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Journal articles on the topic 'GMM estimator'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Lee, Lung-fei, and Xiaodong Liu. "EFFICIENT GMM ESTIMATION OF HIGH ORDER SPATIAL AUTOREGRESSIVE MODELS WITH AUTOREGRESSIVE DISTURBANCES." Econometric Theory 26, no. 1 (2009): 187–230. http://dx.doi.org/10.1017/s0266466609090653.

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In this paper, we extend the GMM framework for the estimation of the mixed-regressive spatial autoregressive model by Lee(2007a) to estimate a high order mixed-regressive spatial autoregressive model with spatial autoregressive disturbances. Identification of such a general model is considered. The GMM approach has computational advantage over the conventional ML method. The proposed GMM estimators are shown to be consistent and asymptotically normal. The best GMM estimator is derived, within the class of GMM estimators based on linear and quadratic moment conditions of the disturbances. The b

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Abonazel, Mohamed. "Bias correction methods for dynamic panel data models with fixed effects." International Journal of Applied Mathematical Research 6, no. 2 (2017): 58. http://dx.doi.org/10.14419/ijamr.v6i2.7774.

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This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is rem

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Standsyah, Rahmawati Erma, Bambang Widjanarko Otok, and Agus Suharsono. "Fixed Effect Meta-Analytic Structural Equation Modeling (MASEM) Estimation Using Generalized Method of Moments (GMM)." Symmetry 13, no. 12 (2021): 2273. http://dx.doi.org/10.3390/sym13122273.

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The fixed effect meta-analytic structural equation modeling (MASEM) model assumes that the population effect is homogeneous across studies. It was first developed analytically using Generalized Least Squares (GLS) and computationally using Weighted Least Square (WLS) methods. The MASEM fixed effect was not estimated analytically using the estimation method based on moment. One of the classic estimation methods based on moment is the Generalized Method of Moments (GMM), whereas GMM can possibly estimate the data whose studies has parameter uncertainty problems, it also has a high accuracy on da

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Abonazel, Mohamed R., Mohamed Abdallah, and El-Housainy A. Rady. "On New Two-Step GMM Estimation of the Panel Vector Autoregressive Models with Missing observations." WSEAS TRANSACTIONS ON MATHEMATICS 21 (September 20, 2022): 671–83. http://dx.doi.org/10.37394/23206.2022.21.79.

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Few estimation methods were discussed to handle the missing data problem in the panel data models. However, in the panel vector autoregressive (PVAR) model, there is no estimator to handle this problem. The traditional treatment in the case of incomplete data is to use the generalized method of moment (GMM) estimation based on only available data without imputation of the missing data. Therefore, this paper introduces a new GMM estimation for the PVAR model in case of incomplete data based on the mean imputation. Moreover, we make a Monte Carlo simulation study to study the efficiency of the p

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Caner, Mehmet. "LASSO-TYPE GMM ESTIMATOR." Econometric Theory 25, no. 1 (2009): 270–90. http://dx.doi.org/10.1017/s0266466608090099.

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This paper proposes the least absolute shrinkage and selection operator–type (Lasso-type) generalized method of moments (GMM) estimator. This Lasso-type estimator is formed by the GMM objective function with the addition of a penalty term. The exponent of the penalty term in the regular Lasso estimator is equal to one. However, the exponent of the penalty term in the Lasso-type estimator is less than one in the analysis here. The magnitude of the exponent is reduced to avoid the asymptotic bias. This estimator selects the correct model and estimates it simultaneously. In other words, this meth

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6

Cheng, Xu, Zhipeng Liao, and Ruoyao Shi. "On uniform asymptotic risk of averaging GMM estimators." Quantitative Economics 10, no. 3 (2019): 931–79. http://dx.doi.org/10.3982/qe711.

