Generalized Method Of Moments Approach Research Articles

Bank Stability and Competition: Evidence from Albanian Banking Market

This paper analyses the inter-temporal competition – stability nexus after the global financial crises. For this reason, the empirical estimation approach follows a five – step procedure. First, we utilise quarterly macroeconomic and balance sheet and income statement data for 16 banks operating in the Albanian banking sector over the period 2008 – 2015. Second, we calculate a new composite index as a measure of bank stability conditions, which includes a wide set of information rather than focusing only on one aspect of risk. Then, we construct a proxy for bank competition such as the Boone indicator. Empirical estimations are based on the General Method of Moments approach. A set of robustness checks include also the use of other alternative proxy of competition such as the Lerner index and the efficient-adjusted Lerner index, profit elasticity and the Herfindahl index. Empirical results strongly support the “competition – stability” view after the global financial crises - that higher degree of competition boosts further bank stability conditions. Results further indicate that greater concentration has also a negative impact on bank stability. Results imply also that bank stability is positively linked with macroeconomic conditions and capital ratio and inverse with operational efficiency. Finally, we do not find a non-linear relationship between competition and stability.

Comparative GMM and GQL logistic regression models on hierarchical data

ABSTRACTWe often rely on the likelihood to obtain estimates of regression parameters but it is not readily available for generalized linear mixed models (GLMMs). Inferences for the regression coefficients and the covariance parameters are key in these models. We presented alternative approaches for analyzing binary data from a hierarchical structure that do not rely on any distributional assumptions: a generalized quasi-likelihood (GQL) approach and a generalized method of moments (GMM) approach. These are alternative approaches to the typical maximum-likelihood approximation approach in Statistical Analysis System (SAS) such as Laplace approximation (LAP). We examined and compared the performance of GQL and GMM approaches with multiple random effects to the LAP approach as used in PROC GLIMMIX, SAS. The GQL approach tends to produce unbiased estimates, whereas the LAP approach can lead to highly biased estimates for certain scenarios. The GQL approach produces more accurate estimates on both the regression coefficients and the covariance parameters with smaller standard errors as compared to the GMM approach. We found that both GQL and GMM approaches are less likely to result in non-convergence as opposed to the LAP approach. A simulation study was conducted and a numerical example was presented for illustrative purposes.

Rolling Regression Analysis of the Pástor-Stambaugh Model: Evidence from Robust Instrumental Variables

The capital asset pricing model (CAPM), Fama-French (FF), and Pastor-Stambaugh (PS) factor models are examined using a new dynamic rolling regression version of the generalized method of moments (GMM) method. This rolling regression framework not only allows us to investigate phases of the business cycle, but also permits regression estimates to vary through time due to changes in the development and efficiency of the sectors. The principal reasons for using the dynamic GMM with robust instruments is that some of these factors are measured with errors and the disturbances may be non-spherical. The CAPM appears as the most parsimonious model to explain the FF sector returns. Furthermore, the rolling GMM approach is clearly more sensitive to dynamic financial episodes than the ordinary least squares approach. In particular, liquidity has some anticipatory power, as it is able to forecast the 2007–2009 crises with heightened volatility starting in late 2005.

Generalized method of moments for estimating parameters of stochastic reaction networks.

BackgroundDiscrete-state stochastic models have become a well-established approach to describe biochemical reaction networks that are influenced by the inherent randomness of cellular events. In the last years several methods for accurately approximating the statistical moments of such models have become very popular since they allow an efficient analysis of complex networks.ResultsWe propose a generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data. The proposed parameter estimation method exploits recently developed moment-based approximations and provides estimators with desirable statistical properties when a large number of samples is available. We demonstrate the usefulness and efficiency of the inference method on two case studies.ConclusionsThe generalized method of moments provides accurate and fast estimations of unknown parameters of reaction networks. The accuracy increases when also moments of order higher than two are considered. In addition, the variance of the estimator decreases, when more samples are given or when higher order moments are included.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0342-8) contains supplementary material, which is available to authorized users.

