Search Results
Working Paper
Risks and Uncertainty in Monetary Policy
Central banks monitor macroeconomic risk through two traditions: scenario analysis, regularly used since the mid-1990s, and distributional forecasting, practiced since the late 1960s. The two are complementary but separate: scenarios provide narratives without probabilities, while predictive distributions provide probabilities with limited economic interpretation. Treating baseline forecasts and scenarios as conditional predictive densities, and distributional forecasts as reference predictive distributions, places both within a common framework and clarifies their roles. The Scenario ...
Working Paper
On Model Aggregation and Forecast Combination
Policy makers express their views and decisions via the lens of a particular model or theory. But since any model is a highly stylized representation of the unknowable object of interest, all these models are inherently misspecified, and the resulting ambiguity injects uncertainty in the decision-making process. We argue that entropy-based aggregation is a convenient device to confront this uncertainty and summarize relevant information from a set of candidate models and forecasts. The proposed aggregation tends to robustify the decision-making process to various sources of risks and ...
Working Paper
Model Uncertainty and the Pricing of Hurricane Risk in Florida
This paper examines how model uncertainty affects the price of homeowners insurance in Florida. We use unique data on expected loss rate projections from seven hurricane risk models approved by regulators for use in Florida property insurance rate filings to quantify model uncertainty. By combining these data with newly published information on local property insurance markets, we are able to empirically test the relationship between model uncertainty and insurance premiums across Florida ZIP codes and over time. Controlling for confounding variables and time-invariant latent factors that may ...
Discussion Paper
Combining Models for Forecasting and Policy Analysis
Model uncertainty is pervasive. Economists, bloggers, policymakers all have different views of how the world works and what economic policies would make it better. These views are, like it or not, models. Some people spell them out in their entirety, equations and all. Others refuse to use the word altogether, possibly out of fear of being falsified. No model is “right,” of course, but some models are worse than others, and we can have an idea of which is which by comparing their predictions with what actually happened. If you are open-minded, you may actually want to combine models in ...
Report
Real-time inflation forecasting in a changing world
This paper revisits the accuracy of inflation forecasting using activity and expectations variables. We apply Bayesian-model averaging across different regression specifications selected from a set of potential predictors that includes lagged values of inflation, a host of real activity data, term structure data, nominal data, and surveys. In this model average, we can entertain different channels of structural instability by incorporating stochastic breaks in the regression parameters of each individual specification within this average, allowing for breaks in the error variance of the ...
Working Paper
Doubts about the Model and Optimal Policy
This paper analyzes optimal policy in setups where both the leader and the follower have doubts about the probability model of uncertainty. I illustrate the methodology in two environments: a) an industry populated with a large firm and many small firms in a competitive fringe, where both types of firms doubt the probability model of demand shocks, and b) a general equilibrium economy, where a policymaker taxes linearly the labor income of a representative household in order to finance an exogenous stream of stochastic spending shocks. The policymaker can distrust the probability model of ...
Discussion Paper
Choosing the Right Policy in Real Time (Why That’s Not Easy)
As an economist, you make policy recommendations at any point in time that depend on what model of the economy you have in mind and on your assessment of the state of the economy. One can see these points play out in the current discussion about the timing of interest rate liftoff and the speed of the subsequent renormalization. If you think nominal rigidities are not all that important, you are likely to conclude that accommodative policies won’t do much for growth but will generate inflation. Similarly, if you are convinced that the economy is already firing on all cylinders, you may see ...
Working Paper
On Monetary Policy, Model Uncertainty, and Credibility
This paper studies the design of optimal time-consistent monetary policy in an economy where the planner and a representative household are faced with model uncertainty: While they are able to construct and agree on a reference model (probability distribution) governing the evolution of the exogenous state of the economy, a representative household has fragile beliefs and is averse to model uncertainty. In such environments, management of households' expectations becomes an active channel of optimal policymaking per se. A central banker who respects the fact that private sector models are ...
Working Paper
Optimal monetary policy under model uncertainty without commitment
This paper studies the design of optimal time-consistent monetary policy in an economy where the planner trusts its own model, while a representative household uses a set of alternative probability distributions governing the evolution of the exogenous state of the economy. In such environments, unlike in the original studies of time-consistent monetary policy, managing households' expectations becomes an active channel of optimal policymaking per se, a feature that the paternalistic government seeks to exploit. We adapt recursive methods in the spirit of Abreu, Pearce, and Stacchetti (1990) ...