Time‐varying volatility and linear trends are common features of several macroeconomic time series. Recent articles have proposed panel unit root tests (PURTs) that are pivotal in the presence of volatility shifts, excluding linear trends, however. This article proposes a new PURT that works well for data that is both heteroskedastic and trending. Under the null hypothesis, the test statistic has a limiting Gaussian distribution. We derive the local asymptotic power to underpin the consistency of the test statistic. Simulation results reveal that the test performs well in small samples. As an empirical illustration, we examine the stationarity of energy use per capita in OECD economies. While the series are in general difference stationary, they could also be considered as trend stationary for specific time spans.

This research seeks to elucidate the relationship between economic activities, energy consumption, and CO₂ emissions, thereby contributing to a deeper understanding of the economic dimensions of climate change mitigation efforts within the European context, which may be useful for developing policies to mitigate CO₂ emissions and promote sustainable development. This study investigates world CO₂ emissions and their relation to population growth and finds a strong positive relation based on data from 1969 to 2023. The World Kaya Identity has been applied to understand how changes in the involved factors affect CO₂ emissions over time. When studying the more complex relation between the variables by controlling for energy use, GDP, and carbon intensity based on the Kaya Identity, the authors identified an overall long-term coupling of all factors. Considering short-term variations, population growth appears to have an insignificant effect, and carbon intensity appears most influential on CO₂ emissions. As a next step, we take a disaggregated view on different country settings, economic sectors, and energy sources to further analyze the role carbon intensity plays for increased CO₂ emissions. Here, we lay a special focus on the European perspective. This descriptive analysis lets us draw some general conclusions regarding strategies for reducing the negative impact of CO₂ emissions and political efforts for sustainability transformations. This study is important for the current state of science, since a clear economic assessment of the negative effects of carbon dioxide is necessary for planning measures and costs in the ecological sphere, the correct assessment of the impact on the health of the population, the prospective implementation of preventive measures at all levels, and financing measures to reduce the negative effects of carbon dioxide. The authors found a significant positive effect of GDPpc, energy intensity, and carbon intensity on impact and an insignificant effect on the population. Thus, an unexpected increase in the population likely does not have short-term effects on CO₂ emissions, and the responses to GDPpc and energy intensity both decrease after some periods, while the shock in carbon intensity shows a significant effect even after 10 years. This is reasonable in the sense that both increases in GDP and energy intensity might be alleviated by technological progress and, thus, only show a short-term positive effect on CO₂ emissions. The carbon intensity of energy consumption is more crucial for the long-term change of CO₂ emissions. For this reason, we study the decomposition of energy use in more detail by considering descriptive statistics over time and over different sectors and countries.

We study the links between financial uncertainty, economic activity, and both conventional and unconventional monetary policies. To disentangle the effects of conventional policies from unconventional ones, we introduce a new identification method that exploits non-Gaussian characteristics of the data and the presence of an external instrument. The inclusion of the external instrument in our methodological framework has the advantage to solve (to some extent) the shock labeling problem often associated with statistical identification schemes. We use this approach to identify and label the two types of monetary policy shocks in the Euro Area. We show it is relevant for central banks to consider financial uncertainty as endogenous to monetary policy, especially conventional, alongside other economic fundamentals. These results shed new light on the transmission and effectiveness of monetary policy on macroeconomic variables.

We develop inference for heterogeneous panel vector autoregressive (VAR) models and their structural impulse response functions, where the error terms are dependent in the cross-sectional and time dimensions (two-way dependence). For proxy-identified structural VARs, we first adapt mean-group estimation and construct a closed-form pooled identification. Considering the reduced-form VAR dynamics, residual covariances, and structural parameters, we derive a joint central limit theorem under joint limits in the cross-sectional and time dimensions. We then propose a recursive-design panel moving- block bootstrap that resamples the estimated error terms in (i) the temporal, (ii) the cross-sectional, or (iii) both dimensions jointly, and prove consistency of the joint panel- block scheme under two-way dependence. Simulations show coverage close to the nominal level for the joint scheme but severe undercoverage for cross-sectional resampling.