We discuss econometric modeling with Prof. Aleksander Welfe from the University of Łódź and Warsaw School of Economics (SGH), Vice-President of the Polish Academy of Sciences.

This article presents the results of studies assesing the significance of the most important macro- and microeconomic factors affecting investors’ propensity to invest in mining. The Polish mining industry in recent years has seen intensive restructuring processes which have considerably affected the status of fixed assets required for the exploitation of useful minerals. In order to efectively manage technological progress in mining plants, it is necessary to understand the role of individual, variable factors influencing investors’ propensity tomake specific expenditures. In the analysis, mathematical statistics and econometric modelling methods were applied to determine the nature of correlations between the values studied and their significance. This examination applied statistical data accumulated by economic entities from 2000–2010. A linear econometric model waspresented illustrating the relationship between capital expenditure in mining and such indicators as fixed assetsvalue, GDP, real interest rate, consumption levels of fixed asset components in mining, and various other factors. Structural parameters of a function specifying the level of investment expenditure can be determined based on statistical data which has been appropriately processed so that the model constructed reflects the economic process studied in relevant way.

Such a model is not free of defects typical in statistical models; however, it simultaneously enables one toobtain valuable information concerning the impact of the factors studied on the value of such expenditure, and the theoretical possibilities to exchange the specific quantity of one factor for another factor. In the final version of the model, it is often sufficient to include only these independent variables which contribute the most essential information to the independent variable. This often simplifies the final form of the model without simultaneous limiting of its importance in explaining the economic phenomenon studied and the possibilities of its practical application. In the final selection of significant variables captured in the model, the method of information capacity indicators was used.

The spectacular surge of Poland's VAT revenues after 2015 prompted a discussion about the role of the tax administration in collecting tax liabilities. Unfortunately, the scarcity of the available data prevents empirical studies from reaching reliable conclusions about the determinants of VAT revenues. Given that, this article presents a wider attempt at identifying the determinants of VAT revenues in the EU Member States. Using panel cointegration methods, several working hypotheses linking VAT gap to income factors, the business cycle, tax carousels, and an effectiveness of the government are evaluated. The results of the research provide evidence that the VAT gap in the EU countries is under a strong influence from variables approximating changes in per capita incomes, the business cycle, and the openness of an economy to intra-EU trade. The latter finding is a sufficient indication that the improvements made to Poland's tax system were both legitimate and effective.

The studies using Mincer equations are generally applied to cross-sectional data at the micro-level. There are however limited studies conducted with macro or panel data for wage equations. Pseudo panel data methods can be applied to empirical studies by creating cohorts from repeated cross-sectional data in the absence of genuine panel data. Difference in both the human and labour resources according to the spatial positions may also affect the prediction of the wage equations. We aim to introduce the application of spatial pseudo panel models by creating cohorts according to the birth years of employees and regions in which they live from the Turkish household labour survey for the period 2010-2015. As a result, we find that the spatial autocorrelation model is appropriate for wage equations of Turkey. We also find that return of education on wages is 11% while return of experience on wages is 4%.

Management and Production Engineering Review (MPER) is a peer-refereed, international, multidisciplinary journal covering a broad spectrum of topics in production engineering and management. Production engineering is a currently developing stream of science encompassing planning, design, implementation and management of production and logistic systems. Orientation towards human resources factor differentiates production engineering from other technical disciplines. The journal aims to advance the theoretical and applied knowledge of this rapidly evolving field, with a special focus on production management, organisation of production processes, management of production knowledge, computer integrated management of production flow, enterprise effectiveness, maintainability and sustainable manufacturing, productivity and organisation, forecasting, modelling and simulation, decision making systems, project management, innovation management and technology transfer, quality engineering and safety at work, supply chain optimization and logistics. Management and Production Engineering Review is published under the auspices of the Polish Academy of Sciences Committee on Production Engineering and Polish Association for Production Management. The main purpose of Management and Production Engineering Review is to publish the results of cutting-edge research advancing the concepts, theories and implementation of novel solutions in modern manufacturing. Papers presenting original research results related to production engineering and management education are also welcomed. We welcome original papers written in English. The Journal also publishes technical briefs, discussions of previously published papers, book reviews, and editorials. Letters to the Editor-in-Chief are highly encouraged.

The first so-called hybrid MSV-MGARCH models were characterized by the conditional covariance matrix that was a product of a univariate latent process and a matrix with a simple MGARCH structure (Engle’s DCC or scalar BEKK). The aim was to parsimoniously describe volatility of a large group of assets. The proposed hybrid models, similarly as pure MSV specifications (and other models based on latent processes), required the Bayesian approach equipped with efficient MCMC simulation tools. The numerical effort has payed – the hybrid models seem particularly useful due to their good fit and ability to jointly cope with large portfolios. In particular, the simplest hybrid, now called the MSF-SBEKK model, has been successfully used in many applications. However, one latent process may be insufficient in the case of a highly heterogeneous portfolio. Thus, in this study we discuss a general hybrid MSV-MGARCH model structure, showing its basic characteristics that explain greater flexibility of such hybrid structure with respect to the corresponding MGARCH class. From the empirical perspective, we advocate the GMSF-SBEKK specification, which uses as many latent processes as there are relatively homogeneous groups of assets. We present full Bayesian inference for such models, with the use of an efficient MCMC simulation strategy. The approach is used to jointly model volatility on very different markets. Joint modelling is formally compared to individual modelling of volatility on each market.

