Methods for Nonparametric and Semiparametric Regressions with Endogeneity: A Gentle Guide

Chen, Xiaohong; Qiu, Yin Jia Jeff

doi:10.2139/ssrn.2756199

Cited by 6 publications

(7 citation statements)

References 117 publications

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“…and Chen and Qiu (2016). 20 In the absence of separability of δ jt in ξ jt , following Appendix B of BH, we can write ξ jt = g j (st , p t , x (1) jt , x (2) ).…”

Section: Setup and Asymptotic Resultsmentioning

confidence: 99%

Market counterfactuals and the specification of multiproduct demand: A nonparametric approach

Compiani

2022

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Demand estimates are essential for addressing a wide range of positive and normative questions in economics that are known to depend on the shape—and notably the curvature—of the true demand functions. The existing frontier approaches, while allowing flexible substitution patterns, typically require the researcher to commit to a parametric specification. An open question is whether these a priori restrictions are likely to significantly affect the results. To address this, I develop a nonparametric approach to estimation of demand for differentiated products, which I then apply to California supermarket data. While the approach subsumes workhorse models such as mixed logit, it allows consumer behaviors and preferences beyond standard discrete choice, including continuous choices, complementarities across goods, and consumer inattention. When considering a tax on one good, the nonparametric approach predicts a much lower pass‐through than a standard mixed logit model. However, when assessing the market power of a multiproduct firm relative to that of a single‐product firm, the models give similar results. I also illustrate how the nonparametric approach may be used to guide the choice among parametric specifications.

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“…and Chen and Qiu (2016). 20 In the absence of separability of δ jt in ξ jt , following Appendix B of BH, we can write ξ jt = g j (st , p t , x (1) jt , x (2) ).…”

Section: Setup and Asymptotic Resultsmentioning

confidence: 99%

Market counterfactuals and the specification of multiproduct demand: A nonparametric approach

Compiani

2022

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“…One classical approach to this problem is a nonparametric analogue of the two-stage least squares (2SLS) method based on sieve estimators [Newey and Powell, 2003] or kernel density estimators [Darolles et al, 2010, Hall, 2005, Carrasco et al, 2007. Another classical approach is to use sieves to convert the conditional moments into unconditional moments of of increasing dimension [e.g., Chen, 2007, Ai and Chen, 2003, Chen and Qiu, 2016, and then combine all unconditional moments via standard GMM method [Hansen, 1982]. Later, Hartford et al [2017], Singh et al [2019] extend the two-stage approach by employing neural network density estimator or conditional mean embedding in RKHS respectively in the first stage.…”

Section: Related Literaturementioning

confidence: 99%

Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach

Kallus¹,

Mao²,

Uehara³

2021

Preprint

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We study the causal inference when not all confounders are observed and instead negative controls are available. Recent work has shown how negative controls can enable identification and efficient estimation of average treatment effects via two so-called bridge functions. In this paper, we consider a generalized average causal effect (GACE) with general interventions (discrete or continuous) and tackle the central challenge to causal inference using negative controls: the identification and estimation of the two bridge functions. Previous work has largely relied on completeness assumptions for identification and uniqueness assumptions for estimation, and mainly focused on estimating these functions parametrically. We provide a new identification strategy for GACE that avoids completeness, and propose new minimax-learning estimators for the (nonnunique) bridge functions that can accommodate general function classes such as Reproducing Kernel Hilbert spaces and neural networks and can provide theoretical guarantees even when the bridge functions are nonnunique. We study finite-sample convergence results both for estimating bridge function themselves and for the final GACE estimator. We do this under a variety of combinations of assumptions on the hypothesis and critic classes employed in the minimax estimator. Depending on how much we are willing to assume, we obtain different convergence rates. In some cases, we show that the GACE estimator may converge to truth even when our minimax bridge function estimators do not converge to any valid bridge function. And, in other cases, we show we can obtain semiparametric efficiency.

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“…Also further publications (e.g., "Methods for nonparametric and semiparametric regressions with endogeneity: A gentle guide" by Chen and Qiu (2016), "Shrinkage estimation of regression models with multiple structural Changes" by Qian and Su (2016) or "Nonparametric/semiparametric estimation and testing of econometric models with data dependent smoothing parameters" by Li and Li (2010)) go in the direction of data-driven nonparametric/semiparametric estimation and testing of econometric models.…”

Section: Data-driven Procedures In Economics and Econometrics (Green)mentioning

confidence: 99%

The effect of web of science subject categories on clustering: the case of data-driven methods in business and economic sciences

Jesenko

Schlögl

2021

Scientometrics

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The primary goal of this article is to identify the research fronts on the application of data-driven methods in business and economics. For this purpose, the research literature of the business and economic sciences Subject Categories from the Web of Science is mapped using BibExcel and VOSviewer. Since the assignment to subject categories is done at the journal level and since a journal is often assigned to several subject categories in Web of Science, two mappings are performed: one without considering multiple assignments (broad view) and one considering only those (articles from) journals that have been assigned exclusively to the business and economic sciences subject categories and no others (narrow view). A further aim of this article is therefore to identify differences in the two mappings. Surprisingly, engineering sciences play a major role in the broad mapping, in addition to the economic sciences. In the narrow mapping, however, only the following clusters with a clear business-management focus emerge: (i) Data-driven methods in management in general and data-driven supply chain management in particular, (ii) Data-driven operations research analyses with different business administration/management focuses, (iii) Data-driven methods and processes in economics and finance, and (iv) Data-driven methods in Information Systems. One limitation of the narrow mapping is that many relevant documents are not covered since the journals in which they appear are assigned to multiple subject categories in WoS. The paper comes to the conclusion that the multiple assignments of subject categories in Web of Science may lead to massive changes in the results. Adjacent subject areas—in this specific case the application of data-driven methods in engineering and more mathematically oriented contributions in economics (econometrics) are considered in the broad mapping (not excluding subject categories from neighbouring disciplines) and are even over-represented compared to the core areas of business and economics. If a mapping should only consider the core aspects of particular research fields, it is shown in this use case that the exclusion of Web of Science-subject categories that do not belong to the core areas due to multiple assignments (narrow view), may be a valuable alternative. Finally, it depends on the reader to decide which mapping is more beneficial to them.

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Methods for Nonparametric and Semiparametric Regressions with Endogeneity: A Gentle Guide

Cited by 6 publications

References 117 publications

Market counterfactuals and the specification of multiproduct demand: A nonparametric approach

Market counterfactuals and the specification of multiproduct demand: A nonparametric approach

Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach

The effect of web of science subject categories on clustering: the case of data-driven methods in business and economic sciences

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