Working Papers
Quantile Local Projections: Identification, Smooth Estimation, and Inference (previously circulated as “Smooth Quantile Projections”)
Standard impulse response functions measure the average effect of a shock on a response variable. However, different parts of the distribution of the response variable may react to the shock differently. A popular method to capture this heterogeneity are quantile regression local projections. We achieve their structural identification by short-run restrictions or external instruments and we establish their asymptotics. To overcome their excessive volatility, we introduce two novel smoothing estimators and propose information criteria for optimal smoothing. In the first empirical application, we show that financial conditions affect the entire distribution of GDP growth and not just its lower part. Thus, financial conditions matter not only for recessions, but also during normal times and even in recovery periods. The second application demonstrates that conventional monetary policy is more effective at curbing inflation than at generating it.
Standard impulse response functions measure the effects of shocks on the expectation of response variables. We introduce a framework to measure the effects of shocks on the entire distribution of response variables, not just on the mean. Various identification schemes are considered: short-run and long-run restrictions, external instruments, and their combinations. Asymptotic distribution of the estimators is established. Simulations show our method is robust to heavy tails. Empirical applications reveal causal effects that cannot be captured by the standard approach. For example, the effect of oil price shock on GDP growth is statistically significant only in the left part of GDP growth distribution, so a spike in oil price may cause a recession, but there is no evidence that a drop in oil price may cause an expansion. Another application reveals that real activity shocks reduce stock market volatility.
Other Publications
Financial stability considerations in the conduct of monetary policy (2023, with P. Bochmann, D. Dieckelmann, S. Fahr)
We empirically analyze the interaction of monetary policy with financial stability and the real economy in the euro area. For this, we apply a quantile vector autoregressive model and two alternative estimation approaches: simulation and local projections. Our specifications include monetary policy surprises, real GDP, inflation, financial vulnerabilities and systemic financial stress. We disentangle conventional and unconventional monetary policy by separating interest rate surprises into two factors that move the yield curve either at the short end or at the long end. Our results show that a build-up of financial vulnerabilities tends to be accompanied initially by subdued financial stress which resurges, however, over a medium-term horizon, harming economic growth. Tighter conventional monetary policy reduces inflationary pressures but increases the risk of financial stress. We find unconventional monetary policy to be similarly effective in reducing inflation, but with a lower adverse effect on growth and financial stress. Tighter unconventional monetary policy is also found to have a dampening effect on the build-up of financial vulnerabilities.
Anticipating the bust: a new cyclical systemic risk indicator to assess the likelihood and severity of financial crises (2019, with J. H. Lang, C. Izzo, S. Fahr)
This paper presents a tractable, transparent and broad-based domestic cyclical systemic risk indicator (d-SRI) that captures risks stemming from domestic credit, real estate markets, asset prices, and external imbalances. The d-SRI increases on average several years before the onset of systemic financial crises, and its early warning properties for euro area countries are superior to those of the total credit-to-GDP gap. In addition, the level of the d-SRI around the start of financial crises is highly correlated with measures of subsequent crisis severity, such as GDP declines. Model estimates suggest that the d-SRI has significant predictive power for large declines in real GDP growth three to four years down the line, as it precedes shifts in the entire distribution of future real GDP growth and especially of its left tail. The d-SRI therefore provides useful information about both the probability and the likely cost of systemic financial crises many years in advance. Given its timely signals, the d-SRI is a useful analytical tool for macroprudential policymakers.
The Real-Time Information Content of Financial Stress and Bank Lending on European Business Cycles (2019, with J. Fiedler, T. Theobald)
We integrate newly created financial stress indices (FSIs) into an automated real-time recession forecasting procedure for the Euro area and Germany. The FSIs are based on a large number of financial indicators, each of them potentially signaling financial stress. A subset of these indicators is selected in real-time and their stress signal is summarized by principal component analysis (PCA). Besides conventional measures of realized financial stress, such as volatilities, we include variables related to the financial cycle, such as different types of credit growth, for which strong increases may anticipate future financial market stress. Building blocks in our fully automated realtime probit forecasts are then i. the use of a broad set of widely acknowledged macroeconomic and financial variables with predictive power for a real economic downturn, ii. the use of both general-to-specific and specific-to-general approaches for variable and lag selection, and iii. the averaging of different specifications into a composite forecast. As a real-time out-of-sample analysis shows, the inclusion of financial stress leads to an improved recession forecast for the Euro area, while the results for Germany are mixed. Finally, we also evaluate the predictive power of the change in bank lending (credit impulse) and find that it adds little additional information.