Research background: On 11 March 2020, the Covid-19 epidemic was identified by the World Health Organization (WHO) as a global pandemic. The rapid increase in the scale of the epidemic has led to the introduction of non-pharmaceutical countermeasures. Forecast of the Covid-19 prevalence is an essential element in the actions undertaken by authorities. Purpose of the article: The article aims to assess the usefulness of the Auto-regressive Integrated Moving Average (ARIMA) model for predicting the dynamics of Covid-19 incidence at different stages of the epidemic, from the first phase of growth, to the maximum daily incidence, until the phase of the epidemic's extinction. Methods: ARIMA(p,d,q) models are used to predict the dynamics of virus distribution in many diseases. Model estimates, forecasts, and the accuracy of forecasts are presented in this paper. Findings & Value added: Using the ARIMA(1,2,0) model for forecasting the dynamics of Covid-19 cases in each stage of the epidemic is a way of evaluating the implemented non-pharmaceutical countermeasures on the dynamics of the epidemic.

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.

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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.

Purpose: The aim of this article is to evaluate the impact of the lockdown caused by the Covid-19 pandemic on the economic situation measured by the number of new passenger cars registered in selected European countries. Design/Methodology/Approach: The assessment of economic changes was conducted using a business cycle clock (BCC) using the number of newly registered passenger cars. Use working day and seasonally adjusted -Car registration, a new passenger car monthly series, was estimated with a Hodrick-Prescott filter. Findings: The lockdown caused by the Covid-19 pandemic had a negative impact on the economy measured by newly registered cars. Practical Implications: The varying effects of the lockdown can be evaluated with a business cycle clock. Originality/Value: The study was based on monthly data up to October 2020 and showed the high usefulness of BCC results.