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This paper studies the averaging GMM estimator that combines a conservative GMM estimator based on valid moment conditions and an aggressive GMM estimator based on both valid and possibly misspecified moment conditions, where the weight is the sample analog of an infeasible optimal weight. We establish asymptotic theory on uniform approximation of the upper and lower bounds of the finite‐sample truncated risk difference between any two estimators, which is used to compare the averaging GMM estimator and the conservative GMM estimator. Under some sufficient conditions, we show that the asymptot

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Newey, Whitney K. "Asymptotic Equivalence of Closest Moments and GMM Estimators." Econometric Theory 4, no. 2 (1988): 336–40. http://dx.doi.org/10.1017/s0266466600012093.

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This note considers an asymptotic property of the class of closest moments estimators. Each such estimator is obtained by setting a vector of sample moments close to corresponding population moments. It is shown that each such estimator is asymptotically equivalent to a GMM estimator, which has a quadratic distance function. An implication of this result is that the estimator that is asymptotically efficient in the GMM class is also asymptotically efficient in the wider class of closest moment estimators.

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Kitamura, Yuichi, and Peter C. B. Phillips. "Efficient IV Estimation in Nonstationary Regression." Econometric Theory 11, no. 5 (1995): 1095–130. http://dx.doi.org/10.1017/s026646660000997x.

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A limit theory for instrumental variables (IV) estimation that allows for possibly nonstationary processes was developed in Kitamura and Phillips (1992, Fully Modified IV, GIVE, and GMM Estimation with Possibly Non-stationary Regressors and Instruments, mimeo, Yale University). This theory covers a case that is important for practitioners, where the nonstationarity of the regressors may not be of full rank, and shows that the fully modified (FM) regression procedure of Phillips and Hansen (1990) is still applicable. FM. versions of the generalized method of moments (GMM) estimator and the gene

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9

Lee, Ji Hyung, and Zhipeng Liao. "ON STANDARD INFERENCE FOR GMM WITH LOCAL IDENTIFICATION FAILURE OF KNOWN FORMS." Econometric Theory 34, no. 4 (2017): 790–814. http://dx.doi.org/10.1017/s026646661700024x.

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This paper studies the GMM estimation and inference problem that occurs when the Jacobian of the moment conditions is rank deficient of known forms at the true parameter values. Dovonon and Renault (2013) recently raised a local identification issue stemming from this type of degenerate Jacobian. The local identification issue leads to a slow rate of convergence of the GMM estimator and a nonstandard asymptotic distribution of the over-identification test statistics. We show that the known form of rank-deficient Jacobian matrix contains nontrivial information about the economic model. By explo

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Moyo, Vusani. "Dynamic Capital Structure Adjustment: Which estimator yields consistent and efficient estimates?" Journal of Economic and Financial Sciences 9, no. 1 (2017): 209–27. http://dx.doi.org/10.4102/jef.v9i1.38.

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The partial adjustment model is key to a number of corporate finance research areas. The model is by its nature an autoregressive-distributed lag model that is characterised by heterogeneity among individuals and autocorrelation due to the presence of the lagged dependent variable. Finding a suitable estimator to fit the model can be challenging, as the existing estimators differ significantly in their consistency and bias. This study used data drawn from 143 non-financial firms listed on the Johannesburg Stock Exchange (JSE) to test for the consistency and efficiency of the leading partial ad

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11

Shina, Arya Fendha Ibnu. "ESTIMASI PARAMETER PADA SISTEM MODEL PERSAMAAN SIMULTAN DATA PANEL DINAMIS DENGAN METODE 2 SLS GMM-AB." MEDIA STATISTIKA 11, no. 2 (2018): 79–91. http://dx.doi.org/10.14710/medstat.11.2.79-91.

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Single equation models ignore interdependencies or two-way relationships between response variables. The simultaneous equation model accommodates this two-way relationship form. Two Stage Least Square Generalized Methods of Moment Arellano and Bond (2 SLS GMM-AB) is used to estimate the parameters in the simultaneous system model of dynamic panel data if each structural equation is exactly identified or over identified. In the simultaneous equation system model with dynamic panel data, each structural equation and reduced form is a dynamic panel data regression equation. Estimation of structur

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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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Liu, Bin, Cindy Long Yu, Michael Joseph Price, and Yan Jiang. "Generalized Method of Moments Estimators for Multiple Treatment Effects Using Observational Data from Complex Surveys." Journal of Official Statistics 34, no. 3 (2018): 753–84. http://dx.doi.org/10.2478/jos-2018-0035.

