Averaging of an Increasing Number of Moment Condition Estimators

Chen, Xiaohong; Jacho-Chávez, David T.; Linton, Oliver

doi:10.1017/s0266466614000851

“…Recent contributions to the literature on estimation with many moments and on the combination of many estimators include Han and Phillips (2006) and Chen, Jacho-Chávez, and Linton (2016). The OMD procedure discussed in Chen et al (2016) is similar to the estimator introduced in section V, the implementation of which is described in the previous subsection.…”

Section: B Composite Likelihood Estimatormentioning

confidence: 99%

“…Recent contributions to the literature on estimation with many moments and on the combination of many estimators include Han and Phillips (2006) and Chen, Jacho-Chávez, and Linton (2016). The OMD procedure discussed in Chen et al (2016) is similar to the estimator introduced in section V, the implementation of which is described in the previous subsection. Chen et al (2016) discuss conditions on the rate of growth of the number of estimators with the sample size under which standard inference applies (see, e.g., their theorem 3).…”

Section: B Composite Likelihood Estimatormentioning

confidence: 99%

Estimation in the Fixed-Effects Ordered Logit Model

Muris

¹

2017

The Review of Economics and Statistics

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This paper introduces a new estimator for the fixed-effects ordered logit model. The proposed method has two advantages over existing estimators. First, it estimates the differences in the cut points along with the regression coefficient, leading to provide bounds on partial effects. Second, the proposed estimator for the regression coefficient is more efficient. I use the fact that the ordered logit model with J outcomes and T observations can be converted to a binary choice logit model in (J − 1) T ways. As an empirical illustration, I examine the income-health gradient for children using the Medical Expenditure Panel Survey.

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“…This can be established using for example the arguments of Chen et al (2016), henceforth CJL, who consider averaging of estimators in a different context. In their framework, there is no heterogeneity, i.e., θ 0i = θ 0 for all i.…”

Section: Asymptotic Theory For the Mean Group Estimatormentioning

confidence: 99%

A discrete choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance

Boneva¹,

Linton²

2017

Self Cite

1

0

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What is the effect of funding costs on the conditional probability of issuing a corporate bond? We study this question in a novel dataset covering 5610 issuances by US firms over the period from 1990 to 2014. Identification of this effect is complicated because of unobserved, common shocks such as the global financial crisis. To account for these shocks, we extend the common correlated effects estimator to settings where outcomes are discrete. Both the asymptotic properties and the small sample behavior of this estimator are documented. We find that for non-financial firms, yields are negatively related to bond issuance but that effect is larger in the pre-crisis period.

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“…θ i is consistent and asymptotically normal at rate √ N . This can be established using for example the arguments of Chen et al (2016), henceforth CJL, who consider averaging of estimators in a different context. In their framework, there is no heterogeneity, i.e., θ 0i = θ 0 for all i.…”

Section: Asymptotic Theory For the Mean Group Estimatormentioning

confidence: 99%

A Discrete Choice Model for Large Heterogeneous Panels with Interactive Fixed Effects with an Application to the Determinants of Corporate Bond Issuance

Boneva

¹

,

Linton

²

2016

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What is the effect of funding costs on the conditional probability of issuing a corporate bond? We study this question in a novel dataset covering 5610 issuances by US firms over the period from 1990 to 2014. Identification of this effect is complicated because of unobserved, common shocks such as the global financial crisis. To account for these shocks, we extend the common correlated effects estimator to settings where outcomes are discrete. Both the asymptotic properties and the small sample behavior of this estimator are documented. We find that for non-financial firms, yields are negatively related to bond issuance but that effect is larger in the pre-crisis period. AbstractWhat is the effect of funding costs on the conditional probability of issuing a corporate bond? We study this question in a novel dataset covering 5610 issuances by US firms over the period from 1990 to 2014. Identification of this effect is complicated because of unobserved, common shocks such as the global financial crisis. To account for these shocks, we extend the common correlated effects estimator to settings where outcomes are discrete. Both the asymptotic properties and the small sample behavior of this estimator are documented. We find that for non-financial firms, yields are negatively related to bond issuance but that effect is larger in the pre-crisis period.

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Averaging of an Increasing Number of Moment Condition Estimators

Cited by 20 publications

References 63 publications

Estimation in the Fixed-Effects Ordered Logit Model

Estimation in the Fixed-Effects Ordered Logit Model

A discrete choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance

A Discrete Choice Model for Large Heterogeneous Panels with Interactive Fixed Effects with an Application to the Determinants of Corporate Bond Issuance

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