This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in their complexity and economic content. Our key finding is that considerable reductions in mean-squared prediction error relative to a random walk benchmark can be achieved in real time for forecast horizons of up to two years. A particularly promising model is a six-variable Bayesian vector autoregressive model that includes the fundamental determinants of the supply and demand for natural gas. To capture real-time data constraints of these and other predictor variables, we assemble a rich database of historical vintages from multiple sources. We also compare our model-based forecasts to readily available model-free forecasts provided by experts and futures markets. Given that no single forecasting method dominates all others, we explore the usefulness of pooling forecasts and find that combining forecasts from individual models selected in real time based on their most recent performance delivers the most accurate forecasts.

The European Commission’s growth forecasts play a crucial role in shaping policies and provide a benchmark for many (national) forecasters. The annual forecasts are built on quarterly estimates, which do not receive much attention and are hardly known. Therefore, this paper provides a comprehensive analysis of multi-period ahead quarterly GDP growth forecasts for the European Union (EU), euro area, and several EU member states with respect to first-release and current-release data. Forecast revisions and forecast errors are analyzed, and the results show that the forecasts are not systematically biased. However, GDP forecasts for several member states tend to be overestimated at short-time horizons. Furthermore, the final forecast revision in the current quarter is generally downward biased for almost all countries. Overall, the differences in mean forecast errors are minor when using real-time data or pseudo-real-time data and these differences do not significantly impact the overall assessment of the forecasts’ quality. Additionally, the forecast performance varies across countries, with smaller countries and Central and Eastern European countries (CEECs) experiencing larger forecast errors. The paper provides evidence that there is still potential for improvement in forecasting techniques both for nowcasts but also forecasts up to eight quarters ahead. In the latter case, the performance of the mean forecast tends to be superior for many countries.

We estimate potential regional industrial effects in case of a threatening gas deficit. For Germany, the reduction leads to a potential decrease in industrial value added by 1.6 %. The heterogeneity across German states is remarkable, ranging from 2.2 % for Rhineland-Palatinate to 0.7 % for Hamburg. We emphasize the need for regional input-output tables to conduct economic analysis on a sub-national level, particularly when regional industrial structures are heterogeneous. The approximation with national figures can lead to results that differ both in magnitude and relative regional exposure. Our findings highlight that more accurate policy guidance can be achieved by improving the regional database.

This paper investigates how state ownership affects financial reporting practices in China. Using several measures of state (government) ownership, we show that a one-standard-deviation increase in state ownership decreases financial statement comparability by 36.61%, and the impact is more pronounced when the central authority has majority control of the company. Moreover, lower earnings quality and lower levels of accounting conservatism among state-owned enterprises (SOEs) may explain the lower accounting comparability between SOEs and non-SOEs (NSOEs). Additionally, similar (different) managerial objectives converge (diverge) financial statement comparability between SOEs and NSOEs. Last, the geographical locations of firms also contribute to financial statement comparability. We employ a difference-in-differences design, changes regression and entropy balancing to mitigate potential endogeneity bias.