Monetary policy and the private sector behaviour of the U.S. economy are modelled as a time varying structural vector autoregression, where the sources of time variation are both the coefficients and the variance covariance matrix of the innovations. The paper develops a new, simple modelling strategy for the law of motion of the variance covariance matrix and proposes an efficient Markov chain Monte Carlo algorithm for the model likelihood/posterior numerical evaluation. The main empirical conclusions are: (1) both systematic and non-systematic monetary policy have changed during the last 40 years—in particular, systematic responses of the interest rate to inflation and unemployment exhibit a trend toward a more aggressive behaviour, despite remarkable oscillations; (2) this has had a negligible effect on the rest of the economy. The role played by exogenous non-policy shocks seems more important than interest rate policy in explaining the high inflation and unemployment episodes in recent U.S. economic history.

This paper provides an explanation for the run-up of U. S. inflation in the 1960s and 1970s and the sharp disinflation in the early 1980s, which standard macroeconomic models have difficulties in addressing. I present a model in which rational policy-makers learn about the behavior of the economy in real time and set stabilization policy optimally, conditional on their current beliefs. The steady state associated with the self-confirming equilibrium of the model is characterized by low inflation. However, prolonged episodes of high inflation ending with rapid disinflations can occur when policy-makers underestimate both the natural rate of unemployment and the persistence of inflation in the Phillips curve. I estimate the model using likelihood methods. The estimation results show that the model accounts remarkably well for the evolution of policy-makers' beliefs, stabilization policy, and the postwar behavior of inflation and unemployment in the United States.

We investigate the sources of the important shifts in the volatility of US macroeconomic variables in the postwar period. To this end, we propose the estimation of DSGE models allowing for time variation in the volatility of the structural innovations. We apply our estimation strategy to a large-scale model of the business cycle and find that shocks specific to the equilibrium condition of investment account for most of the sharp decline in volatility of the last two decades.

We study the evolution of market-oriented policies over time and across countries. We consider a model in which own and neighbors' past experiences influence policy choices through their effect on policymakers' beliefs. We estimate the model using a large panel of countries and find that it fits a large fraction of the policy choices observed in the postwar data, including the slow adoption of liberal policies. Our model also predicts that there would be reversals to state intervention if nowadays the world was hit by a shock of the size of the Great Depression.

We compare sparse and dense representations of predictive models in macroeconomics, microeconomics, and finance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate on a single sparse model, but on a wide set of models that often include many predictors.

Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-of-sample forecasts, particularly for models with many variables. A solution to this problem is to use informative priors in order to shrink the richly parameterized unrestricted model toward a parsimonious naıve benchmark, and thus reduce estimation uncertainty. This paper studies the optimal choice of the informativeness of these priors, which we treat as additional parameters, in the spirit of hierarchical modeling. This approach, theoretically grounded and easy to implement, greatly reduces the number and importance of subjective choices in the setting of the prior. Moreover, it performs very well in terms of both out-of-sample forecasting—as well as factor models—and accuracy in the estimation of impulse response functions.

The surge in credit and house prices that preceded the Great Recession was particularly pronounced in ZIP codes with a higher fraction of subprime borrowers We present a simple model with prime and subprime borrowers distributed across geographic locations, which can reproduce this stylized fact as a result of an expansion in the supply of credit. Due to their low income, subprime households are constrained in their ability to meet interest payments and hence sustain debt. As a result, when the supply of credit increases and interest rates fall, they take on disproportionately more debt than their prime counterparts, who are not subject to that constraint.

We document the emergence of a disconnect between mortgage and Treasury interest rates in the summer of 2003. Following the end of the Federal Reserve expansionary cycle in June 2003, mortgage rates failed to rise according to their historical relationship with Treasury yields, leading to significantly and persistently easier mortgage credit conditions. We uncover this phenomenon by analyzing a large dataset with millions of loan-level observations, which allows us to control for the impact of varying loan, borrower and geographic characteristics. These detailed data also reveal that delinquency rates started to rise for loans originated after mid 2003, exactly when mortgage rates disconnected from Treasury yields and credit became relatively cheaper.

An increase in credit supply driven by looser lending constraints in the mortgage market is the key force behind four empirical features of the housing boom before the Great Recession: the unprecedented rise in home prices, the surge in household debt, the stability of debt relative to house values, and the fall in mortgage rates. These facts are more difficult to reconcile with the popular view that attributes the housing boom only to looser borrowing constraints associated with lower collateral requirements, because they shift the demand for credit.