Generalized Method Of Moments Tests Research Articles

An evident prescience of determinants of dividend policy of Indian real estate companies

PurposeThis paper aims to make an attempt to identify the determinants of dividend policy by analyzing 125 real estate companies, which are selected on the basis of consistent dividend distribution throughout the study period. Most of these companies either listed with Bombay Stock Exchange or National Stock Exchange.Design/methodology/approachThis paper applies three alternative methods to verify and validate the results obtained from each other method, namely, fully modified ordinary least square (FMOLS), dynamic ordinary least square and generalized method of moments (GMM). Data collected of the selected companies’ post-recession period i.e. 2009-2017. The selected companies have age either 5 years old or more when data are retrieved from the above-mentioned sources. Due to much volatility in the recession period in the real estate firms at the global level, no data have been taken of the firms before March 2009. Moreover, for arriving at good analysis and an adequate number of observations for the study more recent data have been taken.FindingsEmpirical findings of this research paper depict that firm previous dividend, firm risk and liquidity are strong predictors of future dividend payout ratios (DPRs). The results indicate that firm risk as measured through price-earnings ratio (PE ratio) has a positive association with a DPR of selected real estate firms. Lagged DPR used in the GMM test as an exogenous variable is showing positive significant association with DPR. Firm’s growth is found significant in FMOLS and GMM techniques. On the other firm’s size is found significant according to cointegration techniques.Practical implicationsThe present study shall be useful to different stakeholders of real estate companies. Various significant determinants as identified can be used by management for designing optimum dividend policy and providing maximum benefits to existing shareholders. Similarly existing and prospective shareholders may predict the future payment of dividend and accordingly they may take investment decisions in these firms, as the future fund’s requirement of a firm depends upon dividend payment and retention ratio.Originality/valueAs per the authors’ knowledge, there is no single study carried in the post-recession period to predict determinants of dividend policy of real estate sector using three alternatives of methods to verify and validate the results obtained from each other method. The study is carried out after exploring determinant from a diverse range of period of studies (oldest one to latest one).

External Debt and Economic Growth in Nigeria

External credits have been received from various sources including bilateral and multilateral arrangements but the country’s debt is a source of worry since the projects for which these loans were contracted cannot finance the credit facilities. This paper focused on the impact of external debt on economic growth in Nigeria within the period of 1980 to 2016. Thus, secondary data on gross domestic product and external debt were sourced from CBN statistical bulletin and debt management office fact book. The econometric method of Generalized Method of Moments(GMM) test was used. Priori the GMM test is the Kwiatkowski, Phillips, Schemidt and Shin, (KPSS) unit root test to ascertain the stationarity of the variables. Based on the empirical results; the KPSS stationarity test for each of the series showed that all the variables were stationary at order one as their respective LM statistics was less than the critical value at 5%. The GMM test shows that external debt and economic growth has positive and significant relationship with R2 of 54 percent. Therefore, to achieve long-term solution to the problem of external debts burden, government should stimulate domestic production to liberate the Nigerian economy from the shackles of wants and excessive dependence on external economics, which build up debt. Also, government should avoid unnecessary and unproductive borrowing that will serve as a leakage to the economy. This to a large extent will enhance the growth of the Nigerian economy.

Johansen’s Reduced Rank Estimator Is GMM

The generalized method of moments (GMM) estimator of the reduced-rank regression model is derived under the assumption of conditional homoscedasticity. It is shown that this GMM estimator is algebraically identical to the maximum likelihood estimator under normality developed by Johansen (1988). This includes the vector error correction model (VECM) of Engle and Granger. It is also shown that GMM tests for reduced rank (cointegration) are algebraically similar to the Gaussian likelihood ratio tests. This shows that normality is not necessary to motivate these estimators and tests.

Portfolio Analysis Using Stochastic Dominance, Relative Entropy, and Empirical Likelihood

This study formulates portfolio analysis in terms of stochastic dominance, relative entropy, and empirical likelihood. We define a portfolio inefficiency measure based on the divergence between given probabilities and the nearest probabilities that rationalize a given portfolio for some admissible utility function. When applied to a sample of time-series observations in a blockwise fashion, the inefficiency measure becomes a likelihood ratio statistic for testing inequality moment conditions. The limiting distribution of the test statistic is bounded by a chi-squared distribution under general sampling schemes, allowing for conservative large-sample testing. We develop a tight numerical approximation for the test statistic based on a two-stage optimization procedure and piecewise linearization techniques. A Monte Carlo simulation study of the empirical likelihood ratio test shows superior small-sample properties compared with various generalized method of moments tests. An application analyzes the efficiency of a passive stock market index in data sets from the empirical asset pricing literature. This paper was accepted by Manel Baucells, decision analysis.

