Digital euro
The Eurosystem is preparing for the potential issuance of a digital euro to complement physical cash.
INTERVIEW
22 September 2026
Energy shock to keep inflation higher
The second wave of energy price increases means inflation is likely to be higher for longer before falling back towards target from mid-2027 onwards, Chief Economist Philip R. Lane tells Le Temps. He also discusses economic growth, government spending and artificial intelligence.
Read the full Q&A
SPEECH
14 September 2026
A new age of capital: growth, sovereignty and AI
AI is the project that could drive European capital markets forward to channel savings where they are needed, says President Christine Lagarde. It needs capital on a scale that only markets can provide, and the stakes for Europe could hardly be higher.
Read President Lagarde's speech
PUBLICATION
15 September 2026
Calling merchants for digital euro pilot
The Eurosystem is looking for e-commerce and mobile commerce merchants to join the digital euro pilot in 2027. Selected applicants will test a beta version of the digital euro at ECB and national central bank premises to help refine its design and functionalities.
Read the call for expression of interest- 22 September 2026
- WEEKLY FINANCIAL STATEMENTEnglishOTHER LANGUAGES (22) +Annexes
- 22 September 2026
- WEEKLY FINANCIAL STATEMENT - COMMENTARY
- 21 September 2026
- PRESS RELEASE
- 21 September 2026
- PRESS RELEASE
- 18 September 2026
- PRESS RELEASE
- 18 September 2026
- BALANCE OF PAYMENTS (MONTHLY)
- 14 September 2026
- Speech by Christine Lagarde, President of the ECB, at “Hofburg im Dialog – Economy, Europe, Resilience” in Vienna, Austria
- 14 September 2026
- Keynote speech by Piero Cipollone, Member of the Executive Board of the ECB, at the House of the EuroRelated
- 23 July 2026
- EnglishOTHER LANGUAGES (23) +
- 14 September 2026
- Slides by Isabel Schnabel, Member of the Executive Board of the European Central Bank, at the 2nd Federal Ministry for Economic Affairs and Energy (BMWE) Symposium on European Competitiveness in Berlin, Germany
- 12 September 2026
- Speech by Christine Lagarde, President of the ECB, at Fête de la Pomme in Épreville-en-Lieuvin, France
- 11 September 2026
- Slides by Philip R. Lane, Member of the Executive Board of the ECB, at Richard Cantillon Lecture at 49th DEW Annual Economic Policy Conference 2026 in Wexford, Ireland
- 22 September 2026
- Interview with Philip R. Lane, Member of the Executive Board of the ECB, conducted by Sébastien Ruche on 15 September 2026
- 18 September 2026
- Interview with Boris Vujčić, Vice-President of the ECB, conducted by Francesco Canepa on 16 September 2026
- 12 September 2026
- Interview with Christine Lagarde, President of the ECB, conducted by Jean-Christophe Lalay and Maxime Mainguet on 12 September 2026
- 24 August 2026
- Interview with Piero Cipollone, Member of the Executive Board of the ECB, conducted by Lorenzo Torrisi on 10 August 2026
- 15 July 2026
- Interview with Piero Cipollone, Member of the Executive Board of the ECB, conducted by Élisabeth Montaufray-Bureau on 10 July 2026
- 15 September 2026
- Around 80% of euro area households do not own stocks or any other market-based financial instruments, unlike their counterparts in the United States. This blog post examines the barriers that keep many Europeans from investing and explores ways to broaden capital market participation.Details
- JEL Code
- G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions
G51 : Financial Economics
- 2 September 2026
- Synthetic securitisation can free up bank capital. But does that mean banks lend more to firms? This ECB Blog post explores the effects of loan securitisation. We find that banks that issue synthetic securitisations lend marginally more, but also tend to pay more dividends.Details
- JEL Code
- G00 : Financial Economics→General→General
- 1 September 2026
- The drivers of the recent rise in inflation are different from those of the pandemic-era surge. This time the energy supply shock dominates, while demand and public policy stimulus have minor roles. These differences are key to explaining why monetary policy responses differ.Details
- JEL Code
- E30 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→General
- 31 August 2026
- US tech giants are increasingly tapping the euro area bond market to fund their investments. The ECB Blog investigates the consequences for this market and the potential for these developments to reshape it.Details
