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Standard multivariate tests of mean variance efficiency (MVE) have been criticised on the grounds that they require regression residuals to have a multivariate normal distribution. Generally, the existing evidence suggests that the normality assumption is questionable, even for monthly returns. MacKinlay and Richardson (1991) developed a generalised method of moments (GMM) framework which provides tests which are valid under much weaker distributional assumptions. They examined monthly US data formed into size based portfolios, for mean‐variance efficiency relative to the Sharpe‐Lintner CAPM. They found that inferences regarding mean‐variance efficiency can be sensitive to the test considered. In this paper we further investigate their GMM tests using monthly Australian data over the period 1974 to 1994. We extend upon their analysis to consider an alternative version of their GMM test and also to examine a zero‐beta version of the CAPM. Similar to the US case, our results also indicate sensitivity of inferences to the tests used. Finally, while we find that the GMM tests generally provide rejection of mean‐variance efficiency, tests involving the zero‐beta CAPM, particularly when a value‐weighted market index is used, prove less prone to rejection.

Statistical inference (estimation and testing) for the stochastic volatility (SV) model Taylor (1982, 1986) is challenging, especially likelihood-based methods which are difficult to apply due to the presence of latent variables. The existing methods are either computationally costly and/or inefficient. In this paper, we propose computationally simple estimators for the SV model, which are at the same time highly efficient. The proposed class of estimators uses a small number of moment equations derived from an ARMA representation associated with the SV model, along with the possibility of using “winsorization” to improve stability and efficiency. We call these ARMA-SV estimators. Closed-form expressions for ARMA-SV estimators are obtained, and no numerical optimization procedure or choice of initial parameter values is required. The asymptotic distributional theory of the proposed estimators is studied. Due to their computational simplicity, the ARMA-SV estimators allow one to make reliable – even exact – simulation-based inference, through the application of Monte Carlo (MC) test or bootstrap methods. We compare them in a simulation experiment with a wide array of alternative estimation methods, in terms of bias, root mean square error and computation time. In addition to confirming the enormous computational advantage of the proposed estimators, the results show that ARMA-SV estimators match (or exceed) alternative estimators in terms of precision, including the widely used Bayesian estimator. The proposed methods are applied to daily observations on the returns for three major stock prices (Coca-Cola, Walmart, Ford) and the S&P Composite Price Index (2000–2017). The results confirm the presence of stochastic volatility with strong persistence.

This chapter proposes a simple, fairly general, test for global identification of unconditional moment restrictions implied from point-identified conditional moment restrictions. The test is a Hausman-type test based on the Hausdorff distance between an estimator that is consistent even under global identification failure of the unconditional moment restrictions, and an estimator of the identified set of the unconditional moment restrictions. The proposed test has a χ2 limiting distribution and is also able to detect weak identification. Some Monte Carlo experiments show that the proposed test has competitive finite sample properties already for moderate sample sizes.

This 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.

This 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.

Empirical 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.

The 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.

As 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).

It is shown in the literature that the Arellano–Bond type generalized method of moments (GMM) of dynamic panel models is asymptotically biased (e.g., Hsiao & Zhang, 2015; Hsiao & Zhou, 2017). To correct the asymptotical bias of Arellano–Bond GMM, the authors suggest to use the jackknife instrumental variables estimation (JIVE) and also show that the JIVE of Arellano–Bond GMM is indeed asymptotically unbiased. Monte Carlo studies are conducted to compare the performance of the JIVE as well as Arellano–Bond GMM for linear dynamic panels. The authors demonstrate that the reliability of statistical inference depends critically on whether an estimator is asymptotically unbiased or not.

We show how to use a smoothed empirical likelihood approach to conduct efficient semiparametric inference in models characterized as conditional moment equalities when data are collected by variable probability sampling. Results from a simulation experiment suggest that the smoothed empirical likelihood based estimator can estimate the model parameters very well in small to moderately sized stratified samples.

In this study, the relationship between female labour force participation rate and economic growth is investigated in middle-income countries. The study covers the period of 2001–2016 by employing a dynamic panel approach. Pooled Ordinary Least Square and Fixed Effects model estimations are calculated as a decision criterion to select proper GMM Method. The outcomes indicate that the proper estimation technique, which is a System-GMM model, evidences the U Feminisation Theory for the middle-income countries while controlling all other factors.

The novelty of this study is that the research not only employs both difference and system generalised method of moments (GMM) estimators but also includes main explanatory variables such as education, fertility, and total labour force rate. The study provides an opportunity to review the U-shape nexus between the female labour force and economic growth while controlling education, fertility and total labour participation rate.

