Generalized Method Of Moments Framework Research Articles

Board Governance Mechanisms and Capital Structure of Saudi Non-Financial Listed Firms: A Dynamic Panel Analysis

The dynamic trade-off model emphasizes that random shocks and other constraints may force firms to deviate from their optimum debt ratio. In addition, firms must incur adjustment costs for them to revert to their target level due to market frictions. However, prior literature in Saudi does not consider the effect of adjustment costs in determining how board governance mechanisms influence capital structure. By exploiting the generalized method of moments framework (GMM), this study analyzed the balanced panel of 100 Saudi non-financial listed firms covering the period from 2010 to 2019. The research found that Saudi firms observed a dynamic adjustment to attain their target leverage at 30.46% annually. Besides that, findings from the present study equally indicate strongly that board size and CEO tenure are negatively associated with leverage. There is also strong evidence that Saudi firms with a substantial number of independent directors pursue a more levered capital structure. Gender diversity appears insignificant in predicting the firms’ target debt ratio. Consequently, the Saudi board of directors and firms’ managers should consider the adjustment costs and other market frictions when designing the optimum debt-equity ratio.

Regional business cycles and manufacturing productivity: empirical evidence in Colombia

PurposeThe aim of this paper is to disentangle the contemporaneous and non-contemporaneous relationship between regional business cycles and manufacturing productivity in a developing country, namely Colombia.Design/methodology/approachThe methodology is quantitative. To deal with the problems of endogeneity in the production function and with the law motion of productivity (the Markov process), the authors obtain Total Factor Productivity (TFP) through the Wooldridge’s two equations system that can be jointly estimated under the generalized method of moments framework (GMM). Secondly, to avoid bias we estimate regional business cycles through the Kalman filter. Subsequently, we implement an instrumental variables/generalized method of moments regression (IV/GMM) to capture the contemporaneous and endogenous TFP–GDP cycles’ linkage at the regional level. Lastly, to deal with the non-contemporaneous link, the authors estimate a vector autoregressive model with exogenous variables (VARX) for each region. We also present the corresponding impulse–response functions.FindingsThe authors’ general results suggest a remarkable causality, both contemporary and non-contemporary, from productivity to GDP (but not vice versa) in the most developed regions of the country. This implied productivity could influence in the economic growth of regions in short and long runs. These results are different than those expected by economic theory and should be considered by local economic policy makers.Research limitations/implicationsThe authors consider that a more detailed analysis should be carried out at the level of each sector within the manufacturing industry to further clarify these findings.Practical implicationsThe policy should be oriented to obtaining cutting-edge technologies through subsidies, and also should facilitate the access to financial capital and the investment in R&D laboratories. On the other hand, the link with international trade also must be reinforced because the importing of intermediate inputs and exporting of output allow the firms to obtain embodied technologies, also to incur on learning by exporting and importing processes and finally to gain experience and competitiveness in foreign markets.Social implicationsThe causality in the region that provides more than 50% of economic activity within the country (Third region) is only in one directional, from TFP towards gross domestic product (GDP) and not vice versa. As the influence from GDP towards TFP is minimal in the remaining regions, the manufacturing productivity influences both short and long run regional economic growth in Colombia. This implies that economic policy at the level of macro-region must be modified; the government should give additional support to the manufacturing sector, especially in developed regions and for the small and medium-sized enterprises (SMEs) (wich represent 92% of manufacturing firms) to increase economic growth in the future.Originality/valueThe authors’ contribution is threefold. First, they pay special attention to the contemporaneous cyclical relationship (i.e. pro-cyclical, counter-cyclical or acyclic) and the non-contemporaneous causality with productivity. Second, they estimate productivity with the GMM two equation system considering an endogenous Markov process. Third, to the best of their knowledge, at least in the case of Latin America, there are no studies in this direction combining these statistic methods, including that of Colombia.

Censored linear regression in the presence or absence of auxiliary survival information.

