Comparