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Baltagi and Li (2001) derived Lagrangian multiplier tests to jointly test for functional form and spatial error correlation. This companion paper derives Lagrangian multiplier tests to jointly test for functional form and spatial lag dependence. In particular, this paper tests for linear or log-linear models with no spatial lag dependence against a more general Box-Cox model with spatial lag dependence. Conditional LM tests are also derived which test for (i) zero spatial lag dependence conditional on an unknown Box-Cox functional form, as well as, (ii) linear or log-linear functional form given spatial lag dependence. In addition, modified Rao-Score tests are also derived that guard against local misspecification. The performance of these tests are investigated using Monte Carlo experiments.

The purpose of this paper is to investigate the driving forces (economics and socio-demographic) that influence family Takaful demand in the Middle East and North Africa (MENA) region, using a sample of 15 countries from the MENA.

The authors use multivariate analysis, bootstrapping and generalised method of moments techniques. They first examine a full model that combines all variables; second, a model that controls for product market factors; and finally, a model that controls for socio-demographic factors. They further separate all models into linear and log-linear demand functions.

The authors demonstrate that the relationship between the demand for family Takaful in MENA and Islamic banking deposits, education, dependency rate, female life expectancy and Muslim population is significantly positive. On the other hand, the significant factors that are inversely related to the demand for family Takaful in MENA are inflation, financial development and male life expectancy.

The crucial limitation of this study is the amount of data available in regards to the dependent variable, family Takaful contributions. Consequently, to improve the understanding in explaining the family Takaful demand in MENA, further research can take advantage of expanding the variables that were omitted in this research as a consequence of the unavailability of data. Some of the possible influential variables can include government social security expenditure, legal system and government policies, price of Takaful and level of competition within the Takaful and insurance industry.

It is obvious that there are very few studies that focus on the MENA market, and indeed, none of them gives attention to the factors that influence demand for family Takaful. While this study is expected to provide more understanding and awareness on the concept of Takaful and the factors that influence its demand, the authors hope that it would encourage more studies on various issues on the Takaful industry so as to help researchers to understand more aspects of this new emerging business.

The purpose of this paper is to provide the maintenance personnel with a methodology for modeling and estimating the reliability of critical machine systems using the historical data of their inter‐failure times.

The failure patterns of five different machine systems were modeled with NHPP‐log linear process and HPP belonging to stochastic point process for predicting their reliability in future time frames. Besides the classical approach, Bayesian approach was also used involving Jeffreys's invariant non‐informative independent priors to derive the posterior densities of the model parameters of NHPP‐LLP and HPP with a view to estimating the reliability of the machine systems in future time intervals.

For at least three machine systems, Bayesian approach gave lower reliability estimates and a larger number of (expected) failures than those obtained by the classical approach. Again, Bayesian estimates of the probability that “ROCOF (rate of occurrence of failures) would exceed its upper threshold limit” in future time frames were uniformly higher for these machine systems than those obtained with the classical approach.

This study indicated that, the Bayesian approach would give more realistic estimates of reliability (in future time frames) of the machine systems, which had dependent inter‐failure times. Such information would be helpful to the maintenance team for deciding on appropriate maintenance strategy.

With the help of Bayesian approach, the posterior densities of the model parameters were found analytically by considering Jeffreys's invariant non‐informative independent prior. The case study would serve to motivate the maintenance teams to model the failure patterns of the repairable systems making use of the historical data on inter‐failure times and estimating their reliability in future time frames.

This study aims to estimate the quality-adjusted changes in residential mobile consumer prices by controlling for the changes in the relevant service characteristics and quality, in a case study on Hungary between 2015 and 2021; compare the results with changes measured by the traditionally calculated official telecommunications price index of the Statistical Office; and discuss separating the hedonic price changes from the effect of a specific government intervention that occurred in Hungary, namely, the significant reduction in the value added tax rate (VAT) levied on internet services.

Since the price of commercial mobile offers does not directly reflect the continuous improvements in service characteristics and functionalities over time, the price changes need to be adjusted for changes in quality. The authors use hedonic regression analysis to address this issue.

The results show significant hedonic price changes over the observed seven-year period of over 30%, which turns out to be primarily driven by the significant developments in the comprising service characteristics and not the VAT policy change.

This paper contributes to the literature on hedonic price analyses on complex telecommunications service plans and enhances this methodology by using weights and analysing the content-related features of the mobile packages.

The purpose of the chapter is to test the hypothesis that food safety (chemical) standards act as barriers to international seafood imports. We use zero-accounting gravity models to test the hypothesis that food safety (chemical) standards act as barriers to international seafood imports. The chemical standards on which we focus include chloramphenicol required performance limit, oxytetracycline maximum residue limit, fluoro-quinolones maximum residue limit, and dichlorodiphenyltrichloroethane (DDT) pesticide residue limit. The study focuses on the three most important seafood markets: the European Union’s 15 members, Japan, and North America.Our empirical results confirm the hypothesis and are robust to the OLS as well as alternative zero-accounting gravity models such as the Heckman estimation and the Poisson family regressions. For the choice of the best model specification to account for zero trade and heteroskedastic issues, it is inconclusive to base on formal statistical tests; however, the Heckman sample selection and zero-inflated negative binomial (ZINB) models provide the most reliable parameter estimates based on the statistical tests, magnitude of coefficients, economic implications, and the literature findings. Our findings suggest that continually tightening of seafood safety standards has had a negative impact on exporting countries. Increasing the stringency of regulations by reducing analytical limits or maximum residue limits in seafood in developed countries has negative impacts on their bilateral seafood imports. The chapter furthers the literature on food safety standards on international trade. We show competing gravity model specifications and provide additional evidence that no one gravity model is superior.

