We provide new insights into determinants of international interest rates spillovers across seven advanced economies. To disentangle and quantify their respective importance, we identify country-specific structural monetary policy, demand, and supply equations in a Bayesian structural panel vector autoregressive model. We formulate prior beliefs on magnitudes and signs of contemporaneous structural coefficients (i.e., (semi-)elasticities), based on a standard theoretical multi-country open economy model from the literature. Our findings show that interest rate spillovers occur via an aggregated demand channel. Unexpected monetary tightening causes modest declines in most foreign interest rates, while demand and supply shocks result in increased foreign interest rates. Our results support that central banks respond to changes in the domestic macroeconomic environment induced by domestic or foreign shocks rather than directly reacting to foreign shocks. Spillovers are quantitatively stronger for shocks originating in economically large areas with strong trade linkages.

This paper studies monetary policy transmission through BigTech and traditional banks. By comparing business loans made by a BigTech bank with those made by traditional banks, it finds that BigTech credit amplifies monetary policy transmission mainly through the extensive margin. Specifically, the BigTech bank is more likely to grant credit to new borrowers compared with conventional banks in response to expansionary monetary policy. The BigTech bank‘s advantages in information, monitoring, and risk management are the potential mechanisms. In addition, monetary policy has a stronger impact on the real economy through BigTech lending.

We propose a macroeconomic model with a nonlinear Phillips curve that has a flat slope when inflationary pressures are subdued and steepens when inflationary pressures are elevated. The nonlinear Phillips curve in our model arises due to a quasi-kinked demand schedule for goods produced by firms. Our model can jointly account for the modest decline in inflation during the Great Recession and the surge in inflation during the Post-Covid period. Because our model implies a stronger transmission of shocks when inflation is high, it generates conditional heteroscedasticity in inflation and inflation risk. Hence, our model can generate more sizeable inflation surges due to cost-push and demand shocks than a standard linearized model. Finally, our model implies that the central bank faces a more severe trade-off between inflation and output stabilization when inflation is high.

The Covid epidemic had a large impact on economic activity. In contrast, the dramatic decline in mortality from infectious diseases over the past 120 years had a small economic impact. We argue that people's response to successive Covid waves helps reconcile these two findings. Our analysis uses a unique administrative data set with anonymized monthly expenditures at the individual level that covers the first three Covid waves. Consumer expenditures fell by about the same amount in the first and third waves, even though the risk of getting infected was larger in the third wave. We find that people had pessimistic prior beliefs about the case-fatality rates that converged over time to the true case-fatality rates. Using a model where Covid is endemic, we show that the impact of Covid is small when people know the true case-fatality rate but large when people have empirically-plausible pessimistic prior beliefs about the case-fatality rate. These results reconcile the large economic impact of Covid with the small effect of the secular decline in mortality from infectious diseases estimated in the literature.

Non-clearing goods markets are an important driver of capacity utilisation and total factor productivity (TFP). The trade-off between goods prices and household search effort is central to goods market matching and therefore drives TFP over the business cycle. In this paper, I develop a New-Keynesian DSGE model with capital utilisation, worker effort, and expand it with<i> goods market search-and-matching (SaM)</i> to model non-clearing goods markets. I conduct a horse-race between the different capacity utilisation channels using Bayesian estimation and capacity utilisation survey data. Models that include goods market SaM improve the data fit, while the capital utilisation and worker effort channels are rendered less important compared to the literature. It follows that TFP fluctuations increase for demand and goods market mismatch shocks, while they decrease for technology shocks. This pattern increases as goods market frictions increase and as prices become stickier. The paper shows the importance of non-clearing goods markets in explaining the difference between technology and TFP over the business cycle.