There has been a rising interest in better exploiting auxiliary summary information from large databases in the analysis of smaller-scale studies that collect more comprehensive patient-level information. The purpose of this paper is twofold: first, we propose a novel approach to synthesize information from both the aggregate summary statistics and the individual-level data in censored linear regression. We show that the auxiliary information amounts to a system of nonsmooth estimating equations and thus can be combined with the conventional weighted log-rank estimating equations by using the generalized method of moments (GMM) approach. The proposed methodology can be further extended to account for the potential inconsistency in information from different sources. Second, in the absence of auxiliary information, we propose to improve estimation efficiency by combining the overidentified weighted log-rank estimating equations with different weight functions via the GMM framework. To deal with the nonsmooth GMM-type objective functions, we develop an asymptotics-guided algorithm for parameter and variance estimation. We establish the asymptotic normality of the proposed GMM-type estimators. Simulation studies show that the proposed estimators can yield substantial efficiency gain over the conventional weighted log-rank estimators. The proposed methods are applied to a pancreatic cancer study forillustration.

Heteroskedasticity- and Autocorrelation-robust F and t Tests in Stata

In this article, we consider time-series, ordinary least-squares, and instrumental-variable regressions and introduce a new pair of commands, har and hart, that implement more accurate heteroskedasticity- and autocorrelation-robust (HAR) F and t tests. These tests represent part of the recent progress on HAR inference. The F and t tests are based on the convenient F and t approximations and are more accurate than the conventional chi-squared and normal approximations. The underlying smoothing parameters are selected to target the type I and type II errors, which are the two fundamental objects in every hypothesis testing problem. The estimation command har and the postestimation test command hart allow for both kernel HAR variance estimators and orthonormal-series HAR variance estimators. In addition, we introduce another pair of new commands, gmmhar and gmmhart, that implement the recently developed F and t tests in a two-step generalized method of moments framework. For these commands, we opt for the orthonormal-series HAR variance estimator based on the Fourier bases because it allows us to develop convenient F and t approximations as in the first-step generalized method of moments framework. Finally, we present several examples to demonstrate these commands.

Outer-product-of-gradients tests for spatial autoregressive models

For Lagrangian multiplier (LM) tests of restrictions on parameters in spatial autoregressive (SAR) models with (SARAR models) or without SAR disturbances, their outer-product-of-gradient (OPG) variants can be simple and robust to unknown heteroskedasticity. However, for certain tests, asymptotic distributions of test statistics might depend on the constrained maximum likelihood or quasi maximum likelihood (QML) estimators, so their OPG variants would not be valid. To overcome such a hurdle, we propose to use C(α)-type score vectors to obtain valid OPG variants. Such OPG tests can be systematically constructed for SARAR models with homoskedastic and heteroskedastic disturbances, which might not be normally distributed. They also have the advantage that any n-consistent estimator can be used in place of a restricted QML estimate. In particular, OPG tests based on generalized method of moments (GMM) estimates are computationally simple and powerful compared to LM tests. Corresponding OPG tests based on C(α)-type gradient vectors in the GMM framework are also investigated.

An Interior Penalty Method for the Generalized Method of Moments

The generalized method of moments (GMM) is a technique to discretize integral equations that permits integration of different types of basis functions as well as different geometric descriptions using a partition of unity framework. While accuracy and efficacy of the method have been demonstrated, the integration quadratures required to compute the inner products are often high, as they have to respect spatial variation of the integrand. To overcome this problem, we introduce an interior penalty function method within the GMM framework. The penalty formulation yields solutions that are smooth and accurate both in surface currents and fields with significantly lower numerical quadrature orders than would be required for the uncompensated operator. To demonstrate and analyze the method, we conduct an analytical and numerical investigation of the properties of the penalty method applied to the 2-D TE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Z</sup> electric field integral equation (EFIE) operator.

The Pástor-Stambaugh empirical model revisited: Evidence from robust instruments

This article uses a parsimonious and robust instrumental variables technique to minimize the specification errors in the Pastor-Stambaugh (PS) empirical model. In particular, we use an improvement of Hansen’s generalized method of moments (GMM) that uses higher moments that are robust instruments. We also propose a robustness test for these instruments. Results with these instruments indicate that the liquidity measure used in the PS empirical model is improperly measured and/or is ill-conceived. Although this article applies a GMM framework to a financial application, this technique is applicable to estimation problems in the presence of specification errors in all areas of quantitative finance.

Fixed-Smoothing Asymptotics in a Two-Step Generalized Method of Moments Framework

This paper develops the fixed-smoothing asymptotics in a two-step generalized method of moments (GMM) framework. Under this type of asymptotics, the weighting matrix in the second-step GMM criterion function converges weakly to a random matrix and the two-step GMM estimator is asymptotically mixed normal. Nevertheless, the Wald statistic, the GMM criterion function statistic, and the Lagrange multiplier statistic remain asymptotically pivotal. It is shown that critical values from the fixed-smoothing asymptotic distribution are high order correct under the conventional increasing-smoothing asymptotics. When an orthonormal series covariance estimator is used, the critical values can be approximated very well by the quantiles of a noncentral F distribution. A simulation study shows that statistical tests based on the new fixed-smoothing approximation are much more accurate in size than existing tests.

