Standard Generalized Method Of Moments Research Articles

Does financial inclusion improve energy accessibility in Sub-Saharan Africa?

ABSTRACT We examine the nexus between financial inclusion and energy poverty. Analysing data for 27 energy-poor countries in the Sub-Saharan Africa region over 2004–2021, we employ sequential (two-stage), panel-corrected standard error (PCSE) and two-step dynamic system GMM (generalized method of moments) regression models, and control for endogeneity, CSD, slope heterogeneity as well as stationarity and cointegration patterns of the variables. Our empirical results show that financial inclusion significantly reduces energy poverty in the selected energy-poor countries. The study also finds a positive significant association between energy access and GDP per capita, while oil price and energy intensity are inversely associated with energy access. The results are robust to different control variables, estimation methods and subsamples. These findings have strong policy implications for energy-poor countries and point to the need for appropriate policies to promote financial inclusion for reducing energy poverty.

Alternative general method of moments estimators in dynamic panel data models

The choice of initial weighting matrix and the problem of instruments proliferation are crucial issues in generalized method of moments (GMM) estimators of dynamic panel models. In this study, we propose alternative system GMM estimators that utilize suboptimal initial weighting matrices together with reduced instruments set (specifically lag-limited and partially collapsed instruments). Comparison of the performance of the proposed estimators against the conventional estimator was undertaken in terms of bias, root mean squared error (RMSE) and coverage probabilities through Monte Carlo simulations. Our simulation results revealed that sub-optimally weighted system GMM estimator adopting partially collapsed instruments outperforms the standard GMM estimator in terms of both bias and RMSE for large T and large variance ratios as the coefficient of the lagged dependent variable gets close to zero. Under these scenarios, the system GMM estimators based on reduced instruments were also found to perform well in terms of coverage probabilities. As the process approaches a random walk, however, there is no considerable gain from the use of suboptimal initial weights matrix and reduced instruments in general.

Modeling Correlated Binary Outcomes with Time-Dependent Covariates

We group approaches to modeling correlated binary data accord ing to data recorded cross-sectionally as opposed to data recorded longi tudinally; according to models that are population-averaged as opposed to subject-specific; and according to data with time-dependent covariates as opposed to time-independent covariates. Standard logistic regression mod els are appropriate for cross-sectional data. However, for longitudinal data, methods such as generalized estimating equations (GEE) and generalized method of moments (GMM) are commonly used to fit population-averaged models, while random-effects models such as generalized linear mixed mod els (GLMM) are used to fit subject-specific models. Some of these methods account for time-dependence in covariates while others do not. This paper addressed these approaches with an illustration using a Medicare dataset as it relates to rehospitalization. In particular, we compared results from standard logistic models, GEE models, GMM models, and random-effects models by analyzing a binary outcome for four successive hospitalizations. We found that these procedures address differently the correlation among responses and the feedback from response to covariate. We found marginal GMM logistic regression models to be more appropriate when covariates are classified as time-dependent in comparison to GEE models. We also found conditional random-intercept models with time-dependent covariates decom posed into components to be more appropriate when time-dependent covari ates are present in comparison to ordinary random-effects models. We used the SAS procedures GLIMMIX, NLMIXED, IML, GENMOD, and LOGIS TIC to analyze the illustrative dataset, as well as unique programs written using the R language.

Об оценке кривой Филлипса для российской экономики

This article focuses on the estimation of the hybrid New-Keynesian Phillips Curve with different kinds of price indices. This helps not only to find to what extent inflation in Russia reacts to the change in the phase of real business cycle, but also to estimate the ratio between agents with rational and adaptive expectations. Such knowledge might be crucial for monetary authorities, because the effectiveness of their policies depends on the understanding of inflation dynamics. Incorrect estimates of the share of rational economic agents can lead to errors in the conducting of monetary policy, which, in turn, may reduce the credibility of central bank amongst population and negatively affect the effectiveness of the future policy conduct.The study uses three different measures of inflation based on the following indexes: the GDP deflator, the CPI, and the GDP deflator, net of export. The main method of estimation is the continuously updating general method of moments (CUE), which has a smaller bias on finite samples and more valid values of the Hansen J-test for the over identification compared to the standard generalized method of moments. Altogether, this makes the inference more valid.The main conclusion is that it is the dynamics of inflation calculated on the basis of the GDP deflator, net of exports, that is best described by the Phillips curve equation, and that the output gap is significant and has a positive sign which is consistent with the theory. Mayhap this is due to the fact that the prices of imported and exported goods are not explicitly included to this measure of inflation, that is, it can be referred to as some internal inflation. Also, an important result is a slightly greater weight of forward looking expectations in the formation of the inflationary process.