GMM Estimation of 2D-RCA Models With Applications to Texture Image Classification.

The 2D indexed random coefficients autoregressive (2D-RCA) models are obtained by introducing appropriate random field coefficients to an AR model on Z(2). The study of such models is motivated by their capability to capture the space-varying behavior of the volatility. A generalized method of moment approach is considered to estimate the 2D-RCA models. Consistency and asymptotic normality of the estimates are derived. Estimated parameters are used, at a later stage, as pixel features in texture image classification.

How does Bank Capital Affect Bank Profitability and Risk? Evidence from China's WTO Accession

AbstractThis paper examines how bank capital affects bank profitability and risk in China, and how its impact differed before and after the nation entered the WTO. Our study uses the dynamic generalized method of moments approach with a panel database containing 171 Chinese commercial banks. We find that bank capital has significant influence on bank profitability and risk, but its impact has declined since China joined the WTO in 2001. For different sized groups, the impact of capital on profitability exhibits a distinct trend. The effects of capital on bank risk are different for large and small banks depending on the risk variables used for the Chinese banking industry.

Moment based estimation of supOU processes and a related stochastic volatility model

Abstract After a quick review of superpositions of OU (supOU) processes, integrated supOU processes and the supOU stochastic volatility model we estimate these processes by using the generalized method of moments (GMM). We show that the GMM approach yields consistent estimators and that it works very well in practice. Moreover, we discuss the influence of long memory effects.

A dynamic approach to road freight flows modeling in Spain

This paper presents an innovative approach to analyzing road vehicle freight traffic that uses a dynamic panel data specification derived from a gravity model. This dynamic approach, which has recently been employed in international goods trade models in lieu of the traditional static specification, is applied to the case of Spain using data for the countrys 15 NUTS-3 regions between 1999 and 2009. Using the system general method of moments approach, we obtained significant evidence that the flow of vehicles carrying commodities by road has a strong persistence effect when controlling for unobserved heterogeneity. We also found that the quality of road transport infrastructure has a significant impact on vehicle trips. According to our findings, we suggest that this type of specification be employed in distribution models in which fixed effects and lags of the dependent variable are included to account for unobserved heterogeneity and persistence effects, respectively.

Causal Factors of Failure of Finance Companies in Sri Lanka during 2008–2009

This paper investigates the causal factors of failure of Licensed Finance Companies in Sri Lanka during the period of 2008–2009. The study considered long-term aspects of failure rather than liquidity constrains caused by short-term deposit runs. However, the failure of finance company was identified based on the liquidity constrains faced during the distress period during 2008–2009. Monthly panel data of six stressed LFCs for the period from 2004 to 2013 was empirically estimated using generalized method of moments approach. Results revealed that the movement of Treasury bill interest rates, increasing trend of non-performing accommodations, decreasing trend of liquid assets in terms of deposit liabilities, high concentration of real estate stocks in relation to the total assets, and high operating cost in terms of net interest income were significant causal factors of failure of finance companies in Sri Lanka.

Management of agricultural research centers in Brazil: A DEA application using a dynamic GMM approach

In this paper, we measure the performance for each of the Brazilian Agricultural Research Corporation research centers by means of a Data Envelopment Analysis model. Performance data are available for a panel covering the period 2002–2009. The approach is instrumentalist, in the sense of Ramalho, Ramalho, and Henriques (2010). We investigate the effects on performance of contextual variable indicators related to the intensity of partnerships and revenue generation. For this purpose, we propose a fractional nonlinear regression model and dynamic GMM (Generalized Method of Moments) estimation. We do not rule out the endogeneity of the contextual variables, cross-sectional correlation or autocorrelation within the panel. We conclude that revenue generation and previous performance scores are statistically significant and positively associated with actual performance.

