Testing Conditional Mean Independence Under Symmetry

Chen, Tao; Ji, Yuanyuan; Zhou, Yong‐Gui; Zhu, Pingfang

doi:10.1080/07350015.2016.1219263

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…) . Obviously, the asymptotic normality in (19) can be used for making inferences for ∆ 1 provided that a consistent estimate of Σ in (19) is available.…”

Section: Model Setupmentioning

confidence: 99%

Statistical Analysis and Evaluation of Macroeconomic Policies: A Selective Review

Liu

Cai

Fang

et al. 2020

Appl. Math. J. Chin. Univ.

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In this paper, we highlight some recent developments of a new route to evaluate macroeconomic policy effects, which are investigated under the framework with potential outcomes. First, this paper begins with a brief introduction of the basic model setup in modern econometric analysis of program evaluation. Secondly, primary attention goes to the focus on causal effect estimation of macroeconomic policy with single time series data together with some extensions to multiple time series data. Furthermore, we examine the connection of this new approach to traditional macroeconomic models for policy analysis and evaluation. Finally, we conclude by addressing some possible future research directions in statistics and econometrics.

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“…) . Obviously, the asymptotic normality in (19) can be used for making inferences for ∆ 1 provided that a consistent estimate of Σ in (19) is available.…”

Section: Model Setupmentioning

confidence: 99%

Statistical Analysis and Evaluation of Macroeconomic Policies: A Selective Review

Liu

Cai

Fang

et al. 2020

Appl. Math. J. Chin. Univ.

View full text Add to dashboard Cite

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“…It is of interest to investigate other type tests such as the testing procedures proposed in Donald et al (2014) and Chen et al (2018) for time series data and our results may be applicable to the aforementioned testing procedures. This extension is left as a future research topic.…”

Section: Discussionmentioning

confidence: 96%

“…For future research, an open but challenging question is how to deal with the dimensionality problem in a general case that the policy choice is not binary. It is of interest to investigate other type tests such as the testing procedures proposed in Donald et al ( 2014) and Chen et al (2018) for time series data and our results may be applicable to the aforementioned testing procedures. This extension is left as a future research topic.…”

Section: Discussionmentioning

confidence: 98%

“…For example, by using a binary instrumental variable, Donald, Hsu, and Lieli (2014) proposed a Durbin-Wu-Hausman type statistic, a popular approach in the econometrics literature, to test the conditional mean independence which is a central identification assumption for economic policy evaluation. With a presumption that the error terms in both the outcome equation and the selection equation are symmetrically distributed, Chen, Ji, Zhou, and Zhu (2018) proposed a Kolmogorov-Smirnov type statistic to test conditional mean independence by comparing two estimators obtained with/without conditional mean independence. Our paper is closely related to the work in Fang et al (2020) which tested CI by using auxiliary variables for iid data.…”

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confidence: 99%

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Testing conditional independence in casual inference for time series data

Cai,

Fang,

Lin

et al. 2023

Statistica Neerlandica

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In this paper, we propose a new procedure to test conditional independence assumption in studying casual inference for time series data. The conditional independence assumption is transformed to a nonparametric conditional moment test with the help of auxiliary variables which are allowed to affect policy choice but the dependence can be fully captured by potential outcomes and observable controls. When the policy choice is binary, a nonparametric statistic test is developed further for testing the conditional independence assumption conditional on policy propensity score. Under some regular conditions, we show that the proposed test statistics are asymptotically normal under the null hypotheses for time series data. In addition, the performances of the proposed methods are illustrated through Monte Carlo simulations and a real example considered in Angrist and Kuersteiner (2011).This article is protected by copyright. All rights reserved.

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