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Journal articles on the topic 'Generalized Method of Moments'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Dell'Aquila, Rosario. "Generalized Method of Moments." Journal of the American Statistical Association 101, no. 475 (2006): 1309–10. http://dx.doi.org/10.1198/jasa.2006.s120.

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2

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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3

Kuersteiner, Guido M., and Laszlo Matyas. "Generalized Method of Moments Estimation." Journal of the American Statistical Association 95, no. 451 (2000): 1014. http://dx.doi.org/10.2307/2669498.

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4

Yin, Guosheng, Yanyuan Ma, Faming Liang, and Ying Yuan. "Stochastic Generalized Method of Moments." Journal of Computational and Graphical Statistics 20, no. 3 (2011): 714–27. http://dx.doi.org/10.1198/jcgs.2011.09210.

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5

Čížek, Pavel. "Generalized method of trimmed moments." Journal of Statistical Planning and Inference 171 (April 2016): 63–78. http://dx.doi.org/10.1016/j.jspi.2015.11.004.

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6

Yin, Guosheng. "Bayesian generalized method of moments." Bayesian Analysis 4, no. 2 (2009): 191–207. http://dx.doi.org/10.1214/09-ba407.

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7

Andrews, Donald W. K. "Consistent Moment Selection Procedures for Generalized Method of Moments Estimation." Econometrica 67, no. 3 (1999): 543–63. http://dx.doi.org/10.1111/1468-0262.00036.

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8

Hansen, Bruce E., and Kenneth D. West. "Generalized Method of Moments and Macroeconomics." Journal of Business & Economic Statistics 20, no. 4 (2002): 460–69. http://dx.doi.org/10.1198/073500102288618603.

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9

K. Newey, Whitney. "Generalized method of moments specification testing." Journal of Econometrics 29, no. 3 (1985): 229–56. http://dx.doi.org/10.1016/0304-4076(85)90154-x.

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10

Hunana, P., T. Passot, E. Khomenko, et al. "Generalized Fluid Models of the Braginskii Type." Astrophysical Journal Supplement Series 260, no. 2 (2022): 26. http://dx.doi.org/10.3847/1538-4365/ac5044.

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Abstract Several generalizations of the well-known fluid model of Braginskii (1965) are considered. We use the Landau collisional operator and the moment method of Grad. We focus on the 21-moment model that is analogous to the Braginskii model, and we also consider a 22-moment model. Both models are formulated for general multispecies plasmas with arbitrary masses and temperatures, where all of the fluid moments are described by their evolution equations. The 21-moment model contains two “heat flux vectors” (third- and fifth-order moments) and two “viscosity tensors” (second- and fourth-order

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11

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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12

BORA, DHRUBA JYOTI, MUNINDRA BORAH, and ABHIJIT BHUYAN. "Regional analysis of maximum rainfall using L-moment and LH-moment: A comparative case study for the northeast India." MAUSAM 68, no. 3 (2021): 451–62. http://dx.doi.org/10.54302/mausam.v68i3.677.

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Rainfall data of the northeast region of India has been considered for selecting best fit model for rainfall frequency analysis. The methods of L-moment has been employed for estimation of parameters five probability distributions, namely Generalized extreme value (GEV), Generalized Logistic(GLO), Pearson type 3 (PE3), 3 parameter Log normal (LN3) and Generalized Pareto (GPA) distributions. The methods of LH-moment of four orders (L1 L2, L3 & L4-moments) have also been used for estimating the parameters of three probability distributions namely Generalized extreme value (GEV), Generalized

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13

Chatelain, Jean-Bernard. "Improving consistent moment selection procedures for generalized method of moments estimation." Economics Letters 95, no. 3 (2007): 380–85. http://dx.doi.org/10.1016/j.econlet.2006.11.011.

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14

Jagannathan, Ravi, Georgios Skoulakis, and Zhenyu Wang. "Generalized Methods of Moments." Journal of Business & Economic Statistics 20, no. 4 (2002): 470–81. http://dx.doi.org/10.1198/073500102288618612.

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15

Imbens, Guido W. "Generalized Method of Moments and Empirical Likelihood." Journal of Business & Economic Statistics 20, no. 4 (2002): 493–506. http://dx.doi.org/10.1198/073500102288618630.

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16

Lewbel, Arthur. "A local generalized method of moments estimator." Economics Letters 94, no. 1 (2007): 124–28. http://dx.doi.org/10.1016/j.econlet.2006.08.011.

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17

Givens, Gregory E., and Michael K. Salemi. "Generalized method of moments and inverse control." Journal of Economic Dynamics and Control 32, no. 10 (2008): 3113–47. http://dx.doi.org/10.1016/j.jedc.2007.11.007.

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18

Loisel, Sébastien, and Marina Takane. "Fast indirect robust generalized method of moments." Computational Statistics & Data Analysis 53, no. 10 (2009): 3571–79. http://dx.doi.org/10.1016/j.csda.2009.03.021.

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19

K. Srinivasa Rao, V. Rohini Kumari,. "Three Parameter Generalized Gaussian Type Distribution." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 6863–70. http://dx.doi.org/10.52783/tjjpt.v44.i4.2479.

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In this article we introduced a three parameter generalized Gaussian type distribution. The various distributional properties like probability density function, moment generating function, the central moments etc., are derived. The estimators of the parameters are also obtained through the method of moments and maximum likelihood method of estimation. This distribution is useful in analyzing the random phenomenon arising at places like, agricultural experiments, biological studies, image processingetc,. This distribution also includes several of the earlier distributions like unimodal, bimodal

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20

Friebel, Ludvík, and Jana Friebelová. "Aproximation with generalized lambda distribution using method of moments." Acta Universitatis Bohemiae Meridionalis 10, no. 1 (2012): 93–96. http://dx.doi.org/10.32725/acta.2007.014.

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21

Hall, Alastair R., Atsushi Inoue, Kalidas Jana, and Changmock Shin. "Information in generalized method of moments estimation and entropy-based moment selection." Journal of Econometrics 138, no. 2 (2007): 488–512. http://dx.doi.org/10.1016/j.jeconom.2006.05.006.

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22

Luong, Andrew. "Generalized Method of Moments and Generalized Estimating Functions Using Characteristic Function." Open Journal of Statistics 10, no. 03 (2020): 581–99. http://dx.doi.org/10.4236/ojs.2020.103035.

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23

Morrison, Hugh, Matthew R. Kumjian, Charlotte P. Martinkus, Olivier P. Prat, and Marcus van Lier-Walqui. "A General N-Moment Normalization Method for Deriving Raindrop Size Distribution Scaling Relationships." Journal of Applied Meteorology and Climatology 58, no. 2 (2019): 247–67. http://dx.doi.org/10.1175/jamc-d-18-0060.1.

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AbstractA general drop size distribution (DSD) normalization method is formulated in terms of generalized power series relating any DSD moment to any number and combination of reference moments. This provides a consistent framework for comparing the variability of normalized DSD moments using different sets of reference moments, with no explicit assumptions about the DSD functional form (e.g., gamma). It also provides a method to derive any unknown moment plus an estimate of its uncertainty from one or more known moments, which is relevant to remote sensing retrievals and bulk microphysics sch

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24

Wang, Weiwei, Qi Zhang, Xinyu Zhang, and Xinmin Li. "Model averaging based on generalized method of moments." Economics Letters 200 (March 2021): 109735. http://dx.doi.org/10.1016/j.econlet.2021.109735.

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25

Jesus, Joao, and Richard E. Chandler. "Estimating functions and the generalized method of moments." Interface Focus 1, no. 6 (2011): 871–85. http://dx.doi.org/10.1098/rsfs.2011.0057.

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Estimating functions provide a very general framework for statistical inference, and are particularly useful when one is either unable or unwilling to specify a likelihood function. This paper aims to provide an accessible review of estimating function theory that has potential for application to the analysis and modelling of a wide range of complex systems. Assumptions are given in terms that can be checked relatively easily in practice, and some of the more technical derivations are relegated to an online supplement for clarity of exposition. The special case of the generalized method of mom

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26

Bobee, Bernard, and Fahim Ashkar. "Generalized Method of Moments Applied to LP3 Distribution." Journal of Hydraulic Engineering 114, no. 8 (1988): 899–909. http://dx.doi.org/10.1061/(asce)0733-9429(1988)114:8(899).

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27

Imbens, Guido W., and Richard Spady. "Confidence intervals in generalized method of moments models." Journal of Econometrics 107, no. 1-2 (2002): 87–98. http://dx.doi.org/10.1016/s0304-4076(01)00114-2.

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28

Mamuangbon, Supitcha, Kamon Budsaba, and Andrei Volodin. "The Performance of Estimators for Generalization of Crack Distribution." WSEAS TRANSACTIONS ON MATHEMATICS 20 (April 2, 2021): 106–11. http://dx.doi.org/10.37394/23206.2021.20.11.

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In this research, we propose a new four parameter family of distributions called Generalized Crack distribution. We generalizes the family three parameter Crack distribution. The Generalized Crack distribution is a mixture of two parameter Inverse Gaussian distribution, Length-Biased Inverse Gaussian distribution, Twice Length-Biased Inverse Gaussian distribution, and adding one more weight parameter . It is a special case for , where and is the weighted parameter. We investigate the properties of Generalized Crack distribution including first four moments, parameters estimation by using the m

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29

Mikhaylov, A. S., and V. S. Mikhaylov. "On an application of the Boundary control method to classical moment problems." Journal of Physics: Conference Series 2092, no. 1 (2021): 012002. http://dx.doi.org/10.1088/1742-6596/2092/1/012002.