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Abstract In this article, we consider a generalized method moments (GMM) estimator to estimate treatment effects defined through estimation equations using an observational data set from a complex survey. We demonstrate that the proposed estimator, which incorporates both sampling probabilities and semiparametrically estimated self-selection probabilities, gives consistent estimates of treatment effects. The asymptotic normality of the proposed estimator is established in the finite population framework, and its variance estimation is discussed. In simulations, we evaluate our proposed estimat

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Fadhil, Diyar, and Rodolfo Oliveira. "Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models." Information 14, no. 12 (2023): 648. http://dx.doi.org/10.3390/info14120648.

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Deep learning is used in various applications due to its advantages over traditional Machine Learning (ML) approaches in tasks encompassing complex pattern learning, automatic feature extraction, scalability, adaptability, and performance in general. This paper proposes an end-to-end (E2E) delay estimation method for 5G networks through deep learning (DL) techniques based on Gaussian Mixture Models (GMM). In the first step, the components of a GMM are estimated through the Expectation-Maximization (EM) algorithm and are subsequently used as labeled data in a supervised deep learning stage. A m

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15

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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16

Tan, Yong, and John Anchor. "Stability and profitability in the Chinese banking industry: evidence from an auto-regressive-distributed linear specification." Investment Management and Financial Innovations 13, no. 4 (2016): 120–29. http://dx.doi.org/10.21511/imfi.13(4).2016.10.

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The important role played by the Chinese commercial banks in the development of China’s economy has made the government and banking regulatory authority concerned about the performance of these banks.Indeedthe stability of the banking sector has attracted greater attention since the financial crisis of 2007-2009. The principal objective of this study is to investigate the inter-relationships between profitability and stability in the Chinese banking industry. Using a sample of Chinese commercial banks over the period 2003-2013, the study examines the inter-relationships under an auto-regressiv

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Khalid, Muhammad, Abid Muhammad Khan, Muhammad Rauf, Muhammad Taha Jilani, and Sheraz Afzal. "FPGA-Based Time-Domain Channel Estimation in Gaussian Mixture Model." Mathematical Problems in Engineering 2021 (May 3, 2021): 1–12. http://dx.doi.org/10.1155/2021/5596301.

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The performance of time-domain channel estimation deteriorates due to the presence of Gaussian mixture model (GMM) noise, which results in high mean squared error (MSE) as a challenging issue. The performance of the estimator further decreases when the complexity of the estimator is high due to the high convergence rate. In this paper, an optimized channel estimation method is proposed with low complexity and high accuracy in the GMM environment. In this channel estimation, an improved Gauss-Seidel iterative method is utilized with a minimum number of iterations. The convergence rate of the Ga

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18

Han, Chirok, and Peter C. B. Phillips. "GMM ESTIMATION FOR DYNAMIC PANELS WITH FIXED EFFECTS AND STRONG INSTRUMENTS AT UNITY." Econometric Theory 26, no. 1 (2009): 119–51. http://dx.doi.org/10.1017/s026646660909063x.

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This paper develops new estimation and inference procedures for dynamic panel data models with fixed effects and incidental trends. A simple consistent GMM estimation method is proposed that avoids the weak moment condition problem that is known to affect conventional GMM estimation when the autoregressive coefficient (ρ) is near unity. In both panel and time series cases, the estimator has standard Gaussian asymptotics for all values of ρ ∈ (−1, 1] irrespective of how the composite cross-section and time series sample sizes pass to infinity. Simulations reveal that the estimator has little bi

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19

Chao, John C. "PANEL STRUCTURAL MODELING WITH WEAK INSTRUMENTATION AND COVARIANCE RESTRICTIONS." Econometric Theory 30, no. 4 (2014): 839–81. http://dx.doi.org/10.1017/s0266466613000492.