Remarks on asymptotic efficient estimation for regression effects in stationary and nonstationary models for panel count data

In a panel count data setup, repeated counts of an individual are assumed to be influenced by the individual’s random effect. Consequently, conditional on the random effect, the repeated responses of the individual are assumed to be serially correlated. Under the assumption that the random effects of the individuals follow a normal distribution, Jowaheer and Sutradhar (Statist. Probab. Letters 79 (2009) 1928–1934) have demonstrated that the generalized quasi-likelihood (GQL) estimation approach produces more efficient estimates than the so-called generalized method of moments (GMM) approach for both regression effects and the variance component of the normal random effects. For the cases where the distribution of the random effects is unknown, there exist two estimation approaches, namely the conditional maximum likelihood (CML) and instrumental variables based GMM (IVGMM) approaches, for the estimation of the regression effects. The purpose of this paper is to examine the asymptotic efficiency performances of the CML and IVGMM approaches as compared to the GQL approach for the regression estimation. When the covariates are stationary, that is, time independent, it is, however, known that the CML and IVGMM approaches are useless for the regression estimation, whereas the GQL approach does not encounter any such limitations. For the general case, that is, when the covariates are time dependent, the IVGMM approach appears to be computationally expensive and hence it is not included in efficiency comparison. Between the CML and GQL approaches, it is found through exact asymptotic variance calculations that the GQL approach is asymptotically more efficient than the CML approach in estimating the regression effects. This makes the GQL as a unified efficient approach irrespective of the cases whether the panel count data are stationary or nonstationary.

Finance and Property Rights: Exploring Other Directions

There is a consensus that stronger property rights advance financial development. We provide evidence that the reverse hypothesis is also true. We isolate the structural component in the finance–property rights relationship using an instrument for financial development (private credit) based on an index of exposure to foreign crises, in addition to generalized method of moments approaches for panel data. Our results suggest a one standard deviation increase in private credit from its average in 2005 translates into a 0.5‐ to 1.0‐point increase in property rights. To contextualize this, the difference in property rights between Israel and Uruguay, two countries separated by about one standard deviation in the volume of private credit, was 0.67 points.

Spatial autoregressive models with unknown heteroskedasticity: A comparison of Bayesian and robust GMM approach

Most of the estimators suggested for the estimation of spatial autoregressive models are generally inconsistent in the presence of an unknown form of heteroskedasticity in the disturbance term. The estimators formulated from the generalized method of moments (GMM) and the Bayesian Markov Chain Monte Carlo (MCMC) frameworks can be robust to unknown forms of heteroskedasticity. In this study, the finite sample properties of the robust GMM estimator are compared with the estimators based on the Bayesian MCMC approach for the spatial autoregressive models with heteroskedasticity of an unknown form. A Monte Carlo simulation study provides evaluation of the performance of the heteroskedasticity robust estimators. Our results indicate that the MLE and the Bayesian estimators impose relatively greater bias on the spatial autoregressive parameter when there is negative spatial dependence in the model. In terms of finite sample efficiency, the Bayesian estimators perform better than the robust GMM estimator. In addition, two empirical applications are provided to evaluate relative performance of heteroskedasticity robust estimators.

Broadband penetration and economic growth in ASEAN countries: a generalized method of moments approach

Broadband infrastructure has numerous economic potentials. This study examines the impact of broadband penetration on economic growth in the panel of 10 countries of the Association of Southeast Asian Nations (ASEAN) from 1998 to 2011. Based on the pooled Ordinary Least Squares (OLS), fixed effects and random effects estimates, we found that broadband deployment has a positive relationship with economic growth. In addition, we used the Generalized Method of Moments (GMM) dynamic panel data model to address the reverse causality and potential endogeneity issues in this study. The result also reveals that broadband penetration appears to be a key factor in the explanation of Gross Domestic Product growth. The finding remains robust even after the control for Information and Communication Technology (ICT) goods imports. This result in turn suggests that the utilization of broadband infrastructure and application is expected to enhance the national aggregate outputs.

