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In the last two decades there has been considerable progress made in the development of alternative estimation techniques to ordinary least squares (OLS) regression. The search for alternative estimators has no doubt been motivated by the observance of erratic OLS estimator behavior in cases where there are too few observations, multicollinearity problems, or simply “information‐poor” data sets. Imprecise and unreliable OLS coefficient estimates have been the result.

The purpose of this paper is to propose a new and improved version of arbitrage pricing theory (APT), namely, downside APT (D-APT) using the concepts of factors’ downside beta and semi-variance.

This study includes 163 stocks traded on the Malaysian stock market and uses eight macroeconomic variables as the dependent and independent variables to investigate the relationship between the adjusted returns and the downside factors’ betas over the whole period 1990-2010, and sub-periods 1990-1998 and 1999-2010. It proposes a new version of the APT, namely, the D-APT to replace two deficient measures of factor's beta and variance with more efficient measures of factors’ downside betas and semi-variance to improve and dispel the APT deficiency.

The paper finds that the pricing restrictions of the D-APT, in the context of an unrestricted linear factor model, cannot be rejected over the sample period. This means that all of the identified factors are able to price stock returns in the D-APT model. The robustness control model supports the results reported for the D-APT as well. In addition, all of the empirical tests provide support the D-APT as a new asset pricing model, especially during a crisis.

It may be worthwhile explaining the autocorrelation limitation between variables when applying the D-APT.

The framework can be useful to investors, portfolio managers, and economists in predicting expected stock returns driven by macroeconomic and financial variables. Moreover, the results are important to corporate managers who undertake the cost of capital computations, fund managers who make investment decisions and, investors who assess the performance of managed funds.

This paper is the first study to apply the concepts of semi-variance and downside beta in the conventional APT model to propose a new model, namely, the D-APT.

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.

The purpose of this paper is to investigate the justification of objective assessment of the agricultural sector energetic potential, and the increasing of the accuracy of assessments results of energy resources of plant by-products.

The study of the problems of bioenergetic potential assessment in the study is carried out in the following order: first, the potential is assessed based upon the 2005-2017 year’s observation data; second, the energetic potential is assessed based upon linear and nonlinear optimization model; and finally, the assessment of the bioenergetic potential predicted values is carried out under the condition of the current pace of development of agricultural business by 2035.

The findings show that the solving of optimization tasks enabled us to make a comparison of the real structure of agricultural production and to justify the optimal structure of the cultivated areas under the conditions of agricultural business profit maximization with due allowance for both main and additional energy products. Using the linear trend model the predicted value of the agricultural sector energetic potential by the year 2035 is obtained. However, it is far more likely that the domestic bioenergetics will take a slower pace of development and to satisfy its own energy demands.

Based on the data of the reference interval of 2005-2018, the predicted values of biomass for 2035 were obtained in the amount of 28 million tons of oil equivalent, which taking into account the indices of generation efficiency, is sufficient to produce 104 billion kW-h.

The use of biomass for energy generation can impact the local environment, for example, by affecting air quality, biodiversity, habitats and ecosystems and water quantity and quality and by changing the local use of land. Social impacts also may arise, notably by affecting local community livelihoods (for example, access to and use of land and resources), food security and economic parameters such as employment and poverty.

The paper presents for the first time the results of the empiric analysis of the Ukrainian sector bioenergetic potential formation that showed that even with respect to the losses during the energy generation, the agricultural production energetic potential will be enough to substitute nuclear national power engineering.

The simulation of magnetic fields with geometric discretization schemes using magnetic vector potentials involves the solution of very large discrete consistently singular curl‐curl systems of equations. Geometric and algebraic multigrid schemes for their solution require intergrid transfer operators of restriction and prolongation that achieve the discrete conservation of integral quantities serving as state‐variables of geometric discretization methods. For non‐conservative restriction operations, a consistency error correction operator related to an algebraic filtering is proposed. Numerical results show the effects of the consistency correction for a non‐nested geometric multigrid method.

The purpose of this paper is to report the implementation of an alternative time integration procedure for the dynamic non-linear analysis of structures.

The time integration algorithm discussed in this work corresponds to a spectral decomposition technique implemented in the time domain. As in the case of the modal decomposition in space, the numerical efficiency of the resulting integration scheme depends on the possibility of uncoupling the equations of motion. This is achieved by solving an eigenvalue problem in the time domain that only depends on the approximation basis being implemented. Complete sets of orthogonal Legendre polynomials are used to define the time approximation basis required by the model.

