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.

Benmir, Jaccard, and Vermandel (2023, BJV) seek to answer the following set of topical and important research questions: (i) How should monetary policy be conducted during a pandemic?, (ii) How do health considerations affect the conduct of monetary policy?, and (iii) How does the presence of contagion risk affect the main building blocks of the New Keynesian model?

Die wirtschaftliche Aktivität und das Ausfallrisiko staatlicher Schulden beeinflussen sich gegenseitig. Sinkt die wirtschaftliche Aktivität einer Volkswirtschaft, steigt wegen fallender Steuereinnahmen das Risiko, dass der Staat Zinszahlungen und Tilgungen auf Staatsanleihen nicht zurückzahlen kann. Umgekehrt kann das staatliche Ausfallrisiko seinerseits die wirtschaftliche Aktivität beeinflussen. Steigt das Ausfallrisiko, geraten Banken unter Druck, die Staatsanleihen in ihren Bilanzen führen, und reduzieren die Kreditvergabe an Unternehmen. In der Konsequenz sinkt die wirtschaftliche Aktivität. Dieser Beitrag nutzt hochfrequente News-Ticker-Daten zur Ableitung politischer Ereignisse und davon ausgelöster Fluktuationen im Staatsschuldenrisiko. Diese allein politisch bedingten Fluktuationen ermöglichen es, den Effekt des Staatsschuldenrisikos auf die wirtschaftliche Aktivität zu messen, ohne dass die Schätzung von der gegenläufigen Beziehung der Variablen beeinträchtigt wird. Das Vorgehen wird am Beispiel Italiens erläutert.

This paper studies the impact of household indebtedness on the transmission of monetary policy to consumption using the Chinese household-level survey data. We employ a panel smooth transition regression model to investigate the non-linear role of indebtedness. We find that housing-related indebtedness weakens the monetary policy transmission, and this effect is non-linear as there is a much larger counteraction of consumption in response to monetary policy shocks when household indebtedness increases from a low level rather than from a high level. Moreover, the weakened monetary policy transmission from indebtedness is stronger in urban households than in rural households. This can be explained by the investment good characteristic of real estate in China.

Die Gaspreise haben sich in Deutschland infolge des Lieferstopps russischen Erdgases deutlich erhöht, mit möglichen Folgen für die Wettbewerbsfähigkeit der deutschen Industrie. Wir berechnen den Gasverbrauch auf Produktebene für die Zeit vor der Energiekrise mit Hilfe der Mikrodaten der amtlichen Statistik, um zielgenau abschätzen zu können, bei welchen Produkten eine Drosselung der Produktion zur maximalen Gaseinsparung bei minimalen wirtschaftlichen Verlusten führen würde. Die Verwendung von Mikrodaten zeigt, dass die Folgen für Umsatz und Wertschöpfung in der Industrie bei Weitem nicht so negativ ausfallen werden wie von vielen befürchtet.

Russian monetary policy has been challenged by large and continuous private capital outflows and a sharp drop in oil prices during 2014. Both contributed to significant depreciation pressures on the ruble and led the central bank to give up its exchange rate management strategy. Against this background, this work estimates a small open economy model for Russia, featuring an oil price sector and extended by a specification of the foreign exchange market to correctly account for systematic central bank interventions. We find that shocks to the oil price and private capital flows substantially affect domestic variables such as inflation and output. Simulations for the estimated actual strategy and alternative regimes suggest that the vulnerability of the Russian economy to external shocks can substantially be lowered by adopting some form of inflation targeting. Strategies to target the nominal exchange rate or the ruble price of oil prove to be inferior.

The international co-movement of interest rates reflects correlated business-cycle fluctuations, largely driven by demand shocks. Monetary policy in advanced economies follows domestic mandates – inflation and the output gap – and does not respond to foreign policy shocks. We derive this result from a Bayesian structural panel vector autoregression with informative priors, homogeneity restrictions on contemporaneous relations, a hierarchical Minnesota prior with cross-sectional shrinkage, and a factor structure for structural shocks.

Real estate markets are pivotal to financial stability given their dual role as the underlying asset of crucial financial products in financial systems, such as mortgage loans and asset-backed securities, and the primary source of household wealth alike. As such, they also play traditionally a crucial role for the transmission of monetary policy. Imbalances and sudden corrections in real estate markets have been the root cause of many financial crises over the last decades. But whereas some national, often survey-based indicators of real estate prices are provided by central banks and statistical offices, a comprehensive collection of purchase prices, rents, and proxies for the liquidity of European real estate markets is lacking. The IWH European Real Estate Index (EREI) seeks to fill this void for residential property. This technical report describes the gathering and processing of sale and rental prices for properties in 18 European countries. We provide the general scrapeing step in the section before describing country-specific details for each country in separated sub-sections.

Asset prices are a valuable source of information about financial market participants' expectations about key macroeconomic variables. However, the presence of time-varying risk premia requires an adjustment of market prices to obtain the market's rational assessment of future price and policy developments. This paper reviews empirical approaches for recovering market-based expectations. It starts by laying out the two canonical modeling frameworks that form the backbone for estimating risk premia and highlights the proliferation of risk pricing factors that result in a wide range of different asset-price-based expectation measures. It then describes a key methodological innovation to evaluate the empirical plausibility of risk premium estimates and to identify the most accurate market-based expectation measure. The usefulness of this general approach is illustrated for price expectations in the global oil market. Then, the paper provides an overview of the body of empirical evidence for monetary policy and inflation expectations with a special emphasis on market-specific characteristics that complicate the quest for the best possible market-based expectation measure. Finally, it discusses a number of economic applications where market expectations play a key role for evaluating economic models, guiding policy analysis, and deriving shock measures.

This paper provides both theoretical and empirical analyses of the differences between BigTech lenders and traditional banks in response to monetary policy changes. Our model integrates Knightian uncertainty into portfolio selection and posits that BigTech lenders possess a diminishing informational advantage with increasing firm size, resulting in reduced ambiguity when lending to smaller firms. The model suggests that the key distinction between BigTech lenders and traditional banks in response to shifts in funding costs, triggered by monetary policy changes, is more evident at the extensive margin rather than the intensive margin, particularly during periods of easing monetary policy. Using a micro-level dataset of small business loans from both types of lenders, we provide empirical support for our theoretical propositions. Our results show that BigTech lenders are more responsive in establishing new lending relationships in an easing monetary policy environment, while the differences in loan amounts are not statistically significant. We also discuss other loan terms and the implications of regulatory policies.