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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 purpose of this paper is to provide an extensive review of the monetary model of exchange rate determination which is the main theoretical framework on analyzing exchange rate behavior over the last 40 years. Furthermore, we test the flexible price monetarist variant and the sticky price Keynesian variant of the monetary model. We conduct our analysis employing a sample of 14 advanced economies using annual data spanning the period 1880–2012.

The theoretical background of the paper relies on the monetary model to the exchange rate determination. We provide a thorough econometric analysis using a battery of unit root and cointegration testing techniques. We test the price-flexible monetarist version and the sticky-price version of the model using annual data from 1880 to 2012 for a group of industrialized countries.

We provide strong evidence of the existence of a nonlinear relationship between exchange rates and fundamentals. Therefore, we model the time-varying nature of this relationship by allowing for Markov regime switches for the exchange rate regimes. Modeling exchange rates within this context can be motivated by the fact that the change in regime should be considered as a random event and not predictable. These results show that linearity is rejected in favor of an MS-VECM specification which forms statistically an adequate representation of the data. Two regimes are implied by the model; the one of the estimated regimes describes the monetary model whereas the other matches in most cases the constant coefficient model with wrong signs. Furthermore it is shown that depending on the nominal exchange rate regime in operation, the adjustment to the long run implied by the monetary model of the exchange rate determination came either from the exchange rate or from the monetary fundamentals. Moreover, based on a Regime Classification Measure, we showed that our chosen Markov-switching specification performed well in distinguishing between the two regimes for all cases. Finally, it is shown that fundamentals are not only significant within each regime but are also significant for the switches between the two regimes.

The results are of interest to practitioners and policy makers since understanding the evolution and determination of exchange rates is of crucial importance. Furthermore, our results are linked to forecasting performance of exchange rate models.

The present analysis extends previous analyses on exchange rate determination and it provides further support in favor of the monetary model as a long-run framework to understand the evolution of exchange rates.

The estimation of regression models subject to linear restrictions is a widely applied technique; however, aside from simple examples, the equivalence between the linear restricted case to the reparameterization and the substitution case is rarely employed. We believe this is due to the lack of a general transformation method for changing from the definition of restrictions in terms of the unrestricted parameters to the equivalent reparameterized model and conversely from the reparameterized model to the equivalent linear restrictions for the unrestricted model. In many cases, the reparameterization method is computationally more efficient especially when estimation involves an iterative method. But the linear restriction case allows a simple method for adding and removal of restrictions.

In this chapter, we derive a general relationship that allows the conversion between the two forms of the restricted models. Examples emphasizing systems of demand equations, polynomial lagged equations, and splines are given in which the transformation from one form to the other are demonstrated as well as the combination of both forms of restrictions. In addition, we demonstrate how an alternative Wald test of the restrictions can be constructed using an augmented version of the reparameterized model.

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.

In this paper, we study a partially linear dynamic panel data model with fixed effects, where either exogenous or endogenous variables or both enter the linear part, and the lagged-dependent variable together with some other exogenous variables enter the nonparametric part. Two types of estimation methods are proposed for the first-differenced model. One is composed of a semiparametric GMM estimator for the finite-dimensional parameter θ and a local polynomial estimator for the infinite-dimensional parameter m based on the empirical solutions to Fredholm integral equations of the second kind, and the other is a sieve IV estimate of the parametric and nonparametric components jointly. We study the asymptotic properties for these two types of estimates when the number of individuals N tends to ∞ and the time period T is fixed. We also propose a specification test for the linearity of the nonparametric component based on a weighted square distance between the parametric estimate under the linear restriction and the semiparametric estimate under the alternative. Monte Carlo simulations suggest that the proposed estimators and tests perform well in finite samples. We apply the model to study the relationship between intellectual property right (IPR) protection and economic growth, and find that IPR has a non-linear positive effect on the economic growth rate.

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.

This paper develops methods of Bayesian inference in a cointegrating panel data model. This model involves each cross-sectional unit having a vector error correction representation. It is flexible in the sense that different cross-sectional units can have different cointegration ranks and cointegration spaces. Furthermore, the parameters that characterize short-run dynamics and deterministic components are allowed to vary over cross-sectional units. In addition to a noninformative prior, we introduce an informative prior which allows for information about the likely location of the cointegration space and about the degree of similarity in coefficients in different cross-sectional units. A collapsed Gibbs sampling algorithm is developed which allows for efficient posterior inference. Our methods are illustrated using real and artificial data.

A concise introduction to the normalized quadratic expenditure or cost function is provided so that the interested reader will have the necessary information to understand and use this functional form. The normalized quadratic is an attractive functional form for use in empirical applications as correct curvature can be imposed in a parsimonious way without losing the desirable property of flexibility. We believe it is unique in this regard. Topics covered include the problem of cardinalizing utility, the modeling of nonhomothetic preferences, the use of spline functions to achieve greater flexibility, and the use of a “semiflexible” approach to make it feasible to estimate systems of equations with a large number of commodities.