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Author:Giannone, Domenico 

Discussion Paper
A DSGE Perspective on Safety, Liquidity, and Low Interest Rates

The preceding two posts in this series documented that interest rates on safe and liquid assets, such as U.S. Treasury securities, have declined significantly in the past twenty years. Of course, short-term interest rates in the United States are under the control of the Federal Reserve, at least in nominal terms. So it is legitimate to ask, To what extent is this decline driven by the Federal Reserve’s interest rate policy? This post addresses this question by coupling the results presented in the previous post with those obtained from an estimated dynamic stochastic general equilibrium ...
Liberty Street Economics , Paper 20180207

Working Paper
Nowcasting Business Cycles: a Bayesian Approach to Dynamic Heterogeneous Factor Models

We develop a framework for measuring and monitoring business cycles in real time. Following a long tradition in macroeconometrics, inference is based on a variety of indicators of economic activity, treated as imperfect measures of an underlying index of business cycle conditions. We extend existing approaches by permitting for heterogenous lead-lag patterns of the various indicators along the business cycles. The framework is well suited for high-frequency monitoring of current economic conditions in real time - nowcasting - since inference can be conducted in presence of mixed frequency ...
Finance and Economics Discussion Series , Paper 2015-66

Working Paper
Global Trends in Interest Rates

The trend in the world real interest rate for safe and liquid assets fluctuated close to 2 percent for more than a century, but has dropped significantly over the past three decades. This decline has been common among advanced economies, as trends in real interest rates across countries have converged over this period. It was driven by an increase in the convenience yield for safety and liquidity and by lower global economic growth.
Working Papers , Paper 1812

Discussion Paper
Changing Risk-Return Profiles

Are stock returns predictable? This question is a perennially popular subject of debate. In this post, we highlight some results from our recent working paper, where we investigate the matter. Rather than focusing on a single object like the forecasted mean or median, we look at the entire distribution of stock returns and find that the realized volatility of stock returns, especially financial sector stock returns, has strong predictive content for the future distribution of stock returns. This is a robust feature of the data since all of our results are obtained with real-time analyses ...
Liberty Street Economics , Paper 20181004

Report
A Large Bayesian VAR of the United States Economy

We model the United States macroeconomic and financial sectors using a formal and unified econometric model. Through shrinkage, our Bayesian VAR provides a flexible framework for modeling the dynamics of thirty-one variables, many of which are tracked by the Federal Reserve. We show how the model can be used for understanding key features of the data, constructing counterfactual scenarios, and evaluating the macroeconomic environment both retrospectively and prospectively. Considering its breadth and versatility for policy applications, our modeling approach gives a reliable, reduced form ...
Staff Reports , Paper 976

Discussion Paper
Vulnerable Growth

Traditional GDP forecasts potentially present an overly optimistic (or pessimistic) view of the state of the economy: by focusing on the point estimate for the conditional mean of growth, such forecasts ignore risks around the central forecast. Yet, policymakers around the world increasingly focus on risks to the central forecast in policy debates. For example, in the United States the Federal Open Market Committee (FOMC) commonly discusses the balance of risks in the economy, with the relative prominence of this discussion fluctuating with the state of the economy. In a recent paper, we ...
Liberty Street Economics , Paper 20180409

Report
Economic predictions with big data: the illusion of sparsity

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 or dense model, but on a wide set of models. A clearer pattern of sparsity can only emerge when models of very low dimension are strongly favored a priori.
Staff Reports , Paper 847

Working Paper
Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases

This paper formalizes the process of updating the nowcast and forecast on output and inflation as new releases of data become available. The marginal contribution of a particular release for the value of the signal and its precision is evaluated by computing "news" on the basis of an evolving conditioning information set. The marginal contribution is then split into what is due to timeliness of information and what is due to economic content. We find that the Federal Reserve Bank of Philadelphia surveys have a large marginal impact on the nowcast of both inflation variables and real ...
Finance and Economics Discussion Series , Paper 2005-42

Discussion Paper
Economic Predictions with Big Data: The Illusion of Sparsity

The availability of large data sets, combined with advances in the fields of statistics, machine learning, and econometrics, have generated interest in forecasting models that include many possible predictive variables. Are economic data sufficiently informative to warrant selecting a handful of the most useful predictors from this larger pool of variables? This post documents that they usually are not, based on applications in macroeconomics, microeconomics, and finance.
Liberty Street Economics , Paper 20180521

Working Paper
Low Frequency Effects of Macroeconomic News on Government Bond Yields

This study analyzes the reaction of the U.S. Treasury bond market to innovations in macroeconomic fundamentals. We identify these innovations with macroeconomic news, defined as differences between the actual releases and their market expectations. We show that macroeconomic news explain about one-third of the low frequency (quarterly) fluctuations of long-term bond yields. When focusing on the high frequency (daily) movements this share decreases to one-tenth. This result is due to the fact that macro news have a persistent effect on the yield curve. Non-fundamental factors, instead, ...
Finance and Economics Discussion Series , Paper 2014-52

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