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Abstract We establish relationships between the classical moments problems which are problems of a construction of a measure supported on a real line, on a half-line or on an interval from prescribed set of moments with the Boundary control approach to a dynamic inverse problem for a dynamical system with discrete time associated with Jacobi matrices. We show that the solution of corresponding truncated moment problems is equivalent to solving some generalized spectral problems.

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30

Dault, D., and B. Shanker. "An Interior Penalty Method for the Generalized Method of Moments." IEEE Transactions on Antennas and Propagation 63, no. 8 (2015): 3561–68. http://dx.doi.org/10.1109/tap.2015.2430876.

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31

Mat Jan, Nur Amalina, Ani Shabri, and Ruhaidah Samsudin. "Handling non-stationary flood frequency analysis using TL-moments approach for estimation parameter." Journal of Water and Climate Change 11, no. 4 (2019): 966–79. http://dx.doi.org/10.2166/wcc.2019.055.

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Abstract Non-stationary flood frequency analysis (NFFA) plays an important role in addressing the issue of the stationary assumption (independent and identically distributed flood series) that is no longer valid in infrastructure-designed methods. This confirms the necessity of developing new statistical models in order to identify the change of probability functions over time and obtain a consistent flood estimation method in NFFA. The method of Trimmed L-moments (TL-moments) with time covariate is confronted with the L-moment method for the stationary and non-stationary generalized extreme v

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32

Zhang, Luwen, and Li Wang. "Generalized Method of Moments Estimation of Realized Stochastic Volatility Model." Journal of Risk and Financial Management 16, no. 8 (2023): 377. http://dx.doi.org/10.3390/jrfm16080377.

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The purpose of this paper is to study the generalized method of moments (GMM) estimation procedures of the realized stochastic volatility model; we give the moment conditions for this model and then obtain the estimation of parameters. Then, we apply these moment conditions to the realized stochastic volatility model to improve the volatility prediction effect. This paper selects the Shanghai Composite Index (SSE) as the original data of model research and completes the volatility prediction under a realized stochastic volatility model. Markov chain Monte Carlo (MCMC) estimation and quasi-maxi

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33

Ashkar, F., B. Bobée, D. Leroux, and D. Morisette. "The generalized method of moments as applied to the generalized gamma distribution." Stochastic Hydrology and Hydraulics 2, no. 3 (1988): 161–74. http://dx.doi.org/10.1007/bf01550839.

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34

Shiea, Mohsen, Antonio Buffo, Marco Vanni, and Daniele Marchisio. "Numerical Methods for the Solution of Population Balance Equations Coupled with Computational Fluid Dynamics." Annual Review of Chemical and Biomolecular Engineering 11, no. 1 (2020): 339–66. http://dx.doi.org/10.1146/annurev-chembioeng-092319-075814.

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This review article discusses the solution of population balance equations, for the simulation of disperse multiphase systems, tightly coupled with computational fluid dynamics. Although several methods are discussed, the focus is on quadrature-based moment methods (QBMMs) with particular attention to the quadrature method of moments, the conditional quadrature method of moments, and the direct quadrature method of moments. The relationship between the population balance equation, in its generalized form, and the Euler-Euler multiphase flow models, notably the two-fluid model, is thoroughly di

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35

Headrick, Todd C., and Mohan D. Pant. "A Doubling Method for the Generalized Lambda Distribution." ISRN Applied Mathematics 2012 (May 7, 2012): 1–19. http://dx.doi.org/10.5402/2012/725754.

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This paper introduces a new family of generalized lambda distributions (GLDs) based on a method of doubling symmetric GLDs. The focus of the development is in the context of L-moments and L-correlation theory. As such, included is the development of a procedure for specifying double GLDs with controlled degrees of L-skew, L-kurtosis, and L-correlations. The procedure can be applied in a variety of settings such as modeling events and Monte Carlo or simulation studies. Further, it is demonstrated that estimates of L-skew, L-kurtosis, and L-correlation are substantially superior to conventional

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36

Anghel, Cristian Gabriel, and Cornel Ilinca. "Predicting Flood Frequency with the LH-Moments Method: A Case Study of Prigor River, Romania." Water 15, no. 11 (2023): 2077. http://dx.doi.org/10.3390/w15112077.

