Search Results
Working Paper
Usual Shocks in our Usual Models
We propose an event-study research design to identify the nature and propagation of large unusual shocks in DSGE models and apply it to study the macroeconomic effects of the Covid shock. The initial outbreak is represented as the onset of a new shock process where the shock loads on wedges associated with the model's usual shocks. Realizations of the Covid shock come with news about its propagation, allowing us to disentangle the role of beliefs about the future of the pandemic. The model attributes a crucial role to the novel Covid shock in explaining the large contraction in output in the ...
Journal Article
The Macroeconomic Effects of the 2018 Bipartisan Budget Act
The 2018 Bipartisan Budget Act raised government spending caps by $300 billion for fiscal years 2018 and 2019. While spending does not increase immediately, private sector investment and consumption may respond ahead of an anticipated fiscal stimulus. This Economic Perspectives article assesses the strength of this mechanism based on the private sector?s expectations.
Working Paper
A Hitchhiker’s Guide to Empirical Macro Models
This paper describes a package which uses MATLAB functions and routines to estimate VARs, local projections and other models with classical or Bayesian methods. The toolbox allows a researcher to conduct inference under various prior assumptions on the parameters, to produce point and density forecasts, to measure spillovers and to trace out the causal effect of shocks using a number of identification schemes. The toolbox is equipped to handle missing observations, mixed frequencies and time series with large cross-section information (e.g. panels of VAR and FAVAR). It also contains a number ...
Working Paper
Identification Using Higher-Order Moments Restrictions
We exploit inequality restrictions on higher-order moments of the distribution of structural shocks to sharpen their identification. We show that these constraints can be treated as necessary conditions and used to shrink the set of admissible rotations. We illustrate the usefulness of this approach showing, by simulations, how it can dramatically improve the identification of monetary policy shocks when combined with widely used sign-restriction schemes. We then apply our methodology to two empirical questions: the effects of monetary policy shocks in the U.S. and the effects of sovereign ...
Working Paper
Delphic and Odyssean Monetary Policy Shocks: Evidence from the Euro Area
We use financial intraday data to identify monetary policy surprises in the euro area. We find that monetary policy statements and press conferences after European Central Bank (ECB) Governing Council meetings convey information that moves the yield curve far out. Moreover, the nature of the information revealed in a narrow window around these statements and press conferences evolved over time. Until 2013, unexpected variations in future interest rates were positively correlated with the changes in market-based measure of inflation expectations consistent with news on future macroeconomic ...
Working Paper
Delphic and Odyssean Monetary Policy Shocks: Evidence from the Euro Area
What drives the strong reaction of financial markets to central bank communication on the days of policy decisions? We highlight the role of two factors that we identify from high-frequency monetary surprises: news on future macroeconomic conditions (Delphic shocks) and news on future monetary policy shocks (Odyssean shocks). These two shocks move the yield curve in the same direction but have opposite effects on financial conditions and macroeconomic expectations. A drop in future interest rates that is associated with a negative Delphic (Odyssean) shock is perceived as being contractionary ...
Newsletter
How Interconnected Are Cryptocurrencies and What Does This Mean for Risk Management
In the past couple of years, the market for digital currencies, commonly known as cryptocurrencies because transactions are verified using cryptography, has expanded significantly in terms of transaction volumes, market capitalization, and the number of digital currencies in existence. On January 1, 2018, the market capitalizations (market caps1) of Bitcoin and Ethereum were $226 billion and $75 billion, respectively. By May 10, 2021, Bitcoin’s market cap had reached almost $1 trillion and Ethereum’s $478 billion.In this article, I measure the market’s interconnections in term of prices ...
Working Paper
The Chicago Fed DSGE Model: Version 2
The Chicago Fed dynamic stochastic general equilibrium (DSGE) model is used for policy analysis and forecasting at the Federal Reserve Bank of Chicago. This guide describes its specification, estimation, dynamic characteristics, and how it is used to forecast the U.S. economy. In many respects the model resembles other medium-scale New Keynesian frameworks, but there are several features which distinguish it: the monetary policy rule includes anticipated future deviations, productivity is driven by both neutral and investment specific technical change, multiple price and wage indices identify ...
Working Paper
Selecting Primal Innovations in DSGE models
DSGE models are typically estimated assuming the existence of certain primal shocks that drive macroeconomic fluctuations. We analyze the consequences of estimating shocks that are "non-existent" and propose a method to select the primal shocks driving macroeconomic uncertainty. Forcing these non-existing shocks in estimation produces a downward bias in the estimated internal persistence of the model. We show how these distortions can be reduced by using priors for standard deviations whose support includes zero. The method allows us to accurately select primal shocks and estimate model ...
Working Paper
A Hitchhiker’s Guide to Empirical Macro Models
This paper describes a package which uses MATLAB functions and routines to estimate VARs, local projections and other models with classical or Bayesian methods. The toolbox allows a researcher to conduct inference under various prior assumptions on the parameters, to produce point and density forecasts, to measure spillovers and to trace out the causal effect of shocks using a number of identification schemes. The toolbox is equipped to handle missing observations, mixed frequencies and time series with large cross-section information (e.g. panels of VAR and FAVAR). It also contains a number ...