- JEL Code
- O16 : Economic Development, Technological Change, and Growth→Economic Development→Financial Markets, Saving and Capital Investment, Corporate Finance and Governance
- 26 August 2026
- Use of artificial intelligence at work has doubled over the last two years and people report significant time-savings. But an ECB survey shows that perceived productivity gains vary widely and there are still barriers preventing many from adopting this new technology.Details
- JEL Code
- J01 : Labor and Demographic Economics→General→Labor Economics: General
O33 : Economic Development, Technological Change, and Growth→Technological Change, Research and Development, Intellectual Property Rights→Technological Change: Choices and Consequences, Diffusion Processes
- 22 September 2026
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 6, 2026Details
- Abstract
- This box discusses the heterogeneous impact on EU countries of China’s rapid industrial transformation, which is reshaping global trade patterns. The similarity between the export structures of China and several EU countries has increased substantially since 2019, particularly in machinery and transport equipment, and is most pronounced in manufacturing-intensive economies, such as Germany. At the same time, China’s goods imports have become less aligned with EU export structures, illustrating China’s decreased reliance on European industrial goods. These developments have coincided with losses in EU export market shares in sectors and destinations where Chinese competition has intensified, although EU exports have remained more resilient in higher-value-added sectors, especially in the US market. These findings illustrate that there are differences across EU countries and sectors in terms of their exposure to China’s industrial rise, reflecting differences in specialisation and integration into global value chains, with implications for policy priorities at the EU and national levels.
- JEL Code
- F14 : International Economics→Trade→Empirical Studies of Trade
F13 : International Economics→Trade→Trade Policy, International Trade Organizations
F15 : International Economics→Trade→Economic Integration
F60 : International Economics→Economic Impacts of Globalization→General
- 22 September 2026
- CONSULTATION RESPONSE
- 22 September 2026
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 6, 2026Details
- Abstract
- This box analyses developments in US equity markets during the recent AI boom and highlights recent signs of heightened price differentiation within the broader equity market rally. It shows that, over the past years, US equity valuations have been bolstered by strong realised and expected earnings tied to the AI boom. Risk appetite in US equity markets has been strong, with compensation for equity risk falling to relatively low levels during this period. These factors have supported resilience in US stock markets despite pressures from higher longer-term interest rates and geopolitical headwinds. However, a note of caution is warranted, as market sentiment can be subject to sudden shifts and there is heightened risk price differentiation among the tails of higher-risk technology firms within the AI-driven rally.
- JEL Code
- G15 : Financial Economics→General Financial Markets→International Financial Markets
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
Q43 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Energy→Energy and the Macroeconomy
F51 : International Economics→International Relations, National Security, and International Political Economy→International Conflicts, Negotiations, Sanctions
- 21 September 2026
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 6, 2026Details
- Abstract
- In the Survey on the Access to Financing of Enterprises (SAFE) for the second quarter of 2026, firms were asked about their exposure to the conflict in the Middle East, how the conflict has influenced their expectations, and what coping strategies they have adopted. It found that exposure to the conflict is higher for small and medium-sized enterprises, for exporters and in the trade sector. The conflict has primarily affected firms’ expectations for nominal variables and demand, while the impact on expectations for other real variables is small. The main coping strategies reported by firms include seeking alternative suppliers of energy, raw materials and components, as well as accelerating investment in energy efficiency.
