estimator of the variance of this limiting distribution is also provided. Together, these results make consistent tests of hypotheses and valid confidence intervals possible in the RC logit model when the distribution of RCs is estimated nonparametrically.Chapter 2 studies efficient estimation of the average treatment effect (ATE) in randomized experiments under a class of randomization procedures known as covariate adaptive randomization (CAR). Here, "efficient estimation" means construction of estimates that achieve the semiparametric efficiency bound (SEB) for the ATE. Experiments that use CAR are commonplace in applied economics and other fields. Under CAR, the experimenter first stratifies the sample according to observed baseline covariates and then assigns treatment randomly within these strata so as to achieve balance according to pre-specified stratum-specific target assignment proportions. We allow for the class of CAR procedures considered in Bugni et al. (2018, 2019). In Chapter 2, we first compute the SEB for estimating the ATE. The stratum-specific target proportions play the role of the propensity score conditional on all baseline covariates. The efficiency bound is a special case of the bound in Hahn (1998), but conditional on all baseline covariates, not just the stratum labels. Next, we show that this efficiency bound is achievable under the same (weak) conditions as those used to derive the bound. To do this, we construct an ATE estimator by combining the efficient influence function, a byproduct of the efficiency bound derivation, and a cross-fitted Nadaraya-Watson kernel regression estimator to form nonparametric regression adjustments.Chapter 3 (joint with Yong Cai) considers the issue of experiment design with the Neyman Allocation, which is used in many papers on experimental design. These papers typically assume that researchers have access to large pilot studies. This may be unrealistic. To understand the properties of the Neyman Allocation with small pilots, we study its behavior in an asymptotic framework that takes pilot size to be fixed even as the size of the main wave tends to infinity. Our analysis shows that the Neyman Allocation can lead to estimates of the ATE with higher asymptotic variance than with (non-adaptive) balanced randomization. In particular, this happens when the outcome variable is relatively homoskedastic with respect to treatment status or when it exhibits high kurtosis. We provide a series of empirical examples showing that such situations can arise in practice. Our results suggest that researchers with small pilots should not use the Neyman Allocation if they believe that outcomes are homoskedastic or heavy-tailed. Finally, we examine some potential methods for improving the finite sample performance of the FNA via simulations.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382757537Subjects--Topical Terms:

559073
Finance.
Subjects--Index Terms:

Random coefficientsIndex Terms--Genre/Form:

554714
Electronic books.

Essays in Econometrics.
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