Integrated reports combine financial and non-financial data into a comprehensive report outlining the company’s value creation process. Our objective is to find the completeness of disclosures, which is a crucial aspect of an integrated report’s quality. This study contributes to the integrated reporting examination by identifying quantitative and qualitative gaps when applying Integrated Reporting standards, focusing on the energy sector. We conducted the study on 57 published integrated reports of listed companies in Poland. The content of each report was examined for 49 features divided into eight areas. We identify the strengths and weaknesses of current reporting performance and the impact of the company’s sector on reports’ quality. We noted that there are significant differences among the areas. The major problems concern implementing IIRC’s framework on the connections between the business model and the organization’s strategy, risks, opportunities, and performance. Our research also noted that the level of specific disclosures might be related to a company’s ownership structure. We investigated the significance of differences among companies from the energy and non-energy sectors using statistical methods. As a result of the study, we obtained that disclosures’ completeness depends on the operation sector. The companies in the energy sector publish higher-quality integrated reports than companies in the other sectors.

On 11 March 2020, the WHO declared the COVID-19 epidemic to be a global pandemic. This was a consequence of the rapid increase in the number of people with positive test results, the increase in deaths due to COVID-19, and the lack of pharmaceutical drugs. Governments introduced national lockdowns, which have impacted both energy consumption and economies. The purpose of this paper is to answer the following question: do COVID-19 lockdowns affect the business cycle? We used the cycle clock approach to assess the magnitude of decrease in electricity consumption in the three waves of the epidemic, namely, April 2020, November 2021, and April 2021. Additionally, we checked the relation between energy consumption and GDP by means of spectral analysis. Results for selected 28 European countries confirm an impact of the introduced non-pharmaceutical interventions on both energy consumption and business cycle. The reduction of restrictions in subsequent pandemic waves increased electricity consumption, which suggests movement out of the economic recession.

We replicate the results in a narrow sense using the gretl and PcGive programs. In a wide sense, we extend the study of model uncertainty using the Bayesian averaging of classical estimates (BACE) approach to compare model reduction strategies. Allowing for the investigation of other specifications, we confirm the same set of significant determinants but find that Hendrys' model is not the most probable.

No abstract

No abstract

We propose to apply a time series-based nonlinear mechanism in the threshold autoregression form in order to examine the possible relationship between economic growth rate and its potential determinants included debt-to-GDP indicator. Our approach employs threshold variables instead of exogenous variables and time-series data instead of panel data to reveal the economic instruments that have determined the business cycle in European countries for the last 2 decades-starting from 1995. The purpose of the study is to reveal the mechanism of growth (measured in terms of GDP growth rate and industrial production growth rate) given different macroeconomic indicators, such as public debt, rate of inflation, interest rate, and rate of unemployment with the level of growth itself serving as the threshold variables. We identify that the monetary mechanism played an important role in diagnosing the phases of business cycle in most European economies which is in line with liberal economic policy dominating in the observed period. The initial level of debt-to-GDP ratio as its increase within the recession period was of no value for the economic growth pattern.

In this paper, we apply Bayesian averaging of classical estimates (BACE) and Bayesian model averaging (BMA) as an automatic modeling procedures for two well-known macroeconometric models: UK demand for narrow money and long-term inflation. Empirical results verify the correctness of BACE and BMA selection and exhibit similar or better forecasting performance compared with a non-pooling approach. As a benchmark, we use Autometrics—an algorithm for automatic model selection. Our study is implemented in the easy-to-use gretl packages, which support parallel processing, automates numerical calculations, and allows for efficient computations.

Tytuł dofinansowany ze środków Narodowego Banku Polskiego oraz ze środków Sekcji Klasyfikacji i Analizy Danych PTS Informacje o naborze artykułów i zasadach recenzowania znajdują się na stronie internetowej Wydawnictwa www.pracenaukowe.ue.wroc.pl www.wydawnictwo.ue.wroc.pl Publikacja udostępniona na licencji Creative Commons Uznanie autorstwa-Użycie niekomercyjne-Bez utworów zależnych 3.0 Polska (CC BY-NC-ND 3.0 PL)