A genetic algorithm for vehicle routing in logistic networks with practical constraints

We optimise a postal delivery problem with time and capacity constraints imposed on vehicles and nodes of the logistic network. Time constraints relate to the duration of routes, whereas capacity constraints concern technical characteristics of vehicles and postal operation outlets. We consider a method which can be applied to a brownfield scenario, in which capacities of outlets can be relaxed and prospective hubs identified. As a solution, we apply a genetic algorithm and test its properties both in small case studies and in a simulated problem instance of a larger (i.e. comparable with real-world instances) size. We show that the genetic operators we employ are capable of switching between solutions based on direct origin-to-destination routes and solutions based on transfer connections, depending on what is more beneficial in a given problem instance. Moreover, the algorithm correctly identifies cases in which volumes should be shipped directly, and those in which it is optimal to use transfer connections within a single problem instance, if an instance in question requires such a selection for optimality. The algorithm is thus suitable for determining hubs and satellite locations. All considerations presented in this paper are motivated by real-life problem instances experienced by the Polish Post, the largest postal service provider in Poland, in its daily plans of delivering postal packages, letters and pallets.

Forecasting currency covariances using machine learning tree-based algorithms with low and high prices

We combine machine learning tree-based algorithms with the usage of low and high prices and suggest a new approach to forecasting currency covariances. We apply three algorithms: Random Forest Regression, Gradient Boosting Regression Trees and Extreme Gradient Boosting with a tree learner. We conduct an empirical evaluation of this procedure on the three most heavily traded currency pairs in the Forex market: EUR/USD, USD/JPY and GBP/USD. The forecasts of covariances formulated on the three applied algorithms are predominantly more accurate than the Dynamic Conditional Correlation model based on closing prices. The results of the analyses indicate that the GBRT algorithm is the bestperforming method.

Substitution between production factors and intermediate inputs in the light of KLEMS growth accounting for Poland

The generally adopted view is that the gross-output-based MFP is the most correct in terms of methodology, and the value-added-based MFP is its imperfect substitute performed when some data are missing. In this paper, however, performing both of them and comparing their results is proposed as a valuable means to studying the development of outsourcing in the economy. The paper presents the elaboration of the methodology for the latter, which is its main contribution to the field. The case of the Polish economy is used as an applicative example (covering the period between 2005 and 2016), as KLEMS growth accounting has recently been implemented in Poland. The results demonstrate that around the year 2011, the expansion of outsourcing ceased. Since outsourcing was one of the main processes of the Polish transition, this observation can be considered as an indication of the maturing of the market economy in Poland. Moreover, KLEMS growth accounting makes it possible to study this issue through NACE activities, i.e. at the industry level. It shows that manufacturing (section C of NACE) is predominantly responsible for the situation described above, which is the main empirical finding of the study. The dominant role of manufacturing is also confirmed by some other sectoral observations of lesser importance. The methodology developed in this paper can potentially be applied to other countries for which both kinds of MFP are performed.

Estimation of Value-at-Risk using Weibull distribution – portfolio analysis on the precious metals market

In this paper, we present a modification of the Weibull distribution for the Value-at- Risk (VaR) estimation of investment portfolios on the precious metals market. The reason for using the Weibull distribution is the similarity of its shape to that of empirical distributions of metals returns. These distributions are unimodal, leptokurtic and have heavy tails. A portfolio analysis is carried out based on daily log-returns of four precious metals quoted on the London Metal Exchange: gold, silver, platinum and palladium. The estimates of VaR calculated using GARCH-type models with non-classical error distributions are compared with the empirical estimates. The preliminary analysis proves that using conditional models based on the modified Weibull distribution to forecast values of VaR is fully justified.

How the shadow economy can be detected in National Accounts

The paper examines how indicators of the shadow economy correspond to the National Accounts values. More precisely, we focus on household accounts assuming that the shadow economy should be visible in the difference between household income and consumption, as household (disposable) income is grossly underreported. Household consumption seems therefore to be a more accurate indicator in this context, as most shadow economy income is eventually spent on consumption. This implies that household savings figures should be negatively related to the values of the shadow economy; consequently, if the values relating to the shadow economy are high, savings should be low, or even negative, and vice versa. We verify this hypothesis using European cross-country data covering the years 1991–2017 with the application of MIMIC model calculations as a point of reference. The estimation results lend very little support to the hypothesis assuming that the shadow economy depresses household savings, even though we can otherwise explain comparatively well the cross-country variation in household savings and consumption growth rates.

