We examine incentives and strategic behavior in a voting game using a new modification of the Borda count in which the score of the juror with the largest deviation from the mean score vector is excluded. We show that introducing juror exclusion has a strong effect on incentives. In particular, it motivates jurors to align with the mean. When jurors' preferences are closely aligned - that is, when the subjective component of the evaluation is small relative to the objective one - excluding the outlier's score is likely to reduce manipulation. However, when jurors' preferences differ significantly, the method may actually increase misreporting compared to the standard Borda count without juror exclusion.

In this paper, we extend the concept of a matrix angular central Gaussian (MACG) distribution to the complex domain. First, we consider a normally distributed random complex matrix and demonstrate that its orientation exhibits a complex MACG (CMACG) distribution. We then discuss the distribution of the orientation of the linear transformation of the random matrix, the orientation of which has a CMACG distribution. Finally, we examine the family of distributions that leads to the CMACG distribution.

This study examines the interplay between monetary policy and the deployment of renewable electricity in Europe, addressing gaps in the existing literature. Against the backdrop of escalating greenhouse gas emissions, the paper examines the impact of monetary policy on the development of green energy infrastructure. By examining various determinants influencing the deployment of renewable electricity, the study identifies a novel area, the relationship between monetary policy and the evolving energy landscape characterised by increased private sector involvement and a shift from consumers to 'prosumers'. Using a pan-European approach from 2008 to 2022, the research poses two key questions: (1) Are interest rate movements associated with variations in renewable electricity deployment across different forms of renewable electricity generation? (2) Does the level of private sector involvement contribute to heterogeneity in the impact of monetary policy? The study uses rigorous panel analysis to unravel these dynamics, providing insight into the critical factors shaping the future trajectory of green energy in Europe. This research contributes to understanding the nuanced drivers of renewable electricity deployment and informs policymakers, researchers, and stakeholders working towards a sustainable energy transition.

An initial procedure in text-as-data applications is text preprocessing. One of the typical steps, which can substantially facilitate computations, consists in removing infrequent terms believed to provide limited information about the corpus. Despite the popularity of vocabulary pruning, there are not many guidelines on how to implement it in the literature. The aim of the paper is to fill this gap by examining the effects of removing infrequent terms for the quality of topics estimated using latent Dirichlet allocation (LDA). The analysis is based on Monte Carlo experiments taking into account different criteria for infrequent term removal and various evaluation metrics. The results indicate that pruning is often beneficial and that the share of vocabulary that might be eliminated can be quite considerable.

This study investigates the effectiveness of machine learning models in forecasting construction indicators derived from Business Tendency Survey data. Specifically, we compare the performance of traditional statistical models such as the autoregressive integrated moving average (ARIMA) with long short-term memory (LSTM) networks and hybrid approaches combining both. Using a range of economic variables -- including sector and economic evaluations, production, financial situation, investments, and sentiment indicator (IRGBUD) -- we evaluate model accuracy across testing dataset and rolling forecast strategy to assess consistency over time. Results demonstrate that while LSTM networks capture non-linear dependencies and temporal patterns, ARIMA-based models consistently outperforms LSTM in scenarios involving seasonal and cyclical structures. The findings highlight that the choice of model should align with the nature of the time series, particularly in relation to seasonality, volatility, and trend dynamics. This work offers practical implications for improving economic forecasting with machine learning in survey-based environments.

The measurement of the labor share is subject to a bias due to the income of self-employed (the mixed income). The aim of this study is to address this bias using a number of assumptions. We present our results for Poland, where both the self-employed and mixed income have high shares in employment and value added, respectively. In our calculations we account for the differences in socio-demographic characteristics of populations of both employees and self-employees and the differences in tax rates. The latter is our original contribution to the issue of measurement of the labor share. We show that in Poland, the correction of raw labor share with a fraction of mixed income increases labor share by an average of 8-13%, with the most reliable estimates being in a lower part of the range. Moreover, the treatment of the self-employed in agriculture is important. We also show that the corrections applied amplify the upward tendency in the labor share, observed since 2012. There are differences in sectoral labor shares - downward tendencies of labor shares dominate in manufacturing and other industries, whereas upward tendencies dominate in services. The shift-share decomposition revealed that the changing structure of the economy was roughly neutralized by the reallocation effect and the economy-wide movements are generated mostly by the within sector changes of labor shares.

The monetary policy decision process is fundamental to achieving price stability and sustainable economic growth over the medium term. In recent years, given the multitude of unprecedented shocks that hit the global economy, many complex models still need to estimate accurately the magnitude of these shocks and their impact on economic activity. This paper proposes a small quarterly projection model to assess the economic outlook for a selected group of Central Eastern European countries with similar economic characteristics. Therefore, we present a comparable analysis between Hungary, Poland and Romania for 2005Q4-2023Q4. Our results suggest a relatively good performance regarding the assessment of the economic downturn from 2020 and a similar recovery pattern in Hungary and Poland, while the recovery in Romania was only partial. A real-time forecasting exercise ensures that the trajectory of macroeconomic indicators is close to the actual data, especially during regular times.

In modern statistics and its applications, in particular in econometrics, random parameters or latent variables are widely used, and their estimation or prediction is of interest. Under some prior assumptions, Bayes formula can be used to obtain their posterior distribution. However, on the sampling-theory grounds, the unknown constants appearing in the prior distribution are estimated using the data being actually modelled. We call such approaches quasi-Bayesian; empirical Bayes procedures give important examples. In this paper we propose theoretical framework that enables Bayesian validation (or interpretation) of quasi-Bayesian inference techniques. Our framework amounts to establishing a formal Bayesian model that justifies the quasi-Bayesian "posterior" as a valid posterior distribution. From the Bayesian model validating the quasi-posterior, i.e. from the joint distribution of observations and other quantities, one can deduce the true sampling model, that is the conditional distribution of observations, and the true prior (or marginal) distribution of the remaining quantities. We illustrate our approach not only by simple examples, but also by the complicated Bayesian model validating one of the basic empirical Bayes estimators of the multivariate normal mean. This model is in fact a non-standard joint measure that separates two subsets of the Cartesian product of the observation space and the parameter space.

We propose a framework identifying sources of markup changes. Our approach derives from the conjectural variation theory and highlights the role of both price elasticity and concentration in shaping the markups. We show that a decline in markups in Poland, showed by by Gradzewicz and Mućk (2024), is related to rising price elasticity of demand, while rising concentration mitigated this effect. Moreover, we document that the globalization trends, e.g. international fragmentation, increasing standardization and tighter integration with global economy, affect both price elasticity of demand and markups. We also identify factors specific to demand elasticity (product varieties and a home bias) and to markups (import content of exports).