- JEL Code
- C83 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Survey Methods, Sampling Methods
D22 : Microeconomics→Production and Organizations→Firm Behavior: Empirical Analysis
D84 : Microeconomics→Information, Knowledge, and Uncertainty→Expectations, Speculations
- 21 September 2026
- WORKING PAPER SERIES - No. 3288Details
- Abstract
- Theory suggests inflation risk premia are positive when supply shocks are expected to dominate demand shocks and negative otherwise. We measure these beliefs using demand and supply narratives derived from inflation news via Causality Extraction, which identifies causal relations between inflation and its drivers. Using narrative extracted from inflation news from the Financial Times for the Euro Area and the Wall Street Journal for the US, our key variable, NetDemand, measures the difference in articles attributing inflation to demand versus supply factors. Consistent with asset pricing theory, inflation risk premia are inversely related to NetDemand across maturities in both regions. This relationship holds even after controlling for the composite PMI and VIX, strengthens with risk aversion in the US and inflation volatility in the Euro Area, and is not subsumed by other measures of demand and supply contributions to inflation, views of professional forecasters, or narratives obtained from LLMs.
- JEL Code
- C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
- 21 September 2026
- WORKING PAPER SERIES - No. 3287Details
- Abstract
- We construct a novel bank-level index that quantifies fragmentation in the capital buffer framework faced by euro area banks. Defined at quarterly frequency, it measures fragmentation by looking at the number of simultaneously active buffers, their geographical dispersion, and the frequency of buffer rate changes within the preceding year. The index is orthogonalised with respect to the level of capital requirements, bank size, and the financial cycle, thus controlling for these factors when measuring fragmentation as defined above. We then show that a one standard deviation increase in the index is associated with about 36 basis points higher capital headroom and around 50 basis points lower corporate lending growth within existing bank-firm relationships. We interpret these results as suggestive evidence that banks facing more fragmented buffer requirements retain extra capital and adjust lending more cautiously to account for higher uncertainty with respect to future adjustments in buffer requirements.
- JEL Code
- E5 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit
E51 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Money Supply, Credit, Money Multipliers
G18 : Financial Economics→General Financial Markets→Government Policy and Regulation
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
- 21 September 2026
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 6, 2026Details
- Abstract
- The sharp rise in energy prices in the first half of 2026 evoked memories of the 2021-22 energy price shock. However, the 2026 energy price shock has so far been smaller in scale. This reflects more limited growth in wholesale gas prices and an increased share of electricity generated from renewables, which has dampened the pass-through of gas prices to wholesale electricity prices. The transmission of wholesale prices to retail prices has also changed, becoming somewhat faster for gas prices. These developments imply that shifts in wholesale gas prices may be feeding through to HICP gas inflation somewhat more swiftly than in the past, while having a lesser impact on HICP electricity inflation. At the same time, both the role of gas prices in wholesale electricity prices and the transmission of wholesale prices to retail prices remain uneven across countries.
- JEL Code
- E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
Q41 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Energy→Demand and Supply, Prices
Q42 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Energy→Alternative Energy Sources
Q43 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Energy→Energy and the Macroeconomy
- 18 September 2026
- WORKING PAPER SERIES - No. 3286Details
- Abstract
- Using exogenous variation generated by the creation of the Single Supervisory Mechanism (SSM) in the euro area, we find that relative to firms borrowing from banks subject to national supervision, firms borrowing from banks subject to supranational supervision reduce their share of intangible assets. This effect does not pre-date the supervisory reform and it does not obtain in non-SSM jurisdictions. The reallocation of investment away from intangible assets is stronger for small and young firms and appears to be driven by a short-term reduction in lending and a persistent increase in collateral standards.
