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Wenn die AfD hier gewinnt, wären die Folgen überall in Deutschland deutlich zu spürenReint GroppDer Spiegel, 8. Januar 2026
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.
Sovereign ratings have frequently failed to predict crises. However, the literature has focused on explaining rating levels rather than the timing of rating announcements. We fill this gap by explicitly differentiating between a decision to assess a country and the actual rating decision. Thereby, we account for rational inattention of rating agencies that exists due to costs of reassessment. Exploiting information of rating announcements, we show that (i) the proposed differentiation significantly improves estimation; (ii) rating agencies consider many nonfundamental factors in their reassessment decision; (iii) markets only react to ratings providing new information; (iv) developed countries get preferential treatment.
We use the Chinese experience between 1867 and 1910 to illustrate how the volatility of vehicle currencies affects trade. Today’s widespread vehicle currency is the dollar. However, the macroeconomic effects of this use of the dollar have rarely been addressed. This is partly due to identification problems caused by its international importance. China had adopted a system, where silver was used almost exclusively for trade, similar to a vehicle currency. While being important for China, the global role of silver was marginal, alleviating said identification problems. We develop a bias corrected structural VAR showing that silver price fluctuations significantly affected trade.
In this paper, we propose a new method to assess the impact of sovereign ratings on sovereign bond yields. We estimate the impulse response of the interest rate, following a change in the rating. Since ratings are ordinal and moreover extremely persistent, it proves difficult to estimate those impulse response functions using a VAR modeling ratings, yields and other macroeconomic indicators. However, given the highly stochastic nature of the precise timing of ratings, we can treat most rating adjustments as shocks. We thus no longer rely on a VAR for shock identification, making the estimation of the corresponding IRFs well suited for so called local projections – that is estimating impulse response functions through a series of separate direct forecasts over different horizons. Yet, the rare occurrence of ratings makes impulse response functions estimated through that procedure highly sensitive to individual observations, resulting in implausibly volatile impulse responses. We propose an augmentation to restrict jointly estimated local projections in a way that produces economically plausible impulse response functions.
In the present paper, we build a bivariate semiparametric dynamic panel model to repro-duce the joint dynamics of sovereign ratings and government bond yields. While the individual equations resemble Pesaran-type cointegration models, we allow for different long-run relationships in both equations, nonlinearities in the level effect of ratings, and asymmetric effects in changes of ratings and yields. We find that the interest rate equation and the rating equation imply significantly different long-run relationships. While the high persistence in both interest rates and ratings might lead to the misconception that they follow a unit root process, the joint analysis reveals that they converge slowly to a joint equilibrium. While this indicates that there is no vicious cycle driving countries into default, the persistence of ratings is high enough that a rating shock can have substantial costs. Generally, the interest rate adjusts rather quickly to the risk premium that is in line with the rating. For most ratings, this risk premium is only marginal. However, it becomes substantial when ratings are downgraded to highly speculative (a rating of B) or lower. Rating shocks that drive the rating below this threshold can increase the interest rate sharply, and for a long time. Yet, simulation studies based on our estimations show that it is highly improbable that rating agencies can be made responsible for the most dramatic spikes in interest rates.
Were real effective exchange rates (REER) of Euro area member countries drastically misaligned at the outbreak of the global financial crisis? The answer is difficult to determine because economic theory gives no simple guideline for determining the equilibrium values of real exchange rates, and the determinants of those values might have been distorted as well. To overcome these limitations, we use synthetic matching to construct a counterfactual economy for each member as a linear combination of a large set of non-Euro area countries. We find that Euro area crisis countries are best described by a mixture of advanced and emerging economies. Comparing the actual REER with those of the counterfactuals gives sensible estimates of the misalignments at the start of the crisis: All peripheral countries were strongly overvalued, while high undervaluation is only observed for Finland.
Due to the recent financial crisis, the interest in econometric models that allow to incorporate binary variables (such as the occurrence of a crisis) experienced a huge surge. This paper evaluates the performance of the Qual VAR, i.e. a VAR model including a latent variable that governs the behavior of an observable binary variable. While we find that the Qual VAR performs reasonably well in forecasting (outperforming a probit benchmark), there are substantial identification problems. Therefore, when the economic interpretation of the dynamic behavior of the latent variable and the chain of causality matter, the Qual VAR is inadvisable.
While the long-run relation between money and inflation as predicted by the quantity theory is well established, empirical studies of the short-run adjustment process have been inconclusive at best. The literature regarding the validity of the quantity theory within a given economy is mixed. Previous research has found support for quantity theory within a given economy by combining the P-Star, the structural VAR and the monetary aggregation literature. However, these models lack precise modelling of the short-run dynamics by ignoring interest rates as the main policy instrument. Contrarily, most New Keynesian approaches, while excellently modeling the short-run dynamics transmitted through interest rates, ignore the role of money and thus the potential mid-and long-run effects of monetary policy. We propose a parsimonious and fairly unrestrictive econometric model that allows a detailed look into the dynamics of a monetary policy shock by accounting for changes in economic equilibria, such as potential output and money demand, in a framework that allows for both monetarist and New Keynesian transmission mechanisms, while also considering the Barnett critique. While we confirm most New Keynesian findings concerning the short-run dynamics, we also find strong evidence for a substantial role of the quantity of money for price movements.
In the tradition of Romer and Romer (2000), this paper compares staff forecasts of the Federal Reserve (Fed) and the European Central Bank (ECB) for inflation and output with corresponding private forecasts. Standard tests show that the Fed and less so the ECB have a considerable information advantage about inflation and output. Using novel tests for conditional predictive ability and forecast stability for the US, we identify the driving forces of the narrowing of the information advantage of Greenbook forecasts coinciding with the Great Moderation.
The signals approach as an early warning system has been fairly successful in detecting crises, but it has so far failed to gain popularity in the scientific community because it does not distinguish between randomly achieved in-sample fit and true predictive power. To overcome this obstacle, we test the null hypothesis of no correlation between indicators and crisis probability in three applications of the signals approach to different crisis types. To that end, we propose bootstraps specifically tailored to the characteristics of the respective datasets. We find (1) that previous applications of the signals approach yield economically meaningful and statistically significant results and (2) that composite
indicators aggregating information contained in individual indicators add value to the signals approach, even where most individual indicators are not statistically significant on their own.