Adaptive Elastic Net GMM Estimation With Many Invalid Moment Conditions: Simultaneous Model and Moment Selection

This article develops the adaptive elastic net generalized method of moments (GMM) estimator in large-dimensional models with potentially (locally) invalid moment conditions, where both the number of structural parameters and the number of moment conditions may increase with the sample size. The basic idea is to conduct the standard GMM estimation combined with two penalty terms: the adaptively weighted lasso shrinkage and the quadratic regularization. It is a one-step procedure of valid moment condition selection, nonzero structural parameter selection (i.e., model selection), and consistent estimation of the nonzero parameters. The procedure achieves the standard GMM efficiency bound as if we know the valid moment conditions ex ante, for which the quadratic regularization is important. We also study the tuning parameter choice, with which we show that selection consistency still holds without assuming Gaussianity. We apply the new estimation procedure to dynamic panel data models, where both the time and cross-section dimensions are large. The new estimator is robust to possible serial correlations in the regression error terms.

Fat tails and spurious estimation of consumption‐based asset pricing models

SummaryThe standard generalized method of moments (GMM) estimation of Euler equations in heterogeneous‐agent consumption‐based asset pricing models is inconsistent under fat tails because the GMM criterion is asymptotically random. To illustrate this, we generate asset returns and consumption data from an incomplete‐market dynamic general equilibrium model that is analytically solvable and exhibits power laws in consumption. Monte Carlo experiments suggest that the standard GMM estimation is inconsistent and susceptible to Type II errors (incorrect nonrejection of false models). Estimating an overidentified model by dividing agents into age cohorts appears to mitigate Type I and II errors.

Fat Tails and Euler Equations: A Monte Carlo Study

The standard generalized method of moments (GMM) estimation of Euler equations in heterogeneous-agent consumption-based asset pricing models is inconsistent under fat tails because the GMM criterion is asymptotically random. To illustrate this, we generate asset returns and consumption data from an incomplete-market dynamic general equilibrium model that is analytically solvable and exhibits power laws in consumption. Monte Carlo experiments suggest that the standard GMM estimation is inconsistent and susceptible to Type II errors (incorrect non-rejection of false models). Estimating an overidentified model by dividing agents into age cohorts appears to mitigate Type I and II errors.

GMM estimation of a realized stochastic volatility model: A Monte Carlo study

ABSTRACTThis article investigates alternative generalized method of moments (GMM) estimation procedures of a stochastic volatility model with realized volatility measures. The extended model can accommodate a more general correlation structure. General closed form moment conditions are derived to examine the model properties and to evaluate the performance of various GMM estimation procedures under Monte Carlo environment, including standard GMM, principal component GMM, robust GMM and regularized GMM. An application to five company stocks and one stock index is also provided for an empirical demonstration.

Select the valid and relevant moments: An information-based LASSO for GMM with many moments

This paper studies the selection of valid and relevant moments for the generalized method of moments (GMM) estimation. For applications with many candidate moments, our asymptotic analysis accommodates a diverging number of moments as the sample size increases. The proposed procedure achieves three objectives in one-step: (i) the valid and relevant moments are distinguished from the invalid or irrelevant ones; (ii) all desired moments are selected in one step instead of in a stepwise manner; (iii) the parameters of interest are automatically estimated with all selected moments as opposed to a post-selection estimation. The new method performs moment selection and efficient estimation simultaneously via an information-based adaptive GMM shrinkage estimation, where an appropriate penalty is attached to the standard GMM criterion to link moment selection to shrinkage estimation. The penalty is designed to signal both moment validity and relevance for consistent moment selection. We develop asymptotic results for the high-dimensional GMM shrinkage estimator, allowing for non-smooth sample moments and weakly dependent observations. For practical implementation, this one-step procedure is computationally attractive.

