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Unsägliche Industriepolitik und ein übergriffiger StaatReint GroppThe Pioneer, 1. März 2025
We show an equivalence result in the standard representative agent New Keynesian model after demand, wage markup and correlated price markup and TFP shocks: assuming sticky prices and flexible wages yields identical allocations for GDP, consumption, labor, inflation and interest rates to the opposite case- flexible prices and sticky wages. This equivalence result arises if the price and wage Phillips curves' slopes are identical and generalizes to any pair of price and wage Phillips curve slopes such that their sum and product are identical. Nevertheless, the cyclical implications for profits and wages are substantially different. We discuss how the equivalence breaks when these factor-distributional implications matter for aggregate allocations, e.g. in New Keynesian models with heterogeneous agents, endogenous firm entry, and non-constant returns to scale in production. Lastly, we point to an econometric identification problem raised by our equivalence result and discuss possible solutions thereof.
This study employs synthetic control methods to estimate the effect of the Iberian exception mechanism on wholesale electricity prices and consumer inflation, for both Spain and Portugal. We find that the intervention led to an average reduction of approximately 40% in the spot price of electricity between July 2022 and June 2023 in both Spain and Portugal. Regarding overall inflation, we observe notable differences between the two countries. In Spain, the intervention has an immediate effect, and results in an average decrease of 3.5 percentage points over the twelve months under consideration. In Portugal, however, the impact is small and generally close to zero. Different electricity market structures in each country are a plausible explanation.
This study investigates the impact of inaccurate assumptions on economic forecast precision. We construct a new dataset comprising an unbalanced panel of annual German GDP forecasts from various institutions, taking into account their underlying assumptions. We explicitly control for different forecast horizons to reflect the information available at the time of release. Our analysis reveals that approximately 75% of the variation in squared forecast errors can be attributed to the variation in squared errors of the initial assumptions. This finding emphasizes the importance of accurate assumptions in economic forecasting and suggests that forecasters should transparently disclose their assumptions to enhance the usefulness of their forecasts in shaping effective policy recommendations.
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.
Climate change and inequality are critical and interrelated defining issues for this century. Despite growing empirical evidence on the economic incidence of climate policies and impacts, mainstream model-based assessments are often silent on the interplay between climate change and economic inequality. For example, all the major model comparisons reviewed in IPCC neglect within-country inequalities. Here we fill this gap by presenting a model ensemble of eight large-scale Integrated Assessment Models belonging to different model paradigms and featuring economic heterogeneity. We study the distributional implications of Paris-aligned climate target of 1.5 degree and include different carbon revenue redistribution schemes. Moreover, we account for the economic inequalities resulting from residual and avoided climate impacts. We find that price-based climate policies without compensatory measures increase economic inequality in most countries and across models. However, revenue redistribution through equal per-capita transfers can offset this effect, leading to on average decrease in the Gini index by almost two points. When climate benefits are included, inequality is further reduced, but only in the long term. Around mid-century, the combination of dried-up carbon revenues and yet limited climate benefits leads to higher inequality under the Paris target than in the Reference scenario, indicating the need for further policy measures in the medium term.
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 develops a quantitative theory of how people weigh the risks of infections against the benefits of engaging in social interactions that contribute to the spread of infectious diseases. Our framework takes into account the effects of public policies and private behavior on the spread of the disease. We evaluate the model using a novel micro panel dataset on consumption expenditures of young and older people across the first three waves of COVID-19 in Portugal. Our model highlights the critical role of expectations in shaping how human behavior influences the dynamics of epidemics.
This paper surveys recent advances in drawing structural conclusions from vector autoregressions (VARs), providing a unified perspective on the role of prior knowledge. We describe the traditional approach to identification as a claim to have exact prior information about the structural model and propose Bayesian inference as a way to acknowledge that prior information is imperfect or subject to error. We raise concerns from both a frequentist and a Bayesian perspective about the way that results are typically reported for VARs that are set-identified using sign and other restrictions. We call attention to a common but previously unrecognized error in estimating structural elasticities and show how to correctly estimate elasticities even in the case when one only knows the effects of a single structural shock.
This paper develops a novel dataset of weekly economic conditions indices for the 50 U.S. states going back to 1987 based on mixed-frequency dynamic factor models with weekly, monthly, and quarterly variables that cover multiple dimensions of state economies. We find considerable cross-state heterogeneity in the length, depth, and timing of business cycles. We illustrate the usefulness of these state-level indices for quantifying the main contributors to the economic collapse caused by the COVID-19 pandemic and for evaluating the effectiveness of the Paycheck Protection Program. We also propose an aggregate indicator that gauges the overall weakness of the U.S. economy.
This study investigates whether and how financial technologies (FinTech) influence the effectiveness of monetary policy transmission. We use an interacted panel vector autoregression model to explore how the effects of monetary policy shocks change with regional-level FinTech adoption. Results indicate that FinTech adoption generally mitigates the transmission of monetary policy to real GDP, consumer prices, bank loans, and housing prices, with the most significant impact observed in the weakened transmission to bank loan growth. The relaxed financial constraints, regulatory arbitrage, and intensified competition are the possible mechanisms underlying the mitigated transmission.
