Chapter 36 Large sample estimation and hypothesis testing

Newey, Whitney K.; McFadden, Daniel

doi:10.1016/s1573-4412(05)80005-4

Cited by 2,082 publications

(2,299 citation statements)

References 65 publications

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“…These identities do not, however, hold in the generic nonlinear case so the consistency of each method must be individually examined. To prove the consistency of 2SRI, we cast it as a special case of the generic two-stage optimization estimator (see Newey and McFadden, 1994;White, 1994, Chapter 6;or Wooldridge, 2002, Chapter 12). For simplicity of exposition, let us assume that x e and x u each have only one element (i.e.…”

Section: Formal Treatment Of Consistency Properties Of the Estimatorsmentioning

confidence: 99%

Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling

Terza

Basu

Rathouz

2008

Journal of Health Economics

1,314

1,137

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The paper focuses on two estimation methods that have been widely used to address endogeneity in empirical research in health economics and health services research B two-stage predictor substitution (2SPS) and two-stage residual inclusion (2SRI). 2SPS is the rote extension (to nonlinear models) of the popular linear two-stage least squares estimator. The 2SRI estimator is similar except that in the second stage regression, the endogenous variables are not replaced by first-stage predictors. Instead, first-stage residuals are included as additional regressors. In a generic parametric framework, we show that 2SRI is consistent and 2SPS is not. Results from a simulation study and an illustrative example also recommend against 2SPS and favor 2SRI. Our findings are important given that there are many prominent examples of the application of inconsistent 2SPS in the recent literature. This study can be used as a guide by future researchers in health economics who are confronted with endogeneity in their empirical work.

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Section: Formal Treatment Of Consistency Properties Of the Estimatorsmentioning

confidence: 99%

Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling

Terza

Basu

Rathouz

2008

Journal of Health Economics

1,314

1,137

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“…is continuous at each b, w with probability one and its absolute value is bounded by 1, so applying Lemma 2.4 of Newey and McFadden, 1994, b F j (w; b) converges uniformly to φ (w, b) . Thus putting the two pieces of (A-2) together,…”

Section: A2 Proof Of Proposition 12mentioning

confidence: 99%

“…The function L N (F, G) converges uniformly over M to a nonrandom continuous function L : M → < For this we apply Lemma 2.4 in Newey and McFadden (1994). The likelihood function is clearly continuous and for any z the log likelihood will be bounded since the support is compact.…”

Section: Condition (I)mentioning

confidence: 99%

Inference with "Difference in Differences" with a Small Number of Policy Changes

2005

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Difference in differences methods have become very popular in applied work. This paper provides a new method for inference in these models when there are a small number of policy changes. This situation occurs in many implementations of these estimators. Identification of the key parameter typically arises when a group "changes" some particular policy. The asymptotic approximations that are typically employed assume that the number of cross sectional groups, N, times the number of time periods, T, is large. However, even when N or T is large, the number of actual policy changes observed in the data is often very small. In this case, we argue that point estimators of treatment effects should not be thought of as being consistent and that the standard methods that researchers use to perform inference in these models are not appropriate. We develop an alternative approach to inference under the assumption that there are a finite number of policy changes in the data, using asymptotic approximations as the number of non-changing groups gets large. In this situation we cannot obtain a consistent point estimator for the key treatment effect parameter. However, we can consistently estimate the finite-sample distribution of the treatment effect estimator, up to the unknown parameter itself. This allows us to perform hypothesis tests and construct confidence intervals. For expositional and motivational purposes, we focus on the difference in differences case, but our approach should be appropriate more generally in treatment effect models which employ a large number of controls, but a small number of treatments. We demonstrate the use of the approach by analyzing the effect of college merit aide programs on college attendance. We show that in some cases the standard approach can give misleading results.

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“…for (27) In order to derive the asymptotic properties of (30) we can cast it as a special case of the class of two-stage optimization (2SOPT) estimators discussed by White (1994), Newey and McFadden (1994), and Wooldridge (2003). 8 2SOPT …”

Section: The Pdom As a Two-stage Optimization Estimator: Asymptotic Imentioning

confidence: 99%

Delayed entry into employment after spinal cord injury: factors related to time to first job

et al. 2009

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Most empirical research in health economics is conducted with the goal of providing scientific evidence that will serve to inform current and future health policy. Such policy analytic studies typically use nonexperimental (survey) data and focus on a particular variable (the policy variable, x p ) that is at present, or will in the future be, under the control of a policy decision-making entity. The goal is the estimation of the effect that a prospective exogenous change in x p would have on a targeted outcome of interest (y) the policy effect. In the present paper we propose a pseudo-difference-of-means (PDOM) framework for policy effect estimation. The policy effect typically takes one of three forms (incremental, marginal, or treatment) depending on the nature of the policy variable (discrete, continuous, or binary), and on the type of change in x p under policy consideration. The full development of the PDOM estimator in each of its three forms is presented --from its intuitive conceptual foundations to the details of its practical implementation. In the design of the PDOM estimator, particular attention is paid to accommodating the potential endogeneity of the policy variable. The asymptotic properties of the PDOM are derived after showing that it can be cast as a special case of a general class of estimators that have been thoroughly studied in the econometrics literature.Three empirical applications of the PDOM are discussed --one for each of the alternative forms of the PDOM (incremental, marginal, treatment). In each of these examples the potential endogeneity of the policy variable was tested and found to be both statistically and substantively significant. JEL Classification: C31, I11

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Chapter 36 Large sample estimation and hypothesis testing

Cited by 2,082 publications

References 65 publications

Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling

Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling

Inference with "Difference in Differences" with a Small Number of Policy Changes

Delayed entry into employment after spinal cord injury: factors related to time to first job

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