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This paper considers estimating a panel data simultaneous equations model under both coefficient and covariance matrix restrictions in a scenario where one or the other set of identifying restrictions may be invalid or may hold only weakly. We study the limiting properties of various estimators in an asymptotic framework, which takes both the cross-sectional dimensionNand the time dimensionTto infinity. In this setting as in the pure cross-sectional setup, the performance of the 2SLS estimator depends on the strength of the identifying conditions imposed on the coefficients of the model, and i

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20

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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21

Hansen, Bruce. "Johansen’s Reduced Rank Estimator Is GMM." Econometrics 6, no. 2 (2018): 26. http://dx.doi.org/10.3390/econometrics6020026.

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22

Koenker, Roger, José A. F. Machado, Christopher L. Skeels, and Alan H. Welsh. "Momentary Lapses: Moment Expansions and the Robustness of Minimum Distance Estimation." Econometric Theory 10, no. 1 (1994): 172–97. http://dx.doi.org/10.1017/s0266466600008288.

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This paper explores the robustness of minimum distance (GMM) estimators focusing particularly on the effect of intermediate covariance matrix estimation on final estimator performance. Asymptotic expansions to order Op(n−3/2) are employed to construct O(n−2) expansions for the variance of estimators constructed from preliminary least-squares and general M-estimators. In the former case, there is a rather curious robustifying effect due to estimation of the Eicker-White covariance matrix for error distributions with sufficiently large kurtosis.

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Carrasco, Marine, and Jean-Pierre Florens. "ON THE ASYMPTOTIC EFFICIENCY OF GMM." Econometric Theory 30, no. 2 (2013): 372–406. http://dx.doi.org/10.1017/s0266466613000340.

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The efficiency of the generalized method of moment (GMM) estimator is addressed by using a characterization of its variance as an inner product in a reproducing kernel Hilbert space. We show that the GMM estimator is asymptotically as efficient as the maximum likelihood estimator if and only if the true score belongs to the closure of the linear space spanned by the moment conditions. This result generalizes former ones to autocorrelated moments and possibly infinite number of moment restrictions. Second, we derive the semiparametric efficiency bound when the observations are known to be Marko

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Jenish, Nazgul. "SPATIAL SEMIPARAMETRIC MODEL WITH ENDOGENOUS REGRESSORS." Econometric Theory 32, no. 3 (2014): 714–39. http://dx.doi.org/10.1017/s0266466614000905.

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This paper proposes a semiparametric generalized method of moments estimator (GMM) estimator for a partially parametric spatial model with endogenous spatially dependent regressors. The finite-dimensional estimator is shown to be consistent and root-n asymptotically normal under some reasonable conditions. A spatial heteroscedasticity and autocorrelation consistent covariance estimator is constructed for the GMM estimator. The leading application is nonlinear spatial autoregressions, which arise in a wide range of strategic interaction models. To derive the asymptotic properties of the estimat

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Ježić, Zoran, Petra Adelajda Zaninović, and Renee Škulić. "How does the ICT affect human development? Evidence from developing vs. developed countries." Zbornik radova Ekonomskog fakulteta u Rijeci: časopis za ekonomsku teoriju i praksu/Proceedings of Rijeka Faculty of Economics: Journal of Economics and Business 40, no. 1 (2022): 9–27. http://dx.doi.org/10.18045/zbefri.2022.1.9.

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This study aims to assess the impact of information and communication technology (ICT) on human development (measured with the human development index – HDI). The authors have analyzed the countries with different development levels in order to identify the possible differences in these impacts when observing the level of income (development of the country). The paper uses a static panel data regression analysis, while a fixed-effects estimator (FE) is used for estimation. To address the possible endogeneity problem caused by reverse causality, we also perform a dynamic panel data regression u

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Ataünal, Levent, and Aslı Aybars. "Testing Target-Adjustment and Pecking Order Models of Capital Structure and Estimating Speed of Adjustment." International Journal of Corporate Finance and Accounting 4, no. 1 (2017): 1–15. http://dx.doi.org/10.4018/ijcfa.2017010101.