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The higher-order linear moments (LH-moments) method is one of the most commonly used methods for estimating the parameters of probability distributions in flood frequency analysis without sample censoring. This article presents the relationships necessary to estimate the parameters for eight probability distributions used in flood frequency analysis. Eight probability distributions of three parameters using first- and second-order LH-moments are presented, namely the Pearson V distribution, the CHI distribution, the inverse CHI distribution, the Wilson–Hilferty distribution, the Pseudo-Weibull

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37

Abas, Asaf, Tamir Bendory, and Nir Sharon. "The Generalized Method of Moments for Multi-Reference Alignment." IEEE Transactions on Signal Processing 70 (2022): 1377–88. http://dx.doi.org/10.1109/tsp.2022.3157483.

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38

Li, Jia, and Dacheng Xiu. "Generalized Method of Integrated Moments for High-Frequency Data." Econometrica 84, no. 4 (2016): 1613–33. http://dx.doi.org/10.3982/ecta12306.

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39

Brown, Bryan W., and Whitney K. Newey. "Generalized Method of Moments, Efficient Bootstrapping, and Improved Inference." Journal of Business & Economic Statistics 20, no. 4 (2002): 507–17. http://dx.doi.org/10.1198/073500102288618649.

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40

Piatt, Sadie, and Allen C. Price. "Analyzing dwell times with the Generalized Method of Moments." PLOS ONE 14, no. 1 (2019): e0197726. http://dx.doi.org/10.1371/journal.pone.0197726.

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41

Otunuga, Olusegun M., Gangaram S. Ladde, and Nathan G. Ladde. "Local lagged adapted generalized method of moments and applications." Stochastic Analysis and Applications 35, no. 1 (2016): 110–43. http://dx.doi.org/10.1080/07362994.2016.1213640.

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42

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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43

Casale, Giuliano. "A generalized method of moments for closed queueing networks." Performance Evaluation 68, no. 2 (2011): 180–200. http://dx.doi.org/10.1016/j.peva.2010.08.026.

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44

Qian, Hailong, and Peter Schmidt. "Improved instrumental variables and generalized method of moments estimators." Journal of Econometrics 91, no. 1 (1999): 145–69. http://dx.doi.org/10.1016/s0304-4076(98)00074-8.

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45

Medgyesi-Mitschang, L. N., J. M. Putnam, and M. B. Gedera. "Generalized method of moments for three-dimensional penetrable scatterers." Journal of the Optical Society of America A 11, no. 4 (1994): 1383. http://dx.doi.org/10.1364/josaa.11.001383.

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46

Gordienko, A. B. "Generalized method of moments for calculation of optical functions." Russian Physics Journal 52, no. 12 (2009): 1352–54. http://dx.doi.org/10.1007/s11182-010-9376-3.

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47

Al Masry, Z., S. Mercier, and G. Verdier. "Generalized method of moments for an extended gamma process." Communications in Statistics - Theory and Methods 47, no. 15 (2017): 3687–714. http://dx.doi.org/10.1080/03610926.2017.1361988.

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48

Phillips, Robert F. "A numerical equivalence result for generalized method of moments." Economics Letters 179 (June 2019): 13–15. http://dx.doi.org/10.1016/j.econlet.2019.03.014.

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49

Salisu, Abubakar, Abdulhamid Ado Osi, Musa Uba Muhammad, Abubakar Yahaya, and Muhammad Haladu. "Development of Rayleigh-Exponentiated Odd Generalized-Weibull Distribution with Properties." Journal of Science Research and Reviews 2, no. 2 (2025): 115–25. https://doi.org/10.70882/josrar.2025.v2i2.68.

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We propose a new family of distributions called the Rayleigh-Exponentiated odd Generalized-Weibull Distribution with two positive parameters, which generalizes the Cordeiro and de Castro's family. Some special distributions in the new class are discussed. We derive the validity and some mathematical properties of the proposed distribution including explicit expressions for the quantile function, ordinary moments, Moment generating function, hazard and Survival function. The method of maximum likelihood is used to estimation of REOG-Weibull distributions. These functions are illustrated with gr

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50

Erickson, Timothy, Robert Parham, and Toni M. Whited. "Fitting the Errors-in-variables Model Using High-order Cumulants and Moments." Stata Journal: Promoting communications on statistics and Stata 17, no. 1 (2017): 116–29. http://dx.doi.org/10.1177/1536867x1701700107.

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In this article, we consider a multiple mismeasured regressor errors-in-variables model. We present xtewreg, a command for using two-step generalized method of moments and minimum distance estimators that exploit overidentifying information contained in high-order cumulants or moments of the data. The command supports cumulant or moment estimation, internal support for the bootstrap with moment condition recentering, an arbitrary number of mismeasured regressors and perfectly measured regressors, and cumulants or moments up to an arbitrary degree. We also demonstrate how to use the estimators

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