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Relevant bibliographies by topics / Generalised Method of moments (GMM)

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Journal articles

Academic literature on the topic 'Generalised Method of moments (GMM)'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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Journal articles on the topic "Generalised Method of moments (GMM)"

1

Owusu Junior, Peterson, and Carl H. Korkpoe. "Generalised Lambda Distributions by Method of Moments and Maximum Likelihood using the JSE-ASI Returns." Asian Journal of Finance & Accounting 9, no. 1 (2017): 224. http://dx.doi.org/10.5296/ajfa.v9i1.10913.

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The four-parameter generalised lambda distribution provides the flexibility required to describe the key moments of any distribution as compared with the normal distribution which characterises the distribution with only two moments. As markets have increasingly become nervous, the inadequacies of the normal distribution in capturing correctly the tail events and describing fully the entire distribution of market returns have been laid bare. The focus of this paper is to compare the generalised method of moments (GMM) and maximum likelihood essential estimates (MLE) methods as subsets of the G

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2

Wooldridge, Jeffrey M. "Applications of Generalized Method of Moments Estimation." Journal of Economic Perspectives 15, no. 4 (2001): 87–100. http://dx.doi.org/10.1257/jep.15.4.87.

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I describe how the method of moments approach to estimation, including the more recent generalized method of moments (GMM) theory, can be applied to problems using cross section, time series, and panel data. Method of moments estimators can be attractive because in many circumstances they are robust to failures of auxiliary distributional assumptions that are not needed to identify key parameters. I conclude that while sophisticated GMM estimators are indispensable for complicated estimation problems, it seems unlikely that GMM will provide convincing improvements over ordinary least squares a

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3

Abdal, Nurul Mukhlisah, Wahyudin Nur, and Ainun Mawaddah Abdal. "Penaksiran Generalized Method of Moments dengan Penggunaan Metode Marquardt-Levenberg." Indonesian Journal of Fundamental Sciences 6, no. 1 (2020): 37. http://dx.doi.org/10.26858/ijfs.v6i1.13943.

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Generalized Method of Moments is a method for estimating parameters using sample moments. GMM is used by the researcher particularly in economics to determine econometrical models which their distribution function is hardly known. Not only for economics, but GMM also is useful for agriculture, transportation, health care, etc. Research methodology for this article is review of literature. This article describes the combination of GMM and Marquardt-Levenberg algorithm along with the example of its use

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4

Hahn, Jinyong. "A Note on Bootstrapping Generalized Method of Moments Estimators." Econometric Theory 12, no. 1 (1996): 187–97. http://dx.doi.org/10.1017/s0266466600006496.

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Recently, Arcones and Giné (1992, pp. 13–47, in R. LePage & L. Billard [eds.], Exploring the Limits of Bootstrap, New York: Wiley) established that the bootstrap distribution of the M-estimator converges weakly to the limit distribution of the estimator in probability. In contrast, Brown and Newey (1992, Bootstrapping for GMM, Seminar note) discovered that the bootstrap distribution of the GMM overidentification test statistic does not converge weakly to the x2 distribution. In this paper, it is shown that the bootstrap distribution of the GMM estimator converges weakly to the limit distri

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5

Hansen, Bruce E., and Seojeong Lee. "Inference for Iterated GMM Under Misspecification." Econometrica 89, no. 3 (2021): 1419–47. http://dx.doi.org/10.3982/ecta16274.

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This paper develops inference methods for the iterated overidentified Generalized Method of Moments (GMM) estimator. We provide conditions for the existence of the iterated estimator and an asymptotic distribution theory, which allows for mild misspecification. Moment misspecification causes bias in conventional GMM variance estimators, which can lead to severely oversized hypothesis tests. We show how to consistently estimate the correct asymptotic variance matrix. Our simulation results show that our methods are properly sized under both correct specification and mild to moderate misspecific

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6

Hu, Yi, Xiaohua Xia, Ying Deng, and Dongmei Guo. "Higher Order Mean Squared Error of Generalized Method of Moments Estimators for Nonlinear Models." Discrete Dynamics in Nature and Society 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/324904.

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Generalized method of moments (GMM) has been widely applied for estimation of nonlinear models in economics and finance. Although generalized method of moments has good asymptotic properties under fairly moderate regularity conditions, its finite sample performance is not very well. In order to improve the finite sample performance of generalized method of moments estimators, this paper studies higher-order mean squared error of two-step efficient generalized method of moments estimators for nonlinear models. Specially, we consider a general nonlinear regression model with endogeneity and deri

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7

NZIMANDE, NTOKOZO PATRICK, and HAROLD NGALAWA. "THE ENDOGENEITY OF BUSINESS CYCLE SYNCHRONISATION IN SOUTHERN AFRICAN DEVELOPMENT COMMUNITY." Global Economy Journal 19, no. 02 (2019): 1950010. http://dx.doi.org/10.1142/s2194565919500106.

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This paper investigates the endogeneity hypothesis of optimal currency area (OCA) criterion, that is, business cycles synchronisation, in a panel of Southern African Development Community (SADC) member countries, for the period 1994–2016. Using a Generalised Method of Moments (GMM), the study finds that, amongst other factors, trade induces business cycles comovement. This finding lends support to the endogeneity hypothesis of OCA theory.

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8

Lynch, Anthony W., and Jessica A. Wachter. "Using Samples of Unequal Length in Generalized Method of Moments Estimation." Journal of Financial and Quantitative Analysis 48, no. 1 (2013): 277–307. http://dx.doi.org/10.1017/s0022109013000070.

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AbstractThis paper describes estimation methods, based on the generalized method of moments (GMM), applicable in settings where time series have different starting or ending dates. We introduce two estimators that are more efficient asymptotically than standard GMM. We apply these to estimating predictive regressions in international data and show that the use of the full sample affects inference for assets with data available over the full period as well as for assets with data available for a subset of the period. Monte Carlo experiments demonstrate that reductions hold for small-sample stan

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9

Erickson, Timothy, and Toni M. Whited. "TWO-STEP GMM ESTIMATION OF THE ERRORS-IN-VARIABLES MODEL USING HIGH-ORDER MOMENTS." Econometric Theory 18, no. 3 (2002): 776–99. http://dx.doi.org/10.1017/s0266466602183101.

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We consider a multiple mismeasured regressor errors-in-variables model where the measurement and equation errors are independent and have moments of every order but otherwise are arbitrarily distributed. We present parsimonious two-step generalized method of moments (GMM) estimators that exploit overidentifying information contained in the high-order moments of residuals obtained by “partialling out” perfectly measured regressors. Using high-order moments requires that the GMM covariance matrices be adjusted to account for the use of estimated residuals instead of true residuals defined by pop

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10

Gallant, A. Ronald, and George Tauchen. "Which Moments to Match?" Econometric Theory 12, no. 4 (1996): 657–81. http://dx.doi.org/10.1017/s0266466600006976.

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We describe an intuitive, simple, and systematic approach to generating moment conditions for generalized method of moments (GMM) estimation of the parameters of a structural model. The idea is to use the score of a density that has an analytic expression to define the GMM criterion. The auxiliary model that generates the score should closely approximate the distribution' of the observed data but is not required to nest it. If the auxiliary model nests the structural model then the estimator is as efficient as maximum likelihood. The estimator is advantageous when expectations under a structur

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