Inference for Income Distributions Using Grouped Data

We develop a general approach to estimation and inference for income distributions using grouped or aggregate data that are typically available in the form of population shares and class mean incomes, with unknown group bounds. We derive generic moment conditions and an optimal weight matrix that can be used for generalized method-of-moments (GMM) estimation of any parametric income distribution. Our derivation of the weight matrix and its inverse allows us to express the seemingly complex GMM objective function in a relatively simple form that facilitates estimation. We show that our proposed approach, which incorporates information on class means as well as population proportions, is more efficient than maximum likelihood estimation of the multinomial distribution, which uses only population proportions. In contrast to the earlier work of Chotikapanich, Griffiths, and Rao, and Chotikapanich, Griffiths, Rao, and Valencia, which did not specify a formal GMM framework, did not provide methodology for obtaining standard errors, and restricted the analysis to the beta-2 distribution, we provide standard errors for estimated parameters and relevant functions of them, such as inequality and poverty measures, and we provide methodology for all distributions. A test statistic for testing the adequacy of a distribution is proposed. Using eight countries/regions for the year 2005, we show how the methodology can be applied to estimate the parameters of the generalized beta distribution of the second kind (GB2), and its special-case distributions, the beta-2, Singh–Maddala, Dagum, generalized gamma, and lognormal distributions. We test the adequacy of each distribution and compare predicted and actual income shares, where the number of groups used for prediction can differ from the number used in estimation. Estimates and standard errors for inequality and poverty measures are provided. Supplementary materials for this article are available online.

Time-Varying Coefficients in a GMM Framework: Estimation of a Forward Looking Taylor Rule for the Federal Reserve

This article deals with the estimation of a time-varying coefficients equation with endogenous regressors. A non-parametric approach is proposed, combining the Generalized Method of Moments (GMM) with the smoothing splines litterature as in Hodrick and Prescott (1981). This new method is used to analyze the evolution of a forward-looking Taylor rule for the Federal Reserve (FED) from 1960 until 2006. It suggests that monetary policy accommodated inflation during the 60s and the 70s whereas the chairmanship of P. Volcker was a turning point toward a more aggressive stance on inflation. In addition, monetary policy became more and more countercyclical.

Bayesian generalized method of moments

We propose the Bayesian generalized method of moments (GMM), which is particularly useful when likelihood-based methods are difficult. By deriving the moments and concatenating them together, we build up a weighted quadratic objective function in the GMM framework. As in a normal density function, we take the negative GMM quadratic function divided by two and exponentiate it to substitute for the usual likelihood. After specifying the prior distributions, we apply the Markov chain Monte Carlo procedure to sample from the posterior distribution. We carry out simulation studies to examine the proposed Bayesian GMM procedure, and illustrate it with a real data example.

Limited information likelihood and Bayesian analysis

In this paper, we study how to embed the optimal generalized method of moments (GMM) estimate in a likelihood-based inference framework and the Bayesian framework. First, we derive a limited information likelihood (LIL) under some moment-based limited information available in GMM based on entropy theory of I-projection theory. Second, we study a limited information Bayesian framework in which the posterior is derived from the LIL and a prior. As the LIL enables us to incorporate GMM or related inference methods in the likelihood-based inference framework, it allows us a rich set of practical applications in the Bayesian framework in which the posterior is obtained from a likelihood and a prior. Our results are primarily large sample results as inference in the underlying GMM framework is usually justified in asymptotics. Investigation of large sample properties of the posterior derived from the LIL reveals an interesting relation between the Bayesian and the classical distribution theories.

Testing the Heath-Jarrow-Morton/Ho-Lee Model of Interest Rate Contingent Claims Pricing

This paper presents empirical tests of the constant volatility version of the Heath, Jarrow, and Morton model, which is also the continuous time limit of the Ho and Lee model. Using a generalized method of moments (GMM) test on three years of daily data for Eurodollar futures and futures options, the model can be rejected for most subperiods. Various biases in the fitted option prices relative to the market prices are documented through a regression study. The small sample properties and power of the GMM framework to this setting are also studied through simulations.