The purpose of this study is to account for a recent non-mainstream econometric approach using microdata and how it can inform research in business administration. More specifically, the paper draws from the applied microeconometric literature stances in favor of fitting Poisson regression with robust standard errors rather than the OLS linear regression of a log-transformed dependent variable. In addition, the authors point to the appropriate Stata coding and take into account the possibility of failing to check for the existence of the estimates – convergency issues – as well as being sensitive to numerical problems.

The author details the main issues with the log-linear model, drawing from the applied econometric literature in favor of estimating multiplicative models for non-count data. Then, he provides the Stata commands and illustrates the differences in the coefficient and standard errors between both OLS and Poisson models using the health expenditure dataset from the RAND Health Insurance Experiment (RHIE).

The results indicate that the use of Poisson pseudo maximum likelihood estimators yield better results that the log-linear model, as well as other alternative models, such as Tobit and two-part models.

The originality of this study lies in demonstrating an alternative microeconometric technique to deal with positive skewness of dependent variables.

The purpose of this paper is to validate and quantify the effect of key macroeconomic drivers on London house prices using annual data over the period 1983–2016.

Within this context, the authors estimate alternative error-correction and partial-adjustment models (PAMs), which have been widely used in the empirical literature in modelling the slow adjustments of house prices to demand and supply shocks.

The results verify the existence of a strong long-term relationship between London house prices and key macroeconomic variables, such as UK GDP, London population and housing completions. A key finding of the study relevant to the debate on the causes of the housing affordability crisis is that the results provide little evidence in support of the argument that user demand, which is captured in the author’s model by Greater London population, may have had a diminished role in driving house price inflation in London.

The practical and policy implications of the results are that increased homebuilding activity in London will undoubtedly help limit house price increases. Also, any potential reduction of immigration and economic growth due to Brexit will also have a similar effect.

The originality of this research lies in the use of annual data that may better capture the long-term effect of macroeconomic drivers on house prices and the estimation of such effects through both error-correction and partial-adjustment models.

The purpose of this paper is to analyze the impact of learning in a project‐driven organization and demonstrate analytically how the learning, which takes place during the execution of successive projects, and the forgetting that takes place during the dormant time between the project executions, can impact performance and productivity in the future.

A learn‐forget model was developed using the learning curve concept prevalent in many manufacturing processes. The model assumes that learning occurs while project tasks are being performed and forgetting takes place during dormant times between the successive implementations. The log‐linear model was adapted, with both learning and forgetting rates being a function of the doubling or tripling of output. Forgetting is inhibited through the use of knowledge transfer tools such as use of close‐out documents or content management platforms. The model is applied to a simulated project environment where a number of projects are executed sequentially, and the results are evaluated using the reduction in total duration and return on investment.

Computational results demonstrate that the learning and forgetting rates and level of project close‐out effort impact project performance, in the form of reduction in duration, much more significantly compared to the impact of the length of dormant times between the project initiations. Furthermore, even in a slow learning environment, using close‐out reports as a knowledge transfer tool, managers can achieve more than a 40 percent reduction in duration after several successive implementations.

Although the theoretical development is applicable to a general organizational setting, the empirical testing of the model is done in project‐driven organizations where projects are implemented on an ongoing basis.

Managers can significantly benefit from the findings of this study. It is shown that the accumulated learning which represents knowledge generated during the implementation of a project, if transferred successfully, improves productivity and enables faster implementation. In a project‐driven organization an almost 80 percent reduction in total duration is achievable with the use of close‐out documents. This result promotes the importance of the learning process and managers should enable their team members to learn as much as they can while implementing a task and to document it methodically.

This study constitutes an initial effort to illustrate quantitatively how the level of learning and forgetting impact performance in a project‐driven organization. This study is also original in that it methodically demonstrates the importance of spending time during the phase‐out, documenting the project artifacts, that enables knowledge transfer, and thus improves performance.

This paper reports on a study about applying regression techniques to identify the determinants and functional forms of tourism hotel expenditure in Hong Kong. Annual time series data from 1983 to 1997 of average room rate, the number of visitor arrivals, the service price index, and hotel accommodation rates were hypothesised to affect tourism hotel expenditure. Seven exogenous variables were selected for regression model development in both linear and log‐linear forms. In view of the potential problems of multicollinearity between the independent variables, and therefore the associated instability of the regression coefficients, stepwise regression analyses were employed to improve the initial model. Final empirical results showed that the hotel expenditure in Hong Kong could be explained by four of the seven exogenous variables. A log‐linear form of the regression model appeared to slightly outperform the linear form.