Paolo Gelain

We document a sizable effect of oil price fluctuations on US banking variables by estimating an SVAR with sign restrictions as in Baumeister and Hamilton (2019). We find that oil market shocks that lead to a contraction in world economic activity unambiguously lower the amount of bank credit to the US economy, tend to decrease US banks' net worth, and tend to increase the US credit spread. The effects can be strong and long-lasting, or more modest and short-lived, depending on the source of the oil price fluctuations. The effects are stronger for smaller and lower leveraged banks.

This paper outlines the dynamic stochastic general equilibrium (DSGE) model developed at the Federal Reserve Bank of Cleveland as part of the suite of models used for forecasting and policy analysis by Cleveland Fed researchers, which we have nicknamed CLEMENTINE (CLeveland Equilibrium ModEl iNcluding Trend INformation and the Effective lower bound). This document adopts a practitioner's guide approach, detailing the construction of the model and offering practical guidance on its use as a policy tool designed to support decision-making through forecasting exercises and policy counterfactuals.

We document the existence of a quantitative relevant banks' balance-sheet transmission channel of oil price shocks by estimating a dynamic stochastic general equilibrium model with banking and oil sectors. The associated amplification mechanism implies that those shocks explain a non-negligible share of US GDP growth fluctuations, up to 17 percent, instead of 6 percent absent the banking sector. Also, they mitigated the severity of the Great Recession’s trough. GDP growth would have been 2.48 percentage points more negative in 2008Q4 without the beneficial effect of low oil prices. The estimate without the banking sector is only 1.30 percentage points.

We use mixed-frequency (quarterly-monthly) data to estimate a dynamic stochastic general equilibrium model embedded with the financial accelerator mechanism à la Bernanke et al. (1999). We find that the financial accelerator can work very differently at monthly frequency compared to quarterly frequency; that is, we document its inversion. That is because aggregating monthly data into quarterly data leads to large biases in the estimated quarterly parameters and, as a consequence, to a deep change in the transmission of shocks.

We consider two approaches to incorporate judgment into DSGE models. First, Bayesian estimation indirectly imposes judgment via priors on model parameters, which are then mapped into a judgmental interest rate decision. Standard priors are shown to be associated with highly unrealistic judgmental decisions. Second, judgmental interest rate decisions are directly provided by the decision maker and incorporated into a formal statistical decision rule using frequentist procedures. When the observed interest rates are interpreted as judgmental decisions, they are found to be consistent with DSGE models for long stretches of time, but excessively tight in the 1980s and late 1990s and excessively loose in the late 1970s and early 2000s.

This paper investigates how the presence of pervasive financial frictions and large financial shocks changes the optimal monetary policy prescriptions and the estimated dynamics in a New Keynesian model. We find that financial factors affect the optimal policy only to some extent. A policy of nominal stabilization (with a particular focus on targeting wage inflation) is still the optimal policy, although the central bank is now unable to fully stabilize economic activity around its potential level. In contrast, the presence of financial frictions and financial shocks crucially changes the size and shape of the estimated output gap and the relative importance of different shocks in driving economic fluctuations, with financial shocks absorbing explanatory power from labor supply shocks.

We study the implications of multiperiod mortgage loans for monetary policy, considering several realistic modifications—fixed interest rate contracts, a lower bound constraint on newly granted loans, and the possibility of the collateral constraint to become slack—to an otherwise standard DSGE model with housing and financial intermediaries. We estimate the model in its nonlinear form and argue that all these features are important to understand the evolution of mortgage debt during the recent US housing market boom and bust. We show how the nonlinearities associated with the two constraints make the transmission of monetary policy dependent on the housing cycle, with weaker effects observed when house prices are high or start falling sharply. We also find that higher average loan duration makes monetary policy less effective and may lead to asymmetric responses to positive and negative monetary shocks.

Research suggests that standard macroeconomic models may require adjustments to capture key developments during the post-pandemic period and forecast a path consistent with a soft landing.