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.

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.

Robust small sample accurate inference in moment condition models

Procedures based on the Generalized Method of Moments (GMM) are basic tools in modern econometrics. In most cases, the theory available for making inference with these procedures is based on first order asymptotic theory. It is well-known that the (first order) asymptotic distribution does not provide accurate p-values and confidence intervals in moderate to small samples. Moreover, in the presence of small deviations from the assumed model, p-values and confidence intervals based on classical GMM procedures can be drastically affected (nonrobustness). Several alternative techniques have been proposed to improve the accuracy of GMM procedures. These alternatives address either the first order accuracy of the approximations (information and entropy econometrics (IEE)) or the nonrobustness (Robust GMM estimators and tests). A new procedure which combines robustness properties and accuracy in small samples is proposed. Specifically, IEE techniques are combined with robust methods obtained by bounding the original orthogonality function. This leads to new robust estimators and tests in moment condition models with excellent finite sample accuracy. Finally, the accuracy of the new statistic is illustrated with Monte Carlo simulations for three models on overidentifying moment conditions.

GMM Tests for SD Efficiency of a Given Portfolio

We develop empirical tests for Second-order Stochastic Dominance (SSD) efficiency and Third-order Stochastic Dominance (TSD) efficiency of a given investment portfolio relative to all possible portfolios formed from a set of assets. Contrary to existing Linear Programming tests, our tests are embedded in the Generalized Method of Moments (GMM) framework, which results in superior statistical power properties and increases the comparability with existing mean-variance tests. Using these tests, we demonstrate that the mean-variance inefficiency of the CRSP all-share index relative to beta-sorted portfolios can be explained by tail risk not captured by variance.

Robust GMM tests for structural breaks

We propose a class of new robust Generalized Method of Moments (GMM) tests for endogenous structural breaks. The tests are based on supremum, average and exponential functionals derived from robust GMM estimators with bounded influence function. We study the theoretical local robustness properties of the new tests and show that they imply a uniformly bounded asymptotic sensitivity of size and power under general local deviations from a reference model. We then analyze the finite sample performance of the new robust tests via Monte Carlo simulations, and compare it with that of classical GMM tests for structural breaks. In large samples, we find that the performance of classical asymptotic GMM tests can be quite unstable under slight departures from some given reference distribution. In particular, the loss in power can be substantial in some models. Robust asymptotic tests for structural breaks yield important power improvements both in exactly identified and overidentified model settings. In small samples, bootstrapped versions of the classical and the robust GMM tests provide accurate and stable empirical levels also for quite small sample sizes. However, bootstrapped robust GMM tests are found to provide again a higher finite sample efficiency.

DETECTING LACK OF IDENTIFICATION IN GMM

One of the key assumptions of the standard linear instrumental variables (IV) model is that the instruments and endogenous variables are correlated. This is the identification assumption, without which the usual IV estimator is neither consistent nor asymptotically normal. If the correlation between the instruments and the endogenous variables is nonzero, but slight, then the conventional Gaussian asymptotic theory for the IV model can nevertheless provide a very poor approximation to the actual sampling distribution of estimators and test statistics. Recognizing the identification assumption on which the IV model relies, it is quite common in the applied literature to test for instrument relevance by a first-stage F-test. The null hypothesis is one of a total lack of identification. A rejection of this hypothesis by no means implies that issues of weak instruments can be ignored (Staiger and Stock, 1997). But a failure to reject this hypothesis is a strong indication of identification difficulties. The firststage F-test is an important and useful diagnostic in the IV model. The generalized method of moments (GMM) model (Hansen, 1982) nests the linear IV model as a special case. Not surprisingly, analogous issues arise in this model. Researchers have found that, in many contexts, the conventional Gaussian asymptotic theory provides a poor approximation to the sampling distribution of GMM estimators and test statistics. There are many possible reasons why this could happen, but they include identification problems. However, I am aware of no test of the identification condition in the nonlinear-inparameters GMM model in the existing literature. This paper proposes such a

GMM tests for the Katz family of distributions

Generalized method of moments (GMM) is used to develop tests for discriminating discrete distributions among the two-parameter family of Katz distributions. Relationships involving moments are exploited to obtain identifying and over-identifying restrictions. The asymptotic relative efficiencies of tests based on GMM are analyzed using the local power approach and the approximate Bahadur efficiency. The paper also gives results of Monte Carlo experiments designed to check the validity of the theoretical findings and to shed light on the small sample properties of the proposed tests. Extensions of the results to compound Poisson alternative hypotheses are discussed.

