Conditional moment models under semi-strong identification

Antoine, Bertille; Lavergne, Pascal

doi:10.1016/j.jeconom.2014.04.008

Cited by 22 publications

(16 citation statements)

References 29 publications

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“…The practical relevance of heteroscedasticity in linear instrumental variable (IV) regression has been highlighted before by Antoine and Lavergne (2012), Chao and Newey (2012), and Hausman et al (2012). We show, more generally, that departures from the conditionally homoscedastic serially uncorrelated framework affect the weak instrument asymptotic distribution of both the two-stage least squares (TSLS) and the limited information maximum likelihood (LIML) estimators.…”

Section: Introductionmentioning

confidence: 63%

A Robust Test for Weak Instruments

Olea

Pflueger

2013

Journal of Business & Economic Statistics

768

298

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We develop a test for weak instruments in linear instrumental variables regression that is robust to heteroscedasticity, autocorrelation, and clustering. Our test statistic is a scaled nonrobust first-stage F statistic. Instruments are considered weak when the two-stage least squares or the limited information maximum likelihood Nagar bias is large relative to a benchmark. We apply our procedures to the estimation of the elasticity of intertemporal substitution, where our test cannot reject the null of weak instruments in a larger number of countries than the test proposed by Stock and Yogo in 2005. Supplementary materials for this article are available online.

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Section: Introductionmentioning

confidence: 63%

A Robust Test for Weak Instruments

Olea

Pflueger

2013

Journal of Business & Economic Statistics

768

298

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“…and regress the logarithm of such ratio on the logarithm of the sample size. According to asymptotic results developed in Antoine and Renault (2009) and Antoine and Lavergne (2014), the estimator of a is strongly identified with a standard rate of convergence ffiffiffiffi T p , whereas estimators of b and c may not be as strongly identified. As a result, we expect the slope coefficients of the above regression to be positive coefficients between 0 and 0.5: the closer to zero, the stronger the identification.…”

Section: Resultsmentioning

confidence: 99%

“…In the i.i.d. case, Antoine and Lavergne (2014) show that the bandwidth dependent SMD estimatorĥ T h ð Þ defined in (5.9) is consistent under semi-strong identification for any chosen fixed bandwidth h. However, as the gradient of the objective function flattens under semi-strong identification, the solution of the first-order conditions can be numerically quite dispersed and unstable in practice. To avoid such a behavior, Antoine and Lavergne (2014) propose instead the following WMD estimator:…”

Section: Weak Identificationmentioning

confidence: 93%

Pseudo-True SDFs in Conditional Asset Pricing Models*

Antoine

Proulx

Renault

2018

Journal of Financial Econometrics

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This article is motivated by the need to bridge some gap between modern asset pricing theory and recent developments in econometric methodology. While asset pricing theory enhances the use of conditional pricing models, econometric inference of conditional models can be challenging due to misspecification or weak identification. To tackle the case of misspecification, we utilize the conditional Hansen and Jagannathan (1997) (HJ) distance as studied by Gagliardini and Ronchetti (2016), but we set the focus on interpretation and estimation of the pseudo-true value defined as the argument of the minimum of this distance. While efficient Generalized Method of Moments (GMM) has no meaning for estimation of a pseudo-true value, the HJ-distance not only delivers a meaningful loss function, but also features an additional advantage for the interpretation and estimation of managed portfolios whose exact pricing characterizes the pseudo-true pricing kernel (stochastic discount factor (SDF)). For conditionally affine pricing kernels, we can display some managed portfolios which are well-defined independently of the pseudo-true value of the parameters, although their exact pricing is achieved by the pseudo-true SDF. For the general case of nonlinear SDFs, we propose a smooth minimum distance (SMD) estimator (Lavergne and Patilea, 2013) that avoids a focus on specific directions as in the case of managed portfolios. Albeit based on kernel smoothing, the SMD approach avoids instabilities and the resulting need of trimming strategies displayed by classical local GMM estimators when the density function of the conditioning variables may take arbitrarily small values. In addition, the fact that SMD may allow fixed bandwidth asymptotics is helpful regarding the curse of dimensionality. In contrast with the true unknown value for a well-specified model, the estimated pseudo-true value, albeit defined in a time-invariant (unconditional) way, may actually depend on the choice of the state variables that define fundamental factors and their scaling weights. Therefore, we may not want to be overly parsimonious about the set of explanatory variables. Finally, following Antoine and Lavergne (2014), we show how SMD can be further robustified to deal with weaker identification contexts. Since SMD can be seen as a local extension of the method of jackknife GMM (Newey and Windmeijer, 2009), we characterize the Gaussian * We gratefully acknowledge very helpful comments by Lars Peter Hansen and Sydney Ludvigson on an earlier draft of this article. We are also grateful to participants of the 2017 EC 2 conference in Amsterdam for insightful questions and remarks.

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“…The reduced information set is strictly included in the joint distribution of (z 1;t ; z 2;t ), but it is larger than the sole knowledge of the marginal distribution of y ;t . 1 and focusing on the estimation of ( 1 ; )). In this respect, Assumption 2 is quite strong.…”

Section: The Icgmm Estimatorsmentioning

confidence: 99%

“…Arguably, the two-step SMD procedure would outperform the basic SMD in the presence of several CMRs as it would permits to exploit the information content of the cross-covariances of the CMRs. We conduct a last simulation experiment aimed at assessing the sensitivity of the performance of the ICGMM2 estimator to the choice of the number of the discretization points n. Given that h (1) t ( ; ; C) depends on seven parameters, n = 7 is the minimum number of discretization points that can be used to compare the estimators based on the moment functions h (2) t ( ; ), h (3) t ( ; ) and h (1) t ( ; ; C). Table 3 presents the simulation results for n = 7 and n = 10 (see Table 2 for the case n = 13).…”

Section: Simulation Studymentioning

confidence: 99%

The indirect continuous-GMM estimation

Kotchoni

2014

Computational Statistics & Data Analysis

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A curse of dimensionality arises when using the Continuum-GMM procedure to estimate large dimensional models. Two solutions are proposed, both of which convert the high dimensional model into a continuum of reduced information sets. Under certain regularity conditions, each reduced information set can be used to produce a consistent estimator of the parameter of interest. An indirect CGMM estimator is obtained by optimally aggregating all such consistent estimators. The simulation results suggest that the indirect CGMM procedure makes an e¢cient use of the information content of moment restrictions.

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Conditional moment models under semi-strong identification

Cited by 22 publications

References 29 publications

A Robust Test for Weak Instruments

A Robust Test for Weak Instruments

Pseudo-True SDFs in Conditional Asset Pricing Models*

The indirect continuous-GMM estimation

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