Should We Go One Step Further? An Accurate Comparison of One-Step and Two-Step Procedures in a Generalized Method of Moments Framework

Hwang, Jungbin; Sun, Yixiao

doi:10.2139/ssrn.2646326

Cited by 17 publications

(15 citation statements)

References 35 publications

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“…We particularly use system GMM rather than difference GMM as the former provides a more precise estimation addressing the problems of unit root property (Bond, 2002). Further, we use two-step estimation, as it has smaller asymptotic variance in comparison to one-step estimation (Hwang and Sun, 2018). To test the validity of the instrument and over-identifying restrictions in GMM dynamic model estimation, the Sargan test is used.…”

Section: Definition Of Variablesmentioning

confidence: 99%

Gender diversity and financial performance in an emerging economy: empirical evidencefrom India

Maji

Saha

2021

MRR

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Purpose This paper aims to examine the impact of gender diversity both at operational and leadership levels on the financial performance of firms in India. Design/methodology/approach The study is based on a panel data set of 100 large Indian corporate firms. This study uses the Blau index and Shannon index to compute gender diversity. First, this paper uses system generalized method of moments model to deal with the potential endogeneity issue in the association between gender diversity and firm performance. Second, to unveil heterogeneity in such a relationship, the study applies panel data quantile regression model. Finally, the study adopts a generalized estimating equation model to investigate such relationships for group affiliated and standalone firms. Findings This study finds a significant positive impact of workforce gender diversity and board gender diversity on the financial performance of firms. Further, the results of the quantile regression model indicate that the impact of gender diversity (workforce and board) on firm performance is more pronounced at higher quantiles of the conditional distribution of firm performance. However, the study fails to extricate any significant impact of audit committee gender diversity on firm performance. Finally, the study also finds a significant positive impact of gender diversity at both workforce and board level for a group affiliated, as well as standalone firms. Originality/value The present study makes a novel contribution to the extant literature on the association between gender diversity and financial performance of firms by examining such diversity at both operational and leadership levels in the context of an emerging country such as India that captures the complex realities pertaining to gender issues. Further, the study contributes to the empirical literature regarding the heterogeneous impact of gender diversity on firm performance in the Indian context.

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Section: Definition Of Variablesmentioning

confidence: 99%

Gender diversity and financial performance in an emerging economy: empirical evidencefrom India

Maji

Saha

2021

MRR

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“…In finite sample, g n (θ 0 ) = 0 because g n (θ) = 0 for all θ, and this causes extra variations through the terms in (24) and (26). Similar to the Windmeijer correction, by taking into account for these (asymptotically negligible) terms in estimating the variance we can make more accurate inference.…”

Section: Assume Thatmentioning

confidence: 99%

A Doubly Corrected Robust Variance Estimator for Linear GMM

2019

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We propose a new finite sample corrected variance estimator for the linear generalized method of moments (GMM) including the one-step, two-step, and iterated estimators. Our formula additionally corrects for the over-identification bias in variance estimation on top of the commonly used finite sample correction of Windmeijer (2005) which corrects for the bias from estimating the efficient weight matrix, so is doubly corrected. Formal stochastic expansions are derived to show the proposed double correction estimates the variance of some higher-order terms in the expansion. In addition, the proposed double correction provides robustness to misspecification of the moment condition. In contrast, the conventional variance estimator and the Windmeijer correction are inconsistent under misspecification. That is, the proposed double correction formula provides a convenient way to obtain improved inference under correct specification and robustness against misspecification at the same time.1 two-step linear GMM. Specifically, his formula corrects for the bias arising from using the efficient weight matrix being evaluated at an estimate, rather than the true value. The correction formula (the Windmeijer correction, hereinafter) has been routinely used in practice. 1 However, the Windmeijer correction does not take into account for the over-identification bias, which is another important source of bias in the GMM standard error. The over-identification bias arises from the fact that the over-identified sample moment condition is nonzero while it converges in probability to zero under correct specification.We propose a new finite sample correction which takes into account for the over-identification bias for the variance of the linear one-step, two-step, and iterated GMM estimators. For one-step GMM such as the two-stage least squares (2SLS) estimators, the proposed finite sample correction is new as the Windmeijer correction does not cover the one-step GMM. For two-step and iterated GMM, the proposed correction improves upon the Windmeijer correction by additionally correcting for the over-identification bias. Thus, we doubly correct the finite sample bias of the linear GMM variance estimator. We provide an additional formal justification of the proposed double correction by deriving the stochastic expansions of the GMM estimators under local misspecification where the moment condition is modeled as a local drifting sequence around zero. The result shows that the double correction estimates the (co)variance of some higher-order terms which increase with the overidentification bias. Rothenberg (1984) provides a general treatment of the stochastic expansion and Newey and Smith (2004) derive the stochastic expansion of GMM and the generalized empirical likelihood (GEL) estimators under correct specification. The order of our double correction equals the order of the sample moment condition. Under correct specification or local misspecification, these terms are O p (n −1/2 ) so that the double correction is a finite-sample corr...

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“…5 5 See Hwang and Sun (2018) for a detailed comparison between one‐step and two‐step GMM estimators. …”

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confidence: 99%

The determinants of export sophistication: Does digitalization matter?

Atasoy

2020

Int. J Fin Econ

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Due to innovation, productivity surged and the structure of exports shifted towards sophisticated products in recent decades. In this study, I analyse the determinants of export sophistication with a special focus on the effects of digitalization for 61 countries by employing three measures of export sophistication. To my knowledge, this is the first study that investigates the nexus between digitalization and sophistication. It is found that exports get more sophisticated as digitalization promotes. In line with the literature, institutional quality, access to credit, and R&D are concluded to be other drivers of export sophistication. However, the evidence on tertiary enrolment and FDIs is mixed. Finally, the level of democracy seems to have a limited effect on sophistication indicating countries that have an autocratic rule also have a chance to sophisticate their exports. In the light of these findings, governments are advised to improve property rights, rule of law, the ease of doing business, access to credit, tertiary enrolment, and R&D expenditures to sophisticate their export baskets.

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Should We Go One Step Further? An Accurate Comparison of One-Step and Two-Step Procedures in a Generalized Method of Moments Framework

Cited by 17 publications

References 35 publications

Gender diversity and financial performance in an emerging economy: empirical evidencefrom India

Gender diversity and financial performance in an emerging economy: empirical evidencefrom India

A Doubly Corrected Robust Variance Estimator for Linear GMM

The determinants of export sophistication: Does digitalization matter?

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