Robust inference with GMM estimators

The local robustness properties of generalized method of moments (GMM) estimators and of a broad class of GMM based tests are investigated in a unified framework. GMM statistics are shown to have bounded influence if and only if the function defining the orthogonality restrictions imposed on the underlying model is bounded. Since in many applications this function is unbounded, it is useful to have procedures that modify the starting orthogonality conditions in order to obtain a robust version of a GMM estimator or test. We show how this can be obtained when a reference model for the data distribution can be assumed. We develop a flexible algorithm for constructing a robust GMM (RGMM) estimator leading to stable GMM test statistics. The amount of robustness can be controlled by an appropriate tuning constant. We relate by an explicit formula the choice of this constant to the maximal admissible bias on the level or (and) the power of a GMM test and the amount of contamination that one can reasonably assume given some information on the data. Finally, we illustrate the RGMM methodology with some simulations of an application to RGMM testing for conditional heteroscedasticity in a simple linear autoregressive model. In this example we find a significant instability of the size and the power of a classical GMM testing procedure under a non-normal conditional error distribution. On the other side, the RGMM testing procedures can control the size and the power of the test under non-standard conditions while maintaining a satisfactory power under an approximatively normal conditional error distribution.

ALTERNATIVE TESTS OF THE ZERO-BETA CAPM

In this paper I develop an analytical Wald test of the zero-beta capital asset pricing model (CAPM) in a simple iid (independent and identically distributed) setting and extend the Wald test to the generalized method of moments (GMM) framework that allows for a general form of serial correlation and conditional heteroskedasticity. The size and power of these tests, along with some existing tests, are investigated under normal errors and other alternative distributional specifications. The results show that, under alternative distributional assumptions for the error terms, the proposed Wald and GMM tests have reliable sizes for medium-size samples, whereas the likelihood ratio test (LRT) rejects the efficiency too often, especially when the error terms significantly deviate from normality. However, the LRT is more powerful than both the Wald and GMM tests. JEL classification: C13, C53, G14.

Testing multiple equation systems for common nonlinear components

This paper provides a generalized method of moments test for common nonlinear components in multiple time series. The test statistic is presented in terms of the canonical correlations between the multiple series and a judicially chosen set of test regressors, and its performance in small samples is evaluated using Monte Carlo simulations. Two applications highlight the usefulness of this test in multivariate modelling. The first shows that the asymmetries which characterize the US and Canadian business cycles can be attributed to a common nonlinear factor, while the second shows that a single nonlinear factor can account for neglected nonlinearity in an empirical real business cycle model. Each of the implied nonlinear factor models shows potential for forecasting.

Some non-parametric tests for duration dependence: an application to UK business cycle data

A distinction between Fisher's implied data-generating process for Monte Carlo cycles and the more general Markov process leads to non-parametric tests for duration dependence. Tests are based on the method of moments, Tauchen's generalized method of moments (GMM) procedure, and a statistic whose null distribution probability limit is zero. Using finite-sample critical values obtained by Monte Carlo methods, our test results are remarkably consistent. The null distribution of the GMM test statistic for samples of the size considered is distinctly non-normal, so that asymptotic critical values give erroneous results. The tests are applied to UK business cycle data for 1854–1992. There is evidence for duration dependence in expansions but not in contractions.

Analytical GMM Tests: Asset Pricing with Time-Varying Risk Premiums

We propose alternative generalized method of moments (GMM) tests that are analytically solvable in many econometric models, yielding in particular analytical GMM tests for asset pricing models with time-varying risk premiums. We also provide simulation evidence showing that the proposed tests have good finite sample properties and that their asymptotic distribution is reliable for the sample size commonly used. We apply our tests to study the number of latent factors in the predictable variations of the returns on portfolios grouped by industries. Using data from October 1941 to September 1986 and two sets of instrumental variables, we find that the tests reject a one-factor model but not a two-factor one.