GMM ESTIMATION AND UNIFORM SUBVECTOR INFERENCE WITH POSSIBLE IDENTIFICATION FAILURE

This paper determines the properties of standard generalized method of moments (GMM) estimators, tests, and confidence sets (CSs) in moment condition models in which some parameters are unidentified or weakly identified in part of the parameter space. The asymptotic distributions of GMM estimators are established under a full range of drifting sequences of true parameters and distributions. The asymptotic sizes (in a uniform sense) of standard GMM tests and CSs are established.The paper also establishes the correct asymptotic sizes of “robust” GMM-based Wald,t, and quasi-likelihood ratio tests and CSs whose critical values are designed to yield robustness to identification problems.The results of the paper are applied to a nonlinear regression model with endogeneity and a probit model with endogeneity and possibly weak instrumental variables.

Select the Valid and Relevant Moments: An Information-Based LASSO for GMM with Many Moments

This paper studies the selection of valid and relevant moments for the generalized method of moments (GMM) estimation. For applications with many candidate moments, our asymptotic analysis accommodates a diverging number of moments as the sample size increases. The proposed procedure achieves three objectives in one-step: (i) the valid and relevant moments are distinguished from the invalid or irrelevant ones; (ii) all desired moments are selected in one step instead of in a stepwise manner; (iii) the parameters of interest are automatically estimated with all selected moments as opposed to a post-selection estimation. The new method performs moment selection and efficient estimation simultaneously via an information-based adaptive GMM shrinkage estimation, where an appropriate penalty is attached to the standard GMM criterion to link moment selection to shrinkage estimation. The penalty is designed to signal both moment validity and relevance for consistent moment selection. We develop asymptotic results for the high-dimensional GMM shrinkage estimator, allowing for non-smooth sample moments and weakly dependent observations. For practical implementation, this one-step procedure is computationally attractive.

Using Samples of Unequal Length in Generalized Method of Moments Estimation

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 standard errors as well as asymptotic ones.

GMM Estimation and Uniform Subvector Inference with Possible Identification Failure

This paper determines the properties of standard generalized method of moments (GMM) estimators, tests, and confidence sets (CS's) in moment condition models in which some parameters are unidentified or weakly identified in part of the parameter space. The asymptotic distributions of GMM estimators are established under a full range of drifting sequences of true parameters and distributions. The asymptotic sizes (in a uniform sense) of standard GMM tests and CS's are established.The paper also establishes the correct asymptotic sizes of robust GMM-based Wald, t; and quasi-likelihood ratio tests and CS's whose critical values are designed to yield robustness to identification problems.The results of the paper are applied to a nonlinear regression model with endogeneity and a probit model with endogeneity and possibly weak instrumental variables.

GMM Estimation and Uniform Subvector Inference with Possible Identification Failure

This paper determines the properties of standard generalized method of moments (GMM) estimators, tests, and confidence sets (CS's) in moment condition models in which some parameters are unidentified or weakly identified in part of the parameter space. The asymptotic distributions of GMM estimators are established under a full range of drifting sequences of true parameters and distributions. The asymptotic sizes (in a uniform sense) of standard GMM tests and CS's are established. The paper also establishes the correct asymptotic sizes of robust GMM-based Wald, t, and quasi-likelihood ratio tests and CS's whose critical values are designed to yield robustness to identification problems. The results of the paper are applied to a nonlinear regression model with endogeneity and a probit model with endogeneity and possibly weak instrumental variables.

Select the Valid and Relevant Moments: A One-Step Procedure for GMM with Many Moments

This paper considers the selection of valid and relevant moments for the generalized method of moments (GMM) estimation. For applications with many candidate moments, our asymptotic analysis ccommodates a diverging number of moments as the sample size increases. The proposed procedure achieves three objectives in one-step: (i) the valid and relevant moments are selected simultaneously rather than sequentially; (ii) all desired moments are selected together instead of in a stepwise manner; (iii) the parameter of interest is automatically estimated with all selected moments as opposed to a post-selection estimation. The new moment selection method is achieved via an information-based adaptive GMM shrinkage estimation, where an appropriate penalty is attached to the standard GMM criterion to link moment selection to shrinkage estimation. The penalty is designed to signal both moment validity and relevance for consistent moment selection and efficient estimation. The asymptotic analysis allows for non-smooth sample moments and weakly dependent observations, making it generally applicable. For practical implementation, this one-step procedure is computationally attractive.

