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Wenn die AfD hier gewinnt, wären die Folgen überall in Deutschland deutlich zu spürenReint GroppDer Spiegel, 8. Januar 2026
This paper studies the interaction between fiscal policy and bondholders against the backdrop of high sovereign debt levels. For our analysis, we investigate the case of Italy, a country that has dealt with high public debt levels for a long time, using a Bayesian structural VAR model. We extend a canonical three variable macro mode to include a bond market, consisting of a fiscal rule and a bond demand schedule for long-term government bonds. To identify the model in the presence of political uncertainty and forward-looking investors, we derive an external instrument for bond demand shocks from a novel news ticker data set. Our main results are threefold. First, the interaction between fiscal policy and bondholders’ expectations is critical for the evolution of prices. Fiscal policy reinforces contractionary monetary policy through sustained increases in primary surpluses and investors provide incentives for “passive” fiscal policy. Second, investors’ expectations matter for inflation, and we document a Fisherian response of inflation across all maturities in response to a bond demand shock. Third, domestic politics is critical in the determination of bondholders’ expectations and an increase in the perceived riskiness of sovereign debt increases inflation and thus complicates the task of controlling price growth.
This paper contributes to a better understanding of the important role that credit demand plays for credit markets and aggregate macroeconomic developments as both a source and transmitter of economic shocks. I am the first to identify a structural credit demand equation together with credit supply, aggregate supply, demand and monetary policy in a Bayesian structural VAR. The model combines informative priors on structural coefficients and multiple external instruments to achieve identification. In order to improve identification of the credit demand shocks, I construct a new granular instrument from regional mortgage origination. I find that credit demand is quite elastic with respect to contemporaneous macroeconomic conditions, while credit supply is relatively inelastic. I show that credit supply and demand shocks matter for aggregate fluctuations, albeit at different times: credit demand shocks mostly drove the boom prior to the financial crisis, while credit supply shocks were responsible during and after the crisis itself. In an out-of-sample exercise, I find that the Covid pandemic induced a large expansion of credit demand in 2020Q2, which pushed the US economy towards a sustained recovery and helped to avoid a stagflationary scenario in 2022.
We show that global supply and demand shocks are important drivers of interest rate co-movement across seven advanced economies. Beyond that, local structural shocks transmit internationally via aggregate demand channels, and central banks react predominantly to domestic macroeconomic developments: unexpected monetary policy tightening decreases most foreign interest rates, while expansionary local supply and demand shocks increase them. To disentangle determinants of international interest rate co-movement, we use a Bayesian structural panel vector autoregressive model accounting for latent global supply and demand shocks. We identify country-specific structural shocks via informative prior distributions based on a standard theoretical multi-country open economy model.
Two contradictory strands of the rating literature criticize that rating agencies merely follow the market on the one hand, and emphasizing that rating changes affect capital movements on the other hand. Both focus on explaining rating levels rather than the timing of rating announcements. Contrarily, we explicitly differentiate between a decision to assess a country and the actual rating decision. We show that this differentiation significantly improves the estimation of the rating function. The three major rating agencies treat economic fundamentals similarly, while differing in their response to other factors such as strategic considerations. This reconciles the conflicting literature.
The Swiss National Bank abolished the exchange rate floor versus the Euro in January 2015. Using a synthetic matching framework, we analyze the impact of this unexpected (and therefore exogenous) policy change on the stock market. The results reveal a significant level shift (decline) in asset prices following the discontinuation of the minimum exchange rate. As a novel finding in the literature, we document that the exchange‐rate elasticity of Swiss asset prices is around −0.75. Differentiating between sectors of the Swiss economy, we find that the industrial, financial and consumer goods sectors are most strongly affected by the abolition of the minimum exchange rate.
In this paper, we use local projections to investigate the impact of consolidation shocks on GDP growth, conditional on the fragility of government finances. Based on a database of fiscal plans in OECD countries, we show that spending shocks are less detrimental than tax-based consolidation. In times of fiscal fragility, our results indicate strongly that governments should consolidate through surprise policy changes rather than announcements of consolidation at a later horizon.
Recurring financial instabilities have led policymakers to rely on early-warning models to signal financial vulnerabilities. These models rely on ex-post optimization of signaling thresholds on crisis probabilities accounting for preferences between forecast errors, but come with the crucial drawback of unstable thresholds in recursive estimations. We propose two alternatives for threshold setting with similar or better out-of-sample performance: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide real-world and simulation evidence that this simplification results in stable thresholds, while keeping or improving on out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.
This paper investigates the propagation of technology news shocks within and across industrialised economies. We construct quarterly utilisation-adjusted total factor productivity (TFP) for thirteen OECD countries. Based on country-specific structural vector autoregressions (VARs), we document that (i) the identified technology news shocks induce a quite homogeneous response pattern of key macroeconomic variables in each country; and (ii) the identified technology news shock processes display a significant degree of correlation across several countries. Contrary to conventional wisdom, we find that the US are only one of many different sources of technological innovations diffusing across advanced economies. Technology news propagate through the endogenous reaction of monetary policy and via trade-related variables. That is, our results imply that financial markets and trade are key channels for the dissemination of technology.
This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance metric, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly efficiently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.