- JEL Code
- D25 : Microeconomics→Production and Organizations
F30 : International Economics→International Finance→General
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
- 18 September 2026
- WORKING PAPER SERIES - No. 3285Details
- Abstract
- This paper examines how bank capital conditions the effect of competition on credit risk in lending markets, informing the debate on banking competition, deregulation, and risk-based supervision. Using ECB supervisory data for 146 euro area banks across 19 countries over 2020Q2–2025Q3, we analyze whether this relationship depends on banks’ regulatory capital positions. We find that greater market power is associated with higher subsequent credit risk, while stronger capitalization is associated with lower risk. Crucially, competition reduces credit risk primarily for well-capitalized banks, whereas the effect is weak or absent for banks with lower capital ratios. By aligning the measurement of competition and risk with the pricing-based mechanism, the paper provides a direct empirical test of the borrower-risk channel and offers an explanation for mixed evidence in the competition–risk literature. The results highlight the importance of considering the interaction between competition and prudential capital requirements when assessing financial stability.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
L11 : Industrial Organization→Market Structure, Firm Strategy, and Market Performance→Production, Pricing, and Market Structure, Size Distribution of Firms
C23 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Panel Data Models, Spatio-temporal Models
- 17 September 2026
- WORKING PAPER SERIES - No. 3284Details
- Abstract
- We develop a direct approach to incorporating survey density forecasts into model-based predictive distributions. Histogram forecasts from the U.S. Survey of Professional Forecasters (SPF) carry rich nonparametric information about expected outcomes, but existing methods rely on moment-based approximations that discard part of it. We instead tilt entropically to the histogram probabilities themselves, matching them exactly. After reformulating the single-histogram problem, we derive a new analytic characterization of the multiple-histogram case, solved by Iterative Proportional Fitting and applicable to simulated densities from essentially any model. Applying the method to real-time forecasts from a Bayesian VAR with time-varying volatility, we find that tilting to SPF histograms substantially improves accuracy relative to the model’s baseline forecasts, especially during the Great Recession and the COVID-19 pandemic. The gains extend beyond the variables the SPF targets, improving forecasts for other variables in the system as well.
- JEL Code
- C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
- 17 September 2026
- WORKING PAPER SERIES - No. 3283Details
- Abstract
- Measuring sentiment from financial news is a central task in economics and finance, yet most existing indicators rely on dictionary-based approaches that infer sentiment from word counts and only partially capture context, negation, and semantic structure. This paper proposes a framework for constructing daily news mood indices using transformer-based language models and evaluates whether they better represent sentiment than dictionary-based alternatives. Using 143,755 financial news articles from Factiva, we classify sentiment at the sentence level with FinBERT and aggregate these predictions into article-level and daily sentiment measures through alternative normalization schemes. We compare the resulting indices with benchmark measures based on Shapiro et al., 2022 and Barbaglia et al., 2025. A central contribution is the validation of alternative sentiment measures against human judgments. We conducted an incentivized annotation exercise in which 444 participants evaluated a validation subsample of 588 financial news articles. Consensus ratings from independent human evaluations serve as an external benchmark for assessing the quality of automated sentiment measures. Across correlation, regression, and classification exercises, transformer-based measures show stronger agreement with human judgments than vocabulary-based alternatives and perform substantially better in distinguishing positive, neutral, and negative articles. Overall, the results suggest that incorporating contextual information through transformer-based language models produces sentiment measures that more closely reflect human assessments of financial news.
- JEL Code
- C55 : Mathematical and Quantitative Methods→Econometric Modeling→Modeling with Large Data Sets?
C81 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Microeconomic Data, Data Access
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
G14 : Financial Economics→General Financial Markets→Information and Market Efficiency, Event Studies, Insider Trading
- 17 September 2026
- OCCASIONAL PAPER SERIES - No. 399Details
- Abstract
- Estimates of potential output and the output gap are widely used in monetary, fiscal and structural policy analysis, but they are unobservable and subject to substantial real-time uncertainty. This paper examines revisions to output gap and potential growth estimates produced by the European Commission, the Eurosystem, the International Monetary Fund (IMF) and the Organisation for Economic Co-operation and Development (OECD) for the euro area and its member countries. Using annual forecast vintages covering up to 20 euro area countries from 2002 to 2025 and a balanced panel of 11 countries from 2007 to 2025, the paper compares the reliability of estimates across institutions, decomposes output gap revisions into their main sources and estimates the impact of real GDP data revisions and forecast errors on potential growth revisions. The results show sizeable differences across institutions: European Commission estimates revise least in the balanced country sample, OECD estimates revise most, and Eurosystem estimates have become markedly more stable over the past decade. Output gap revisions are driven mainly by revisions to real GDP data and potential growth, while nowcast errors play a smaller role. Panel regressions show that both real GDP data revisions and medium-term forecast errors are significantly associated with revisions to potential growth, implying that revised or unexpected GDP developments are to some extent absorbed into estimates of potential growth. The findings highlight the uncertainty surrounding real-time output gap estimates and support the use of complementary indicators of economic slack.