We propose a method of constructing multisector-multiregion input-output tables, based on the standard multisector tables and the tools of spatial econometrics. Voivodship-level (NUTS-2) and subregion-level data (NUTS-3) on sectoral value added is used to fit a spatial model, based on a modification of the Durbin model. The structural coefficients are calibrated, based on I-O multipliers, while the spatial weight matrices are estimated as parsimoniously parametrised functions of physical distance and limited supply in certain regions. We incorporate additional restrictions to derive proportions in which every cross-sectoral flow should be interpolated into cross-regional flow matrix. All calculations are based on publicly available data. The method is illustrated with an example of regional economic impact assessment for a generic construction company located in Eastern Poland.

We develop a fully Bayesian framework for analysis and comparison of two competing approaches to modelling daily prices on different markets. The first approach, prevailing in financial econometrics, amounts to assuming that logarithms of prices behave like a multivariate random walk; this approach describes logarithmic returns most often by the VAR(1) model with MGARCH (or sometimes MSV) disturbances. In the second approach, considered here, it is assumed that daily price levels are linked together and, thus, the error correction term is added to the usual VAR(1)–MGARCH or VAR(1)–MSV model for logarithmic returns, leading to a reduced rank VAR(2) specification for logarithms of prices. The model proposed in the paper uses a hybrid MSV-MGARCH structure for VAR(2) disturbances. In order to keep cointegration modelling as simple as possible, we restrict to the case of two prices representing two different markets.

The aim of the paper is to show how to check if a long-run relationship between daily prices exists and whether taking it into account influences our inference on volatility and short-run relations between returns on different markets. In the empirical example the daily values of the S&P500 index and the WTI oil price in the period 19.12.2005 – 30.09.2011 are jointly modelled. It is shown that, although the logarithms of the values of S&P500 and WTI oil price seem to be cointegrated, neglecting the error correction term leads to practically the same conclusions on volatility and conditional correlation as keeping it in the model.

Often daily prices on different markets are not all observable. The question is whether we should exclude from modelling the days with prices not available on all markets (thus loosing some information and implicitly modifying the time axis) or somehow complete the missing (non-existing) prices. In order to compare the effects of each of two ways of dealing with partly available data, one should consider formal procedures of replacing the unavailable prices by their appropriate predictions. We propose a fully Bayesian approach, which amounts to obtaining the marginal posterior (or predictive) distribution for any particular day in question. This procedure takes into account uncertainty on missing prices and can be used to check validity of informal ways of ”completing” the data (e.g. linear interpolation). We use the MSF-SBEKK structure, the simplest among hybrid MSV-MGARCH models, which can parsimoniously describe volatility of a large number of prices or indices. In order to conduct Bayesian inference, the conditional posterior distributions for all unknown quantities are derived and the Gibbs sampler (with Metropolis-Hastings steps) is designed. Our approach is applied to daily prices from six different financial and commodity markets; the data cover the period from December 21, 2005 till September 30, 2011, so the time of the global financial crisis is included. We compare inferences (on individual parameters, conditional correlation coefficients and volatilities), obtained in the cases where incomplete observations are either deleted or forecasted.

The aim of this paper is to examine the empirical usefulness of two new MSF – Scalar BEKK(1,1) models of n-variate volatility. These models formally belong to the MSV class, but in fact are some hybrids of the simplest MGARCH and MSV specifications. Such hybrid structures have been proposed as feasible (yet non-trivial) tools for analyzing highly dimensional financial data (large n). This research shows Bayesian model comparison for two data sets with n = 2, since in bivariate cases we can obtain Bayes factors against many (even unparsimonious) MGARCH and MSV specifications. Also, for bivariate data, approximate posterior results (based on preliminary estimates of nuisance matrix parameters) are compared to the exact ones in both MSF-SBEKK models. Finally, approximate results are obtained for a large set of returns on equities (n = 34).

The s-period ahead Value-at-Risk (VaR) for a portfolio of dimension n is considered and its Bayesian analysis is discussed. The VaR assessment can be based either on the n-variate predictive distribution of future returns on individual assets, or on the univariate Bayesian model for the portfolio value (or the return on portfolio). In both cases Bayesian VaR takes into account parameter uncertainty and non-linear relationship between ordinary and logarithmic returns. In the case of a large portfolio, the applicability of the n-variate approach to Bayesian VaR depends on the form of the statistical model for asset prices. We use the n-variate type I MSF-SBEKK(1,1) volatility model proposed specially to cope with large n. We compare empirical results obtained using this multivariate approach and the much simpler univariate approach based on modelling volatility of the value of a given portfolio.