Central European Journal of Economic Modelling and Econometrics

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.

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.