Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)
In Bayesian analysis of dynamic stochastic general equilibrium (DSGE) models, prior distributions for some of the taste-and-technology parameters can be obtained from microeconometric or presample evidence, but it is difficult to elicit priors for the parameters that govern the law of motion of unobservable exogenous processes. Moreover, since it is challenging to formulate beliefs about the correlation of parameters, most researchers assume that all model parameters are independent of each other. We provide a simple method of constructing prior distributions for a subset of DSGE model ...
Fitting observed inflation expectations
This paper provides evidence on the extent to which inflation expectations generated by a standard Christiano et al. (2005)/Smets and Wouters (2003)?type DSGE model are in line with what is observed in the data. We consider three variants of this model that differ in terms of the behavior of, and the public?s information on, the central banks? inflation target, allegedly a key determinant of inflation expectations. We find that: 1) time-variation in the inflation target is needed to capture the evolution of expectations during the post-Volcker period; 2) the variant where agents have ...
A History of SOMA Income
Historically, the Federal Reserve has held mostly interest-bearing securities on the asset side of its balance sheet and, up until 2008, mostly currency on its liability side, on which it pays no interest. Such a balance sheet naturally generates income, which is almost entirely remitted to the U.S. Treasury once operating expenses and statutory dividends on capital are paid and sufficient earnings are retained to equate surplus capital to capital paid in. The financial crisis that began in late 2007 prompted a number of changes to the balance sheet. First, the asset side of the balance sheet ...
The FRBNY DSGE Model Forecast
The U.S. economy has been in a gradual but slow recovery. Will the future be more of the same? This post presents the current forecasts from the Federal Reserve Bank of New York’s (FRBNY) DSGE model, described in our earlier “Bird’s Eye View” post, and discusses the driving forces behind the forecasts. Find the code used for estimating the model and producing all the charts in this blog series here. (We should reiterate that these are not the official New York Fed staff forecasts, but only an input to the overall forecasting process at the Bank.)
An Assessment of the FRBNY DSGE Model's Real-Time Forecasts, 2010-2013
The previous post in this series showed how the Federal Reserve Bank of New York?s DSGE model can be used to provide an interpretation of the Great Recession and the slow recovery. In this post, we look at the role of the model as a forecasting tool and evaluate its forecasting performance since 2010. This analysis will give context for the last post, which will present the model?s current forecast for the U.S. economy.
Aggregate unemployment in Krusell and Smith’s economy: a note
Using data on workers? flows into and out of employment, unemployment, and not-in-the-labor-force, I construct transition probabilities between ?employment? and ?unemployment? that can be used in the calibration of economies such as Krusell and Smith?s (1998). I show that calibration in Krusell and Smith has some counterfactual features. Yet the gains from adopting alternative calibrations in terms of matching the data are not very large, unless one assumes that the duration of unemployment spells is well above what is usually assumed in the literature.
More Than Meets the Eye: Some Fiscal Implications of Monetary Policy
In 2012, the Fed’s remittances to the U.S. Treasury amounted to $88.4 billion. The vast majority of these remittances originated as income from the SOMA portfolio (see the second post in this series for an account of the history of SOMA income). While net income has been high in recent years because of the Fed’s large balance sheet, it is likely to drop in the future as the Fed normalizes interest rates. This is because the Fed will likely face increased interest expense on its reserve balances and possibly realize losses in the case of asset sales. A recent paper by economists at the ...
Take your model bowling: forecasting with general equilibrium models
During the past two decades, dynamic stochastic general equilibrium (DSGE) models have taken center stage in academic macroeconomics. Nonetheless, these models are still rarely used in policy-making and forecasting. ; This article describes the workings of the DSGE-VAR, a procedure that combines DSGE models and vector autoregressions (VARs). The procedure uses DSGE models as priors to restrict the VAR?s parameters. Since the VAR?s parameters are imprecisely estimated unless a very long time series of data is available, using DSGE priors can improve the VAR?s forecasting performance. Moreover, ...
Dynamic prediction pools: an investigation of financial frictions and forecasting performance
We provide a novel methodology for estimating time-varying weights in linear prediction pools, which we call dynamic pools, and use it to investigate the relative forecasting performance of dynamic stochastic general equilibrium (DSGE) models, with and without financial frictions, for output growth and inflation in the period 1992 to 2011. We find strong evidence of time variation in the pool?s weights, reflecting the fact that the DSGE model with financial frictions produces superior forecasts in periods of financial distress but doesn?t perform as well in tranquil periods. The dynamic ...
Online Estimation of DSGE Models
The estimation of dynamic stochastic general equilibrium (DSGE) models is a computationally demanding task. As these models change to address new challenges (such as household and firm heterogeneity, the lower bound on nominal interest rates, and occasionally binding financial constraints), they become even more complex and difficult to estimate?so much so that current estimation procedures are no longer up to the task. This post discusses a new technique for estimating these models which belongs to the class of sequential Monte Carlo (SMC) algorithms, an approach we employ to estimate the ...