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This article examines the explanation power of the pecking order and target adjustment models on 148 Borsa Istanbul (BIST) firms' capital structure over the period of 2005 to 2015. The article also estimates the speed of adjustment (SOA) to the targeted leverage level. Although a firm's capital structure is jointly determined by both theories, target adjustment model appear to have relatively higher power in explaining capital structures of BIST firms. Estimates of the adjustment speeds suggests that firms move toward their target debt ratios at a fast pace. Adjustment speeds estimated with ma

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Yildiz, Neşe. "CONSISTENCY OF PLUG-IN ESTIMATORS OF UPPER CONTOUR AND LEVEL SETS." Econometric Theory 28, no. 2 (2011): 309–27. http://dx.doi.org/10.1017/s0266466611000144.

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This paper studies the problem of estimating the set of finite-dimensional parameter values defined by a finite number of moment inequality or equality conditions and gives conditions under which the estimator defined by the set of parameter values that satisfy the estimated versions of these conditions is consistent in Hausdorff metric. This paper also suggests extremum estimators that with probability approaching 1 agree with the set consisting of parameter values that satisfy the sample versions of the moment conditions. In particular, it is shown that the set of minimizers of the sample ge

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Carrasco, Marine, and Jean-Pierre Florens. "GENERALIZATION OF GMM TO A CONTINUUM OF MOMENT CONDITIONS." Econometric Theory 16, no. 6 (2000): 797–834. http://dx.doi.org/10.1017/s0266466600166010.

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This paper proposes a version of the generalized method of moments procedure that handles both the case where the number of moment conditions is finite and the case where there is a continuum of moment conditions. Typically, the moment conditions are indexed by an index parameter that takes its values in an interval. The objective function to minimize is then the norm of the moment conditions in a Hilbert space. The estimator is shown to be consistent and asymptotically normal. The optimal estimator is obtained by minimizing the norm of the moment conditions in the reproducing kernel Hilbert s

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Newey, Whitney K. "Series Estimation of Regression Functionals." Econometric Theory 10, no. 1 (1994): 1–28. http://dx.doi.org/10.1017/s0266466600008203.

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Two-step estimators, where the first step is the predicted value from a nonparametric regression, are useful in many contexts. Examples include a non-parametric residual variance, probit with nonparametric generated regressors, efficient GMM estimation with randomly missing data, heteroskedasticity corrected least squares, semiparametric regression, and efficient nonlinear instrumental variables estimators. The purpose of this paper is the development of consistency and asymptotic normality results when the first step is a series estimator. The paper presents the form of a correction term for

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Gu, Chunzhi, Haoran Xie, Xuequan Lu, and Chao Zhang. "CGMVAE: Coupling GMM Prior and GMM Estimator for Unsupervised Clustering and Disentanglement." IEEE Access 9 (2021): 65140–49. http://dx.doi.org/10.1109/access.2021.3076073.

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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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Leitão, Nuno Carlos. "GMM Estimator: An Application to Intraindustry Trade." Journal of Applied Mathematics 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/857824.

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This paper investigates the determinants of intraindustry trade (IIT), horizontal IIT (HIIT), and Vertical IIT (VIIT) in the automobile industry in Portugal. The trade in this sector between Portugal and the European Union (EU-27) was examined, between 1995 and 2008, using a dynamic panel data. We apply the GMM system to solve the problems of serial correlation and the endogeneity of some explanatory variables. The findings are consistent with the literature. The difference between per capita incomes and factor endowments present a positive sign. These results are according to Heckscher-Ohlin

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Hayakawa, Kazuhiko. "THE ASYMPTOTIC PROPERTIES OF THE SYSTEM GMM ESTIMATOR IN DYNAMIC PANEL DATA MODELS WHEN BOTH N AND T ARE LARGE." Econometric Theory 31, no. 3 (2014): 647–67. http://dx.doi.org/10.1017/s0266466614000449.

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In this paper, we derive the asymptotic properties of the system generalized method of moments (GMM) estimator in dynamic panel data models with individual and time effects when both N and T, the dimensions of cross-section and time series, are large. Specifically, we show that the two-step system GMM estimator is consistent when a suboptimal weighting matrix where off-diagonal blocks are set to zero is used. Such consistency results theoretically support the use of the system GMM estimator in large N and T contexts even though it was originally developed for large N and small T panels. Simula

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COSIMO, MAGAZZINO. "Fiscal Policy, Consumption and Current Account in the European Countries." Economics Bulletin 32, no. 2 (2021): 1330–44. https://doi.org/10.5281/zenodo.4686797.

