Exploiting the heteroskedasticity of the changes in short-term and long-term interest rates and exchange rates around the FOMC announcement, we identify three structural monetary policy shocks. We eliminate the predictable part of the shocks and study their effects on financial variables and macro variables. The first shock resembles a conventional monetary policy shock, and the second resembles an unconventional monetary shock. The third shock leads to an increase in interest rates, stock prices, industrial production, consumer prices, and commodity prices. At the same time, the excess bond premium and uncertainty decrease, and the U.S. dollar depreciates. Therefore, this third shock combines all the characteristics of a central bank information shock.

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 assess the role of silver price fluctuations in Chinese trade and GDP during the late Qing dynasty, when China still had a bimetallic (silver/copper) monetary system, in which silver was mostly used for international trade. Using a structural VAR (SVAR) with blockwise recursive identification, we identify the impact of silver price shocks on the Chinese economy from 1867, when trade data became available, to 1910, one year before the Qing dynasty collapsed. We find that silver price shocks had a sizable impact on both imports and exports but only a very minor effect on the trade balance, only a marginal impact on growth, and almost no effect on domestic prices. Stronger effects were partly mitigated by inelastic export quantities. Generally, the effect of silver price shocks, while considerable, was only short-lived, displaying no persistence in either direction. We find that the bimetallic system in Qing China might have mitigated a potential positive effect of silver depreciation but did not reverse the effect, which – contrary to claims made in the previous literature – was responsible for neither the worsening trade balance nor the inflation and the quickly increasing imports that occurred during our sample period.

In this study, we test whether three popular measures for monetary policy, that is, Romer and Romer (2004), Barakchian and Crowe (2013), and Gertler and Karadi (2015), constitute suitable proxy variables for monetary policy shocks. To this end, we employ different test statistics used in the literature to detect weak proxy variables. We find that the measure derived by Gertler and Karadi (2015) is the most suitable in this regard.