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.

This paper investigates forecast aggregation via the random subspace regressions method (RSM) and explores the potential link between RSM and the Shapley value decomposition (SVD) using the US GDP growth rates. This technique combination enables handling high-dimensional data and reveals the relative importance of each individual forecast. First, it is possible to enhance forecasting performance in certain practical instances by randomly selecting smaller subsets of individual forecasts and obtaining a new set of predictions based on a regression-based weighting scheme. The optimal value of selected individual forecasts is also empirically studied. Then, a connection between RSM and SVD is proposed, enabling the examination of each individual forecast’s contribution to the final prediction, even when there is a large number of forecasts. This approach is model-agnostic (can be applied to any set of predictions) and facilitates understanding of how the aggregated prediction is obtained based on individual forecasts, which is crucial for decision-makers.

In this paper, we discuss how environmental damage and emission reduction policies affect the conduct of monetary policy in a two-sector (clean and dirty) dynamic stochastic general equilibrium model. In particular, we examine the optimal response of the interest rate to changes in sectoral inflation due to standard supply shocks, conditional on a given environmental policy. We then compare the performance of a nonstandard monetary rule with sectoral inflation targets to that of a standard Taylor rule. Our main results are as follows: first, the optimal monetary policy is affected by the existence of environmental policy (carbon taxation), as this introduces a distortion in the relative price level between the clean and dirty sectors. Second, compared with a standard Taylor rule targeting aggregate inflation, a monetary policy rule with asymmetric responses to sector-specific inflation allows for reduced volatility in the inflation gap, output gap, and emissions. Third, a nonstandard monetary policy rule allows for a higher level of welfare, so the two goals of welfare maximization and emission minimization can be aligned.

Bank lobbying has a bitter taste in most forums, ringing the bell of preferential treatment of big banks from governments and regulators. Using corporate loan facilities and hand-matched information on bank lobbying from 1999 to 2017, we show that lobbying banks increase their borrowers' overall performance. This positive effect is stronger for opaque and credit-constrained borrowers, when the lobbying lender possesses valuable information on the borrower, and for borrowers with strong corporate governance. Our findings are consistent with the theory positing that lobbying can provide access to valuable lender-borrower information, resulting in improved efficiency in large firms' corporate financing.

Does banking supervision affect borrowers’ transition to the carbon-neutral economy? We use a unique identification strategy that combines the French bank climate pilot exercise with borrowers’ carbon emissions to present two novel findings. First, climate stress tests actively facilitate borrowers’ transition to a low-carbon economy through a lending channel. Stress-tested banks increase loan volumes but simultaneously charge higher interest rates for brown borrowers. Second, additional lending is associated with some improvements in environmental performance. While borrowers commit more to reduce carbon emissions and are more likely to evaluate environmental effects of their projects, they neither reduce direct carbon emissions, nor terminate relationships with environmentally unfriendly suppliers. Our findings establish a causal link between bank climate stress tests and borrowers’ reductions in transition risk.