- JEL Code
- E23 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Production
E27 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Forecasting and Simulation: Models and Applications
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
C82 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Macroeconomic Data, Data Access
- 16 September 2026
- WORKING PAPER SERIES - No. 3282Details
- Abstract
- Suspending loan repayments is a widely used policy tool to provide liquidity during crises. We study the take-up and real effects of the 2020 Austrian corporate debt moratoria, which required banks to temporarily postpone loan repayments for eligible firms. Exploiting a discontinuity in eligibility at a two-million-euro asset threshold, we document a take-up rate of 44%, well below full participation, reflecting both the pecuniary cost of the policy and firms’ fear of stigmatization. Despite the moderate take-up, moratoria increase investment and profitability without increasing defaults once repayments resume. We estimate a marginal propensity to invest of 47 cents per euro of postponed payments. We compare moratoria with grants, the predominant form of business support in the United States. A key feature of the policy is that relief is administered by relationship lenders, who continue to bear credit risk, retain incentives to monitor borrowers, and can credibly discipline them through future lending decisions. Although grants achieve higher take-up, our results suggest that moratoria differ fundamentally by discouraging firms from diverting liquidity to shareholders and instead channeling it toward productive investment.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G32 : Financial Economics→Corporate Finance and Governance→Financing Policy, Financial Risk and Risk Management, Capital and Ownership Structure, Value of Firms, Goodwill
G35 : Financial Economics→Corporate Finance and Governance→Payout Policy
H81 : Public Economics→Miscellaneous Issues→Governmental Loans, Loan Guarantees, Credits, Grants, Bailouts
D25 : Microeconomics→Production and Organizations
- 15 September 2026
- WORKING PAPER SERIES - No. 3281Details
- Abstract
- How does the effectiveness of monetary policy vary over the policy cycle? Do tightenings and loosenings have symmetric effects on the macroeconomy? This paper addresses these questions using a nonlinear empirical framework that allows financial exposure to evolve endogenously in response to macroeconomic conditions and monetary policy changes. We provide new evidence on how monetary policy effectiveness varies over the policy cycle and across economic states. We find that i) monetary policy transmits more strongly to the real economy in periods of elevated private-sector financial exposure; ii) tightening cycles increase financial exposure in the short run, which in turn amplifies the effect of further interest rate increases, whereas loosening cycles lower financial exposure, increasingly dampening the effect of interest rate cuts; iii) tightening during economic downturns further intensifies debt-servicingpressures, making monetary policy even more potent; instead, when the tightening occurs during expansions, monetary policy effectiveness is not materially affected.
- JEL Code
- E3 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
G01 : Financial Economics→General→Financial Crises
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
- 14 September 2026
- WORKING PAPER SERIES - No. 3280Details
- Abstract
- Does the Federal Reserve’s monetary policy influence the rates on USD-pegged stablecoins? While major stablecoin issuers do not pay interest, investors can earn returns by depositing stablecoins in Decentralized Finance (DeFi) protocols. We document unusually large and persistent spreads between traditional short-term interest rates and DeFi deposit rates, as well as a weak and unstable transmission of policy rate changes. We show that, in the short run, monetary policy shocks can move stablecoin rates in the opposite direction of policy rates, delaying a convergence that occurs only over the medium run. Both the sign of the short-run effect and the speed of convergence depend on the intensity of deleveraging induced by crypto-price reactions relative to the standard interest-rate arbitrage channel — an effect shaped by investors’ limited ability to bridge traditional and decentralized finance.