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This paper explores the relationship between fiscal deficit, trade deficit and private consumption in European countries in the years 1970-2010. The aim of the study is to test empirically the validity and rationale of the Keynesian proposition (conventional view or Twin Deficits hypothesis) and the Ricardian Equivalence hypothesis, as well as to analyze the relationship between fiscal policy and private consumption. The empirical findings of our study show mixed results. In fact, static panel data estimates suggest that a one per cent increase in fiscal deficit/GDP ratio tends to deteriorate

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Kruiniger, Hugo. "GMM ESTIMATION AND INFERENCE IN DYNAMIC PANEL DATA MODELS WITH PERSISTENT DATA." Econometric Theory 25, no. 5 (2009): 1348–91. http://dx.doi.org/10.1017/s0266466608090531.

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In this paper we consider generalized method of moments–based (GMM-based) estimation and inference for the panel AR(1) model when the data are persistent and the time dimension of the panel is fixed. We find that the nature of the weak instruments problem of the Arellano–Bond (Arellano and Bond, 1991,Review of Economic Studies58, 277–297) estimator depends on the distributional properties of the initial observations. Subsequently, we derive local asymptotic approximations to the finite-sample distributions of the Arellano–Bond estimator and the System estimator, respectively, under a variety o

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Kuersteiner, Guido M. "EFFICIENT IV ESTIMATION FOR AUTOREGRESSIVE MODELS WITH CONDITIONAL HETEROSKEDASTICITY." Econometric Theory 18, no. 3 (2002): 547–83. http://dx.doi.org/10.1017/s0266466602183010.

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This paper analyzes autoregressive time series models where the errors are assumed to be martingale difference sequences that satisfy an additional symmetry condition on their fourth-order moments. Under these conditions quasi maximum likelihood estimators of the autoregressive parameters are no longer efficient in the generalized method of moments (GMM) sense. The main result of the paper is the construction of efficient semiparametric instrumental variables estimators for the autoregressive parameters. The optimal instruments are linear functions of the innovation sequence.It is shown that a

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Parente, Paulo M. D. C., and Richard J. Smith. "GEL METHODS FOR NONSMOOTH MOMENT INDICATORS." Econometric Theory 27, no. 1 (2010): 74–113. http://dx.doi.org/10.1017/s0266466610000137.

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This paper considers the first-order large sample properties of the generalized empirical likelihood (GEL) class of estimators for models specified by nonsmooth indicators. The GEL class includes a number of estimators recently introduced as alternatives to the efficient generalized method of moments (GMM) estimator that may suffer from substantial biases in finite samples. These include empirical likelihood (EL), exponential tilting (ET), and the continuous updating estimator (CUE). This paper also establishes the validity of tests suggested in the smooth moment indicators case for overidenti

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Ahmad, Nur Aminah, Georgina M. Tinungki, and Nurtiti Sunusi. "Estimation of Dynamic Panel Data Regression Parameters Using Generalized Methods of Moment." Jurnal Matematika, Statistika dan Komputasi 18, no. 3 (2022): 484–91. http://dx.doi.org/10.20956/j.v18i3.20574.

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Panel data is a combination of cross section and time series. There are two panel data models, namely static and dynamic panel data. Because seeing the advantages of the dynamic panel data model which is able to overcome endogeneity problems related to the use of the dependent variable lag where in the static panel data model the use of the dependent variable lag causes the estimation results to be biased and inconsistent, so the author examines the dynamic panel data regression model. In the dynamic data model there is a lag of the dependent variable, this variable is correlated with error. T

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Abiola, Oladimeji Lukman, Mohammed Kabir Garba, Adediran Dauda Adeshola, and Oke Samuel Abayomi. "Modelling Dynamic Panel Data Using Hierarchical Bayesian Approach." International Journal of Science for Global Sustainability 11, no. 1 (2025): 1–9. https://doi.org/10.57233/ijsgs.v11i1.774.