Bank ownership, performance, and the politics: Evidence from Taiwan

This paper investigates the effects of politics on the performances of financial institutions in Taiwan over the period from 1994 through 2009 using the two-stage generalized method of moments approach. We argue that politics and financial institutions are related and this relationship varies with the ownership of financial institutions. Our main findings are, firstly, during election years, the private financial institutions earned higher ROA and loan growth than the government- and foreign-owned. Secondly, government-owned institutions are not affected by current elections for all measures of performance, while foreign institutions significantly earned lower ROA, higher interest margin, and spent higher overhead costs. The finding that lendings of government-owned institutions are no longer subject to political pressures across time implies partial success of financial reforms in Taiwan.

Combining Revealed and Stated Preference Data to Estimate Preferences for Residential Amenities: A GMM Approach

We show how stated preference information obtained from a choice experiment, and revealed preference information based on housing market transactions, can be combined via generalized method of moments (GMM) estimation. Specifically, we use a moment condition matching the predicted marginal willingness to pay (WTP) from a first-stage hedonic model to the marginal WTP formula implied by the choice experiment utility function. This is coupled with other moments from the choice experiment to produce GMM-based estimates of parameters that reflect the strengths of each data source. Our application values remediation of a contaminated site in Buffalo, New York, and we find evidence in support of estimates arising from our approach. <i>(JEL Q51, Q53)</i>

Generalized Method of Moments Estimation of the Nakagami-m Fading Parameter

The generalized method of moments (GMM) is introduced in the framework of estimating the Nakagami-m fading parameter. This GMM approach provides a systematic procedure for finding the moment-based m parameter estimators. Using the multivariate delta method, we present a derivation for the asymptotic variance of the GMM Nakagami m parameter estimators. Monte Carlo simulation results show that the GMM approach can lead to estimators outperforming existing moment-based m parameter estimators over a wide range of channel conditions. It is shown that the asymptotic performance of these GMM estimators are close to that of the maximum-likelihood based estimator. The proposed method can be easily applied to both noiseless and noisy environments.

Joint estimation of the lognormal-Rician atmospheric turbulence model by the generalized method of moments

The generalized method of moments (GMM) is introduced in the framework of estimating the lognormal-Rician parameters. This GMM approach provides a systematic procedure for finding the moment-based parameter estimators. The GMM estimator can estimate the two shaping parameters of the lognormal-Rician PDF jointly. The asymptotic performance of the GMM estimator is compared with Monte Carlo simulation results. The results show that the GMM approach can lead to estimators with satisfactory performance over a wide range of channel conditions. The proposed method can be easily applied to both noiseless and noisy environments.

GMM versus GQL inferences in semiparametric linear dynamic mixed models

Linear dynamic mixed models are commonly used for continuous panel data analysis in economic statistics. There exists generalized method of moments (GMM) and generalized quasi-likelihood (GQL) inferences for binary and count panel data models, the GQL estimation approach being more efficient than the GMM approach. The GMM and GQL estimating equations for the linear dynamic mixed model can not, however, be obtained from the respective estimating equations under the nonlinear models for binary and count data. In this paper, we develop the GMM and GQL estimation approaches for the linear dynamic mixed models and demonstrate that the GQL approach is more efficient than the GMM approach, also under such linear models. This makes the GQL approach uniformly more efficient than the GMM approach in estimating the parameters of both linear and nonlinear dynamic mixed models.

A functional generalized method of moments approach for longitudinal studies with missing responses and covariate measurement error.

Covariate measurement error and missing responses are typical features in longitudinal data analysis. There has been extensive research on either covariate measurement error or missing responses, but relatively little work has been done to address both simultaneously. In this paper, we propose a simple method for the marginal analysis of longitudinal data with time-varying covariates, some of which are measured with error, while the response is subject to missingness. Our method has a number of appealing properties: assumptions on the model are minimal, with none needed about the distribution of the mismeasured covariate; implementation is straightforward and its applicability is broad. We provide both theoretical justification and numerical results.