- JEL Code
- G14 : Financial Economics→General Financial Markets→Information and Market Efficiency, Event Studies, Insider Trading
G23 : Financial Economics→Financial Institutions and Services→Non-bank Financial Institutions, Financial Instruments, Institutional Investors
G29 : Financial Economics→Financial Institutions and Services→Other
- 14 September 2026
- WORKING PAPER SERIES - No. 3279Details
- Abstract
- Government spending falls disproportionately on non-tradable services. We show empirically that government spending shocks stimulate private consumption along with sizable spillovers to the goods sector and a relative decline in goods prices. We rationalize these findings with a two-sector open-economy HANK model. Uninsurable income risk and precautionary savings lead to a persistent income-driven expansion in private consumption. In the tradable sector, import intensity and limited labor reallocation dampen wage pass-through to prices, matching observed co-movements. The resulting expenditure switching produces a positive tradable output response despite deteriorating net exports. Household heterogeneity and trade openness jointly shape sectoral fiscal multipliers.
- JEL Code
- E62 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Fiscal Policy
F41 : International Economics→Macroeconomic Aspects of International Trade and Finance→Open Economy Macroeconomics
E21 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Consumption, Saving, Wealth
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
- 11 September 2026
- SURVEY OF MONETARY ANALYSTS - AGGREGATE RESULTS
- 10 September 2026
- MACROECONOMIC PROJECTIONS FOR THE EURO AREAEnglishOTHER LANGUAGES (21) +Annexes
- 10 September 2026
- MACROECONOMIC PROJECTIONS FOR THE EURO AREA
- 31 August 2026
- WORKING PAPER SERIES - No. 3278Details
- Abstract
- This paper examines whether environmental policy uncertainty undermines clean-technology investment in the United States by weakening policy-induced investment incentives. Using quarterly project-level data on U.S. greenfield investments from 2007Q1 to 2019Q1, combined with novel news-based indices that separately measure environmental policy salience and environmental policy uncertainty, we find that policy uncertainty substantially offsets the positive investment effects of environmental policy. At the aggregate level, a one-standard-deviation increase in environmental policy uncertainty eliminates roughly 75% of the policy-induced increase in the number of cleantech projects and around 50% of the increase in capital expenditure. The deterrence effect is substantially stronger for foreign than for domestic investors, consistent with greater informational frictions facing cross-border capital. These effects persist for at least two years following an uncertainty shock and are corroborated by country- and firm-level analyses, though results for capital expenditure are less robust at disaggregated levels. The findings imply that policy credibility is a first-order determinant of clean investment: an environmental policy framework that is ambitious but perceived as unstable may fail to mobilize the capital it is designed to attract.
- JEL Code
- Q58 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Environmental Economics→Government Policy
F21 : International Economics→International Factor Movements and International Business→International Investment, Long-Term Capital Movements
F23 : International Economics→International Factor Movements and International Business→Multinational Firms, International Business
E22 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Capital, Investment, Capacity
- 24 August 2026
- WORKING PAPER SERIES - No. 3277Details
- Abstract
- We analyze the Probability of Default (PD) of non-financial corporations in Europe using Random Forests (RF) and assess implications for stress testing the banking sector. To this end, we exploit data on firms’ financial statements (Orbis) and banks’ credit registry (Anacredit). We show that RF displays stronger risk sensitivity than logistic regression in stress testing, shedding new light on the non-linear effect of scenario severity on PD. Moreover, we show how RF-based PD can be used in a network of banks and firms to stress test the banking sector through loan exposures as a key transmission channel of adverse scenarios. A granular inspection of banks’ riskiness indices derived from this network sheds light also on RF’s superior ability in capturing non-linearity thanks to its capability in identifying “tail banks”. Our work is relevant for central banks and banking supervisors alike.
- JEL Code
- C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
C55 : Mathematical and Quantitative Methods→Econometric Modeling→Modeling with Large Data Sets?
C58 : Mathematical and Quantitative Methods→Econometric Modeling→Financial Econometrics
G17 : Financial Economics→General Financial Markets→Financial Forecasting and Simulation
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
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