Predicting in multivariate incomplete time series. Application of the expectation-maximisation algorithm supplemented by the Newton-Raphson method

Statistical practice requires various imperfections resulting from the nature of data to be addressed. Data containing different types of measurement errors and irregularities, such as missing observations, have to be modelled. The study presented in the paper concerns the application of the expectation-maximisation (EM) algorithm to calculate maximum likelihood estimates, using an autoregressive model as an example. The model allows describing a process observed only through measurements with certain level of precision and through more than one data series. The studied series are affected by a measurement error and interrupted in some time periods, which causes the information for parameters estimation and later for prediction to be less precise. The presented technique aims to compensate for missing data in time series. The missing data appear in the form of breaks in the source of the signal. The adjustment has been performed by the EM algorithm to a hybrid version, supplemented by the Newton-Raphson method. This technique allows the estimation of more complex models. The formulation of the substantive model of an autoregressive process affected by noise is outlined, as well as the adjustment introduced to overcome the issue of missing data. The extended version of the algorithm has been verified using sampled data from a model serving as an example for the examined process. The verification demonstrated that the joint EM and Newton-Raphson algorithms converged with a relatively small number of iterations and resulted in the restoration of the information lost due to missing data, providing more accurate predictions than the original algorithm. The study also features an example of the application of the supplemented algorithm to some empirical data (in the calculation of a forecasted demand for newspapers).

The influence of the demographic structure on the economic growth of Ukraine

The national economy is closely related to the demographic structure of the society. Therefore, in the face of demographic changes, it is necessary to assess the influence of these changes on economic growth. This article presents an estimation of the impact that the future changes in the demographic structure will have on the economic growth of Ukraine, represented by the rate of changes in GDP per capita. The decomposition of GDP per capita and making the components of this decomposition dependent on the demographic structure allowed an empirical analysis, which used a variety of econometric and statistical techniques and was based on a population forecast prepared by the Ptoukha Institute for Demography and Social Studies of the National Academy of Sciences of Ukraine. As a result, it was determined that the impact of the changes in the demographic structure on Ukraine’s long-term economic growth will be highly diverse over the studied period (until 2060). However, considering the entire period of the analysis, the negative effects of the changes in the demographic structure on the economy will be counterbalanced by the positive effects of these changes.

The paradigm of statistical inference and the paradigm of statistical learning

The starting point for the presentation of the similarities and differences between the principles of conducting statistical research according to the rules of both statistical inference and statistical learning is the paradigm theory, formulated by Thomas Kuhn. In the first section of this paper, the essential features of the statistical inference paradigm are characterised, with particular attention devoted to its limitations in contemporary statistical research. Subsequently, the article presents the challenges faced by this research jointly with the expanding opportunities for their effective reduction. The essence of learning from data is discussed and the principles of statistical learning are defined. Moreover, significant features of the statistical learning paradigm are formulated in the context of the differences between the statistical inference paradigm and the statistical learning paradigm. It is emphasised that the statistical learning paradigm, as the more universal one of the two discussed, broadens the possibilities of conducting statistical research, especially in socio-economic sciences.

Does the slope of the yield curve of the interbank market influence prices on the Warsaw Stock Exchange? A sectoral perspective

The interest rate curve is often viewed as the leading indicator of economic prosperity in a broad sense. This paper studies the ability of the slope of the yield curve in the term structure of interest rates to impact the sectoral indices on the Warsaw Stock Exchange, using daily data covering the period from 1 January 2001 to 30 September 2020. The results of the research indicate an ambiguous dependence of the logarithmic rates of return of sub-indices on the change of the interbank interest rate curve. The only sectors showing a clear relationship of this type is energy and pharmaceuticals.

The effect of financial, macroeconomic and sentimental factors on stock market volatility

The aim of this paper is to find economic factors that could be helpful in explaining the market’s shifts between periods of prosperity and crisis. The study took into account the main stock indices from developed markets of the USA, Germany and Great Britain, and from two emerging markets, i.e. Poland and Turkey. The analysis confirms the existence of two different states of volatility in these markets, namely the state with a positive returns’ mean and low volatility, and the state with a negative or insignificant mean and high volatility. The Markov-switching model with a dynamic probability matrix was applied in the study. The subject of the analysis was the impact of domestic and global factors, such as VIX and TED spread, oil prices, sentiment indices (ZEW), and macroeconomic indices (unemployment, longterm interest rate, CPI), on the probability of switching between the states. The authors concluded that in all the examined countries, changes in long-term interest rates have an influence on market returns. However, the direction of this impact is different for developed and emerging markets. As regards developed markets, high prices of oil, 10-year bonds, and the ZEW index can suggest a high probability of the countries remaining in the first state, whereas an increase in the VIX index and the TED spread significantly reduces the probability of staying in this state. The other studied factors proved to be rather local in nature.