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This study developed a hierarchical Bayesian framework for dynamic panel data models to address the challenges of unequal variances regularly encountered in empirical research. Dynamic panel data models with temporal dynamics were estimated using OLS and GMM. It was discovered that estimated parameters were biased, inconsistent and unreliable. The developed estimator proved to be robust in addressing the shortfall and outperformed other estimators under various panel data configurations. Estimating a robust hierarchical Bayesian model for dynamic panel and assessing its performance under vario

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Liao, Zhipeng. "ADAPTIVE GMM SHRINKAGE ESTIMATION WITH CONSISTENT MOMENT SELECTION." Econometric Theory 29, no. 5 (2013): 857–904. http://dx.doi.org/10.1017/s0266466612000783.

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This paper proposes a generalized method of moments (GMM) shrinkage method to efficiently estimate the unknown parameters θo identified by some moment restrictions, when there is another set of possibly misspecified moment conditions. We show that our method enjoys oracle-like properties; i.e., it consistently selects the correct moment conditions in the second set and at the same time, its estimator is as efficient as the GMM estimator based on all correct moment conditions. For empirical implementation, we provide a simple data-driven procedure for selecting the tuning parameters of the pena

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Lieberman, Offer, and Peter C. B. Phillips. "IV AND GMM INFERENCE IN ENDOGENOUS STOCHASTIC UNIT ROOT MODELS." Econometric Theory 34, no. 5 (2017): 1065–100. http://dx.doi.org/10.1017/s0266466617000330.

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Lieberman and Phillips (2017; LP) introduced a multivariate stochastic unit root (STUR) model, which allows for random, time varying local departures from a unit root (UR) model, where nonlinear least squares (NLLS) may be used for estimation and inference on the STUR coefficient. In a structural version of this model where the driver variables of the STUR coefficient are endogenous, the NLLS estimate of the STUR parameter is inconsistent, as are the corresponding estimates of the associated covariance parameters. This paper develops a nonlinear instrumental variable (NLIV) as well as GMM esti

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Liu, Xiaodong, Lung-fei Lee, and Christopher R. Bollinger. "An efficient GMM estimator of spatial autoregressive models." Journal of Econometrics 159, no. 2 (2010): 303–19. http://dx.doi.org/10.1016/j.jeconom.2010.08.001.

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Han, Chirok, Peter C. B. Phillips, and Donggyu Sul. "X-DIFFERENCING AND DYNAMIC PANEL MODEL ESTIMATION." Econometric Theory 30, no. 1 (2013): 201–51. http://dx.doi.org/10.1017/s0266466613000170.

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This paper introduces a new estimation method for dynamic panel models with fixed effects and AR(p) idiosyncratic errors. The proposed estimator uses a novel form of systematic differencing, called X-differencing, that eliminates fixed effects and retains information and signal strength in cases where there is a root at or near unity. The resulting “panel fully aggregated” estimator (PFAE) is obtained by pooled least squares on the system of X-differenced equations. The method is simple to implement, consistent for all parameter values, including unit root cases, and has strong asymptotic and

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Pietak, Lukasz. "Structural Funds and Convergence in Poland." Revista Hacienda Pública Española 236, no. 1 (2021): 3–37. http://dx.doi.org/10.7866/hpe-rpe.21.1.1.

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This study aimed to investigate whether cohesion policy funds affected economic growth and resulted in a convergence process across NUTS 2 regions in Poland. The study examined a balanced panel of data from 16 Polish regions between 2004 and 2016 and applied different estimations procedures, in-cluding the fixed effects estimator –as the main estimator– and the GMM estimator. The empirical analysis revealed that cohesion policy funds had a positive impact on regional economic growth. How-ever, the influence of structural funds on convergence was shown to be weak in Poland. The results were rob

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Nguyen, Van Bon. "The Effect of Government Debt on Private Investment in Advanced Economies: Does Institutional Quality Matter?" Scientific Annals of Economics and Business 69, no. 1 (2022): 133–44. http://dx.doi.org/10.47743/saeb-2022-0006.