Kernel-weighted GMM estimators for linear time series models

This paper analyzes the higher-order asymptotic properties of generalized method of moments (GMM) estimators for linear time series models using many lags as instruments. A data-dependent moment selection method based on minimizing the approximate mean squared error is developed. In addition, a new version of the GMM estimator based on kernel-weighted moment conditions is proposed. It is shown that kernel-weighted GMM estimators can reduce the asymptotic bias compared to standard GMM estimators. Kernel weighting also helps to simplify the problem of selecting the optimal number of instruments. A feasible procedure similar to optimal bandwidth selection is proposed for the kernel-weighted GMM estimator.

Select the Valid and Relevant Moments: A One-Step Procedure for GMM with Many Moments

This paper considers the selection of valid and relevant moments for the generalized method of moments (GMM) estimation. For applications with many candidate moments, our asymptotic analysis ccommodates a diverging number of moments as the sample size increases. The proposed procedure achieves three objectives in one-step: (i) the valid and relevant moments are selected simultaneously rather than sequentially; (ii) all desired moments are selected together instead of in a stepwise manner; (iii) the parameter of interest is automatically estimated with all selected moments as opposed to a post-selection estimation. The new moment selection method is achieved via an information-based adaptive GMM shrinkage estimation, where an appropriate penalty is attached to the standard GMM criterion to link moment selection to shrinkage estimation. The penalty is designed to signal both moment validity and relevance for consistent moment selection and efficient estimation. The asymptotic analysis allows for non -smooth sample moments and weakly dependent observations, making it generally applicable. For practical implementation, this one-step procedure is computationally attractive.

Efficient Derivative Pricing by the Extended Method of Moments

In this paper, we introduce the extended method of moments (XMM) estimator. This estimator accommodates a more general set of moment restrictions than the standard generalized method of moments (GMM) estimator. More specifically, the XMM differs from the GMM in that it can handle not only uniform conditional moment restrictions (i.e., valid for any value of the conditioning variable), but also local conditional moment restrictions valid for a given fixed value of the conditioning variable. The local conditional moment restrictions are of special relevance in derivative pricing to reconstruct the pricing operator on a given day by using the information in a few cross sections of observed traded derivative prices and a time series of underlying asset returns. The estimated derivative prices are consistent for a large time series dimension, but a fixed number of cross sectionally observed derivative prices. The asymptotic properties of the XMM estimator are nonstandard, since the combination of uniform and local conditional moment restrictions induces different rates of convergence (parametric and nonparametric) for the parameters.

Feasible estimation of firm‐specific allocative inefficiency through Bayesian numerical methods

AbstractBoth the theoretical and empirical literature on the estimation of allocative and technical inefficiency has grown enormously. To minimize aggregation bias, ideally one should estimate firm and input‐specific parameters describing allocative inefficiency. However, identifying these parameters has often proven difficult. For a panel of Chilean hydroelectric power plants, we obtain a full set of such parameters using Gibbs sampling, which draws sequentially from conditional generalized method of moments (GMM) estimates obtained via instrumental variables estimation. We find an economically significant range of firm‐specific efficiency estimates with differing degrees of precision. The standard GMM approach estimates virtually no allocative inefficiency for industry‐wide parameters. Copyright © 2009 John Wiley & Sons, Ltd.

LASSO-TYPE GMM ESTIMATOR

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 method estimates the redundant parameters as zero in the large samples and provides the standard GMM limit distribution for the estimates of the nonzero parameters in the model. The asymptotic theory for our estimator is nonstandard. We conduct a simulation study that shows that the Lasso-type GMM correctly selects the true model much more often than the Bayesian information Criterion (BIC) and another model selection procedure based on the GMM objective function.