The estimation implies that middle-income countries support a U-shaped relationship. The fertility rate does not impact on the female labour force, and education and total labour force level have a positive influence on women's participation in the labour market.

This study used data that include the period of 2001–2016 for middle-income countries. So, further studies can use different periods of data or different countries.

The authors emphasise the importance of economic growth for female labour force for middle-income countries. Thus, a country intending to increase female labour force should also focus on its economic growth. As the study points out, middle-income countries staying under the minimum threshold, $4698.15 (per capita), should priorities their economic improvement policies to reach their female labour force participation goal. Those countries also should be prepared for a female labour force participation declining phase until they reach the turning point income level.

Furthermore, education is one of the critical determinants that have an impact on FLFPR. The equal opportunity for both genders to engage in education should be considered as a policy. If females do not have an equal chance to enrolment in education, it may influence the policy of increasing female labour force adversely. Fertility rate appears no more statistically significant in our study. Moreover, today, there are some countries they practise equality between genders by providing equally extended parental leave, which may be a promising policy for gender equality in the labour force and may worth a try.

Some previous studies may suffer model mistakes due to lack of consideration the endogeneity problem and bias issue of the results as suggested by Tam (2011). Moreover, previous studies tend to choose either studying U-feminisation as excluding other variables or studying determinants of female labour force participation rate as excluding U-feminisation theory. There is not any panel data study acknowledging both concepts by using recent data to the best knowledge of the authors. Thus, the novelty of this study is that the research not only employs both difference and system generalised method of moments (GMM) estimators but also includes main explanatory variables such as education, fertility, and total labour force rate. The study provides an opportunity to review the U-shape nexus between the female labour force and economic growth while controlling education, fertility and total labour participation rate.

In this paper, we consider the estimation of volatility parameters in the context of a linear regression where the disturbances follow a stochastic volatility (SV) model of order one with Gaussian log-volatility. The linear regression represents the conditional mean of the process and may have a fairly general form, including for example finite-order autoregressions. We provide a computationally simple two-step estimator available in closed form. Under general regularity conditions, we show that this two-step estimator is asymptotically normal. We study its statistical properties by simulation, compare it with alternative generalized method-of-moments (GMM) estimators, and present an application to the S&P composite index.

This study aims to investigate the effects of corporate board and audit committee characteristics and ownership structures on market-based financial performance of listed firms in Thailand.

It applies system GMM (generalized method of moments) as the baseline estimator approach, and ordinary least squares and fixed effects for robustness checks on a sample of 452 firms listed on the Thai Stock Exchange for the period 2000-2016.

Relying mainly on the system GMM estimator, the empirical results indicate some emerging trends in the Thai economy. Contrary to expectations for an emerging market and prior research findings, ownership structures, particularly ownership concentration and family ownership, appear to have no significant influence on market-based firm performance, while managerial ownership exerts a positive effect on performance. Moreover, as expected, board structure variables such as board independence; size; meeting and dual role; and audit committee meeting show significant explanatory power on market-based firm performance in Thai firms.

These findings are important for policymakers in constructing an appropriate set of governance mechanisms in an emerging market context, and for corporate entities and investors in shaping their understanding of corporate governance in the Thai institutional context.

Unlike previous literature on the Thai market, this study is the first to use the more advanced econometric method known as system GMM estimator for addressing causality/endogeneity issues in governance–performance relationships. The findings indicate new trends in the explanatory power of ownership structure variables on market-based firm performance in Thai-listed firms.

This study aims to explore the relationship between tourism and women’s employment in 32 South American and Caribbean countries from 1996 to 2020.

In this paper, both static (fixed effects and random effects) and dynamic panel data models (system and differenced generalized method of moments) are used. In addition to gross domestic product, trade, education and urban population are also considered in this study.

According to the findings, a boost in tourism led to an increase in women’s engagement in the economy and service sectors. This paper also explores the efficiency of alternate methods to deal with various models of women labor force (WLF) involvement in various sectors. Women’s employment opportunities in the service sector expand as a result of tourism, but in the agricultural and industrial sectors, that employment opportunity is reduced.

This study investigated the impact of tourism on WLF participation and found that it had a significant impact. This study, on the other hand, specifically contributed to the tourism sector in some specific study areas, such as tourism and agriculture, service and industry sectors. This study also displays that female participation in South America and the Caribbean countries is increasing and women are shifting away from traditional economic sectors.

This is the pioneering study to discover tourism and female participation in employment in South American and Caribbean countries. The findings of this study have important implications for future studies and policy debates examining the consequence of the tourism industry on WLF.