A classical example with known analytical solution is presented to validate the model, in linear and non-linear analysis. The efficiency of the numerical technique is assessed. Comparisons are made with the classical Newmark method applied to the solution of both linear and non-linear dynamics. The mixed time integration technique presents some interesting features making very attractive its application to the analysis of non-linear dynamic systems. It corresponds in essence to a modal decomposition technique implemented in the time domain. As in the case of the modal decomposition in space, the numerical efficiency of the resulting integration scheme depends on the possibility of uncoupling the equations of motion.

One of the main advantages of this technique is the possibility of considering relatively large time step increments which enhances the computational efficiency of the numerical procedure. Due to its characteristics, this method is well suited to parallel processing, one of the features that have to be conveniently explored in the near future.

Most demand studies have concentrated on the estimation of expenditure elasticities for single commodity at a time thereby not being able to reveal the details of the relationships among various food items demanded by households. The purpose of this paper is to simultaneously estimate the demand equations for a number of food items and to estimate cross-price elasticities which are necessary for studying consumer behaviours, marketing, production planning and policy making.

Relevant data were collected from 320 randomly selected households in a multistage sampling procedure. The normalized data were analysed in a system of equation with symmetry, adding-up and homogeneity restrictions imposed on the model.

Expenditure elasticities show that gaari and palm oil were inferior food items while others could be classified as normal. Own-price elasticities showed that beans, plantain, yam flour and rice were luxuries while others were necessities. Cross-price elasticities revealed that some were substitutes of one another while others were compliments and some were not related.

The data were collected using a month recall approach and generalizing its findings beyond such months of a year may be misleading. Therefore, other researchers should repeat the study across months and locations.

The study recommended that food policies should be broad based to encompass majority of the food items consumed in the study area given the intrinsic relationship inherent among them as their demands were interrelated and consumer behaviours as revealed by various elasticities be considered in formulating food-related policies.

The paper emphasized the need to model food demand in a system of equations as against single equation modelling.

The purpose of this paper is to present an algorithm of real estate mass appraisal in which the impact of attributes (real estate features) is estimated by inequality restricted least squares (IRLS) model.

This paper presents the algorithm of real estate mass appraisal, which was also presented in the form of an econometric model. Vital problem related to econometric models of mass appraisal is multicollinearity. In this paper, a priori knowledge about parameters is used by imposing restrictions in the form of inequalities. IRLS model is therefore used to limit negative consequences of multicollinearity. In ordinary least squares (OLS) models, estimator variances might be inflated by multicollinearity, which could lead to wrong signs of estimates. In IRLS models, estimators efficiency is higher (estimator variances are lower), which could result in better appraisals.

The final effect of the analysis is a vector of the impact of real estate attributes on their value in the mass appraisal algorithm. After making expert corrections, the algorithm was used to evaluate 318 properties from the test set. Valuation errors were also discussed.

Restrictions in the form of inequalities were imposed on the parameters of the econometric model, ensuring the non-negativity and monotonicity of real estate attribute impact. In case of real estate, variables are usually correlated. OLS estimators are then inflated and inefficient. Imposing restrictions in form of inequalities could improve results because IRLS estimators are more efficient. In the case of results inconsistent with theoretical assumptions, the real estate mass appraisal algorithm enables having the obtained results adjusted by an expert. This can be important for low quality databases, which is often the case in underdeveloped real estate markets. Another reason for expert correction may be the low efficiency of a given real estate market.

In the absence of any theoretical guidance, a solution to the question of what is the appropriate functional form for an import demand model can only be found empirically. Examines this question in the context of UK motor vehicle imports by applying a range of tests of functional form to two, alternatively specified, import demand models: the “traditional” price‐income model incorporating the popular but restrictive partial adjustment mechanism and a cost‐expenditure model that employs a less restrictive lag structure. Finds, principally that the commonly imposed linear or log‐linear functional forms cannot be rejected in relation to the price‐income specification, but there is some evidence that neither functional form may be appropriate in relation to the theoretically sounder cost‐expenditure model of import demand.

Standard market risk optimization tools, based on assumptions of normality, are ineffective for evaluating credit risk. In this article, the authors develop three scenario optimization models for portfolio credit risk. They first create the trading risk profile and find the best hedge position for a single asset or obligor. The second model adjusts all positions simultaneously to minimize the regret of the portfolio subject to general linear restrictions. Finally, a credit risk‐return efficient frontier is constructed using parametric programming. While scenario optimization of quantile‐based credit risk measures leads to problems that are not generally tractable, regret is a relevant and tractable measure that can be optimized using linear programming. The three models are applied to optimizing the risk‐return profile of a portfolio of emerging market bonds.