Local GMM estimation of time series models with conditional moment restrictions

Gospodinov, Nikolay; Otsu, Taisuke

doi:10.1016/j.jeconom.2012.05.017

Cited by 14 publications

(9 citation statements)

References 44 publications

(79 reference statements)

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“…We first provide the large sample properties for the estimatorĥ T of the SDF parameter h in a correctly specified family. These properties are derived for example in Gospodinov and Otsu (2012).…”

Section: Supplementary Datamentioning

confidence: 99%

Comparing Asset Pricing Models by the Conditional Hansen-Jagannathan Distance*

Gagliardini

Ronchetti

2019

Journal of Financial Econometrics

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We compare nonnested parametric specifications of the stochastic discount factor (SDF) using the conditional Hansen–Jagannathan (HJ-) distance. This distance measures the discrepancy between a parametric model-implied SDF and the admissible SDF’s satisfying all the conditional (dynamic) no-arbitrage restrictions, instead of just few unconditional no-arbitrage restrictions for managed portfolios chosen through the instrument selection. We estimate the conditional HJ-distance by a generalized method of moments estimator and establish its large sample properties for model selection purposes. We compare empirically several SDF models including multifactor beta pricing specifications and some recently proposed SDF models that are conditionally linear in consumption growth.

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Section: Supplementary Datamentioning

confidence: 99%

Comparing Asset Pricing Models by the Conditional Hansen-Jagannathan Distance*

Gagliardini

Ronchetti

2019

Journal of Financial Econometrics

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“…To avoid the problem of too few local observations, we use a kernel function to assign weights to each observation, as in Smith (2007) and Gospodinov and Otsu (2012). Note that these two moments are implied by the thin-set identification, and conditioning on G 1 literally means selecting only the observations such that Δ t − 1 = 0.…”

Section: Moment Conditionsmentioning

confidence: 99%

Structural estimation of behavioral heterogeneity

Shi

Zheng

2018

J of Applied Econometrics

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Summary We develop a behavioral asset pricing model in which agents trade in a market with information friction. Profit‐maximizing agents switch between trading strategies in response to dynamic market conditions. Owing to noisy private information about the fundamental value, the agents form different evaluations about heterogeneous strategies. We exploit a thin set—a small sub‐population—to point identify this nonlinear model, and estimate the structural parameters using extended method of moments. Based on the estimated parameters, the model produces return time series that emulate the moments of the real data. These results are robust across different sample periods and estimation methods.

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“…A few other recent contributions stress the potential of the conditional generalized empirical likelihood (GEL) framework from an asymptotic theory point of view. Gospodinov and Otsu (2009) show that in an AR(1) model with iid errors the local GMM estimator, which is essentially the same as CEEL, has a higher order asymptotic bias smaller than the OLS estimator. Tripathi and Kitamura (2003) show that a test statistic for conditional moment restrictions based on the CEL objective function is asymptotically optimal in terms of a certain average power criterion.…”

Section: Introductionmentioning

confidence: 99%

“…The asymptotic theory of these estimators speci…es the rate at which the bandwidths should change with the sample size in order to obtain asymptotic e¢ ciency, but this does not provide a clear indication on how to choose the bandwidths in practice. For some models (e.g., the linear heteroskedastic model in KTA, or the AR(1) model with ARCH errors in Gospodinov and Otsu, 2009) di¤erent bandwidth values lead to similar estimates. For models with endogenous regressors, however, it is not known to what extent the …nite sample performance of these estimators is a¤ected, if one uses di¤erent bandwidths, or if one uses some bandwidth selection procedure.…”

Section: Introductionmentioning

confidence: 99%

On the finite-sample properties of conditional empirical likelihood estimators

Crudu

Sándor

2016

Communications in Statistics - Simulation and Computation

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We provide Monte Carlo evidence on the …nite sample behavior of the conditional empirical likelihood (CEL) estimator of Kitamura, Tripathi, and Ahn (2004) and the conditional Euclidean empirical likelihood (CEEL) estimator of Antoine, Bonnal, and Renault (2007) in the context of a heteroskedastic linear model with an endogenous regressor. We compare these estimators with three heteroskedasticity-consistent instrument-based estimators in terms of various performance measures. Our results suggest that the CEL and CEEL with …xed bandwidths may su¤er from the no-moment problem, similarly to the unconditional generalized empirical likelihood estimators studied by Guggenberger (2008). We also study the CEL and CEEL estimators with automatic bandwidths selected through cross-validation. We do not …nd evidence that these su¤er from the nomoment problem. When the instruments are weak, we …nd CEL and CEEL to have …nite sample properties -in terms of mean squared error and coverage probability of con…dence intervals-poorer than the heteroskedasticity-consistent Fuller (HFUL) estimator. In the strong instruments case the CEL and CEEL estimators with automatic bandwidths tend to outperform HFUL in terms of mean squared error, while the reverse holds in terms of the coverage probability, although the di¤erences in numerical performance are rather small.

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Local GMM estimation of time series models with conditional moment restrictions

Cited by 14 publications

References 44 publications

Comparing Asset Pricing Models by the Conditional Hansen-Jagannathan Distance*

Comparing Asset Pricing Models by the Conditional Hansen-Jagannathan Distance*

Structural estimation of behavioral heterogeneity

On the finite-sample properties of conditional empirical likelihood estimators

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