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Unlike developing economies, advanced economies easily borrow debt to finance budget deficits. Government debt is one of the active measures of fiscal policy in these economies to run the economy and overcome its cyclicality. Most related studies note that government debt reduces private investment. Does it hold for advanced economies? Does institutional quality significantly affect the government debt – private investment relationship in these economies? For the answer, the study applies the PMG estimator (PMG) and the two-step difference GMM Arellano & Bond estimator (D-GMM) to investiga

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Armstrong, Timothy B., and Michal Kolesár. "Sensitivity analysis using approximate moment condition models." Quantitative Economics 12, no. 1 (2021): 77–108. http://dx.doi.org/10.3982/qe1609.

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We consider inference in models defined by approximate moment conditions. We show that near‐optimal confidence intervals (CIs) can be formed by taking a generalized method of moments (GMM) estimator, and adding and subtracting the standard error times a critical value that takes into account the potential bias from misspecification of the moment conditions. In order to optimize performance under potential misspecification, the weighting matrix for this GMM estimator takes into account this potential bias and, therefore, differs from the one that is optimal under correct specification. To forma

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Armstrong, Timothy B., and Michal Kolesár. "Sensitivity analysis using approximate moment condition models." Quantitative Economics 12, no. 1 (2021): 77–108. http://dx.doi.org/10.3982/qe1609.

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We consider inference in models defined by approximate moment conditions. We show that near‐optimal confidence intervals (CIs) can be formed by taking a generalized method of moments (GMM) estimator, and adding and subtracting the standard error times a critical value that takes into account the potential bias from misspecification of the moment conditions. In order to optimize performance under potential misspecification, the weighting matrix for this GMM estimator takes into account this potential bias and, therefore, differs from the one that is optimal under correct specification. To forma

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Sebki, Wafa. "Education and Economic Growth in Developing Countries: Empirical Evidence from GMM Estimators for Dynamic Panel Data." Economics and Business 35, no. 1 (2021): 14–29. http://dx.doi.org/10.2478/eb-2021-0002.

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Abstract The paper aims at studying the effect of education measured by enrolment ratios in secondary and higher education on economic growth measured by the rate of GDP growth in a sample of 40 developing countries during the period from 2002 to 2016 using the dynamic panel data estimators. The results of estimating the model of this study using the difference GMM estimator or what is known as the Arellano and Bond estimator showed that the proportions of those enrolled in tertiary education had a significant positive effect on economic growth, while the proportions of those enrolled in secon

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Antoine, Bertille, Kevin Proulx, and Eric Renault. "Pseudo-True SDFs in Conditional Asset Pricing Models*." Journal of Financial Econometrics 18, no. 4 (2018): 656–714. http://dx.doi.org/10.1093/jjfinec/nby017.

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Abstract This article is motivated by the need to bridge some gap between modern asset pricing theory and recent developments in econometric methodology. While asset pricing theory enhances the use of conditional pricing models, econometric inference of conditional models can be challenging due to misspecification or weak identification. To tackle the case of misspecification, we utilize the conditional Hansen and Jagannathan (1997) (HJ) distance as studied by Gagliardini and Ronchetti (2016), but we set the focus on interpretation and estimation of the pseudo-true value defined as the argumen

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Chávez, Carlos Cesar. "The Impact of Macroeconomics Factors on Real Exchange Rate in Latin America:." Latin American Journal of Trade Policy 3, no. 8 (2020): 6. http://dx.doi.org/10.5354/0719-9368.2020.57342.

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This paper studies the determinants of the real exchange rate using macroeconomic variables, and whether they can predict it. A panel data is used, which estimator is system GMM that allows controlling the endogeneity of the variables. In turn, we transformed the variables with forward orthogonal deviations (FOD) and first difference (FD), which allows us to eliminate unobserved effects that are invariant in time. To check the robustness of the estimates, different periods were used, from 1980-2019, 2000-2019 and 2010-2019. For the period 1980-2019, it is found that the past values of the real

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