Search results

The Hausman test is used in applied economic work as a test of misspecification. It is most commonly thought of as a test of whether one or more explanatory variables in a regression model are endogenous. The usual Hausman contrast test requires one estimator to be efficient under the null hypothesis. If data are heteroskedastic, the least squares estimator is no longer efficient. The first option is to estimate the covariance matrix of the difference of the contrasted estimators, as suggested by Hahn, Ham, and Moon (2011). Other options for carrying out a Hausman-like test in this case include estimating an artificial regression and using robust standard errors. Alternatively, we might seek additional power by estimating the artificial regression using feasible generalized least squares. Finally, we might stack moment conditions leading to the two estimators and estimate the resulting system by GMM. We examine these options in a Monte Carlo experiment. We conclude that the test based on the procedure by Hahn, Ham, and Moon has good properties. The generalized least squares-based tests have higher size-corrected power when heteroskedasticity is detected in the DWH regression, and the heteroskedasticity is associated with a strong external IV. We do not consider the properties of the implied pretest estimator.

Hausman (1978) represented a tectonic shift in inference related to the specification of econometric models. The seminal insight that one could compare two models which were both consistent under the null spawned a test which was both simple and powerful. The so-called ‘Hausman test’ has been applied and extended theoretically in a variety of econometric domains. This paper discusses the basic Hausman test and its development within econometric panel data settings since its publication. We focus on the construction of the Hausman test in a variety of panel data settings, and in particular, the recent adaptation of the Hausman test to semiparametric and nonparametric panel data models. We present simulation experiments which show the value of the Hausman test in a nonparametric setting, focusing primarily on the consequences of parametric model misspecification for the Hausman test procedure. A formal application of the Hausman test is also given focusing on testing between fixed and random effects within a panel data model of gasoline demand.

The different types of estimators of rational expectations models are surveyed. A key feature is that the model′s solution has to be taken into account when it is estimated. The two ways of doing this, the substitution and errors‐in‐variables methods, give rise to different estimators. In the former case, a generalised least‐squares or maximum‐likelihood type estimator generally gives consistent and efficient estimates. In the latter case, a generalised instrumental variable (GIV) type estimator is needed. Because the substitution method involves more complicated restrictions and because it resolves the solution indeterminacy in a more arbitary fashion, when there are forward‐looking expectations, the errors‐in‐variables solution with the GIV estimator is the recommended combination.

This research empirically investigates the evidence of the financial development and economic growth nexus in sub-Saharan Africa from 1995 to 2022.

A series of preliminary tests are conducted before using the two-stage estimated generalized least squares and robust least squares methods for the analysis. Two indices are constructed to measure financial development: one for the banking sector indicators and another for the market-based indicators (Ustarz and Fanta, 2021).

The results indicate that the banking sector index significantly impacts the gross domestic product (GDP) per capita positively. The market sector index has a negatively significant effect on the GDP per capita. Government expenditure has a positive impact on the GDP per capita.

Policymakers in sub-Saharan Africa should improve and implement finance–growth inclusive strategies that promote financial reforms and development to efficiently impact all population sectors. Policymakers should take stringent measures to ensure that the banking sector's development is sustainable to lead economic growth. The governments should strategize and promote capital market development using favorable listing rules for companies in the stock markets. Global stock market integration should be encouraged to diversify risks, increase public awareness, raise investors' confidence level and reduce stock market impediments like high taxes and regulatory barriers.

Previous study findings on the financial development and economic growth nexus are inconclusive and debatable. This study employs the financial development index approach.

The purpose of this paper is to study the identification methods for multivariable nonlinear Box‐Jenkins systems with autoregressive moving average (ARMA) noises, based on the auxiliary model and the multi‐innovation identification theory.

A multi‐innovation generalized extended least squares (MI‐GELS) and a multi‐innovation generalized ex‐tended stochastic gradient (MI‐GESG) algorithms are developed for multivariable nonlinear Box‐Jenkins systems based on the auxiliary model. The basic idea is to construct an auxiliary model from the measured data and to replace the unknown terms in the information vector with their estimates (i.e. the outputs of the auxiliary model).

It is found that the proposed algorithms can give high accurate parameter estimation compared with existing stochastic gradient algorithm and recursive extended least squares algorithm.

In this paper, the AM‐MI‐GESG and AM‐MI‐GELS algorithms for MIMO Box‐Jenkins systems with nonlinear input are presented using the multi‐innovation identification theory and the proposed algorithms can improve the parameter estimation accuracy. The paper provides a simulation example.

We formulate generalized maximum entropy estimators for the general linear model and the censored regression model when there is first order spatial autoregression in the dependent variable. Monte Carlo experiments are provided to compare the performance of spatial entropy estimators relative to classical estimators. Finally, the estimators are applied to an illustrative model allocating agricultural disaster payments.

The present study aimed to examine the effect of received remittance inflows on the price level ratio of the purchasing power parity conversion factor to the market exchange rate in 36 developed and developing countries from 2004 to 2020.

The panel data conducted a comparative analysis and used panel least squares, regression with Driscoll-Kraay standard errors of fixed effect, random effect, feasible generalised least squares and maximum likelihood robust least squares to overcome the heterogeneity issue. Furthermore, the two-step difference generalised method of moments to overcome the endogeneity issue. Diagnostic tests were used to increase robustness.

In the studied countries, there was a statistically significant negative relationship between received remittance inflows and the price-level ratio of the purchasing power parity conversion factor to the market exchange rate. This relationship explains why remittance flows depreciate the real exchange rate. The study’s results also indicated that attracting investments can improve the quality of institutions despite high tax rates, leading to low tax revenue.

The current study findings enrich the understanding of policies of how governments should minimise tariff rates on capital imports and introduce export-oriented incentive programmes. The study also revealed that Dutch disease can occur due to differences in the demand structure and manufacturing development policy.

The purpose of this paper is to investigate the determinants of portfolio equity flows to the Sub-Saharan African (SSA) region over the period 1996-2010.

The study uses a sample of 14 SSA countries to estimate the baseline regression through employing the system generalized methods of moment dynamic panel estimation framework. To check the robustness of the estimation results, the study further analyses the data set using the random effects-generalized least squares (EGLS) estimator. The Random effects-generalized least squares estimator is also referred to a the Estimated Generalized least Squares (EGLS) estimator.

The paper finds a significant positive relationship between financial development and portfolio equity flows. Furthermore, while the study surprisingly finds trade openness to have a significant negative relationship, political stability is found to have a significant positive relationship with portfolio equity. To check for the robustness of these results, the authors further analyse the data set using the random EGLS estimator. The result of the EGLS estimator confirms that there is a robust positive relationship between financial development and portfolio equity flows to SSA. However, the results suggest that neither trade openness nor political stability is a robust determinant of portfolio equity flows to the sub-region.

Policy measures should aim at enhancing financial sector development, political stability and rule of law. A transparent judicial system that enhances rule of law and deepens democratic governance in countries in the sub-region is critical, but even more critical is deepening the financial sector, given the important role financial development plays in portfolio equity flows as suggested by the findings. A range of measures and appropriate policy responses are therefore needed for countries that have to manage macroeconomic and financial stability risks to deepen the financial sector.

Most studies on private capital flows to SSA have focussed on foreign direct investment flows with no or scanty evidence on the drivers of portfolio equity flows. This study fills this gap in the literature.

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).

The purpose of this paper is to investigate the relationship between earnings management and chief executive officers’ (CEOs) compensation. Owing to the fact that earnings management does not have only opportunistic effects, but signaling effects, this study focuses on accruals quality to examine earnings management incentives. Thus, accruals quality is described against future cash flow. The empirical evidences suggest that a positive relationship between discretionary accruals and future cash flow provides predictive elements for earnings management, whereas a negative relationship between discretionary accruals and future cash implies to opportunistic elements for earnings management. Should there is no significant relationship between discretionary accruals and future cash flow, there will be no earnings management, and such a result suggests that incentives and managers’ performance in these firms differ.

The statistical population of this research consists of all listed companies on the Tehran Stock Exchange during 2009–2016. Panel data method is applied in order to estimate the research model.

Findings of the study show that there is no significant relationship between discretionary accruals and future cash flow in pharmaceutical and food industries, thus they have neither predictive nor opportunist earnings management, while the results evidence a negative significant relationship between discretionary accruals and future cash flow in machineries, automobile, mineral and chemical industries. Furthermore, it can be alleged that there is no significant difference between CEOs’ compensation in firms with opportunistic earnings management (OEM) and other types of earnings management. It shows that firms do not have appropriate plans for CEOs’ compensation. Moreover, the relationship between earnings management and stock return has been investigated in this study. We document that stock return is influenced by accruals quality and its components. In other words, stock return significantly differs in firms with OEM and firms without any kind of earnings management.

The authors’ findings provide contributions; for managers, it is noticeable that stock markets have sufficient comprehension about financial statements and the undertaken procedures on them, resulting in a higher return base on fair information. For investors and regulators, using the findings, may have deeper understanding to distinguish between industries that are recognized as opportunistic and non-opportunistic, which, in turn, results in better decision and regulation.

Previous studies have been mostly investigated OEM, while the current study examines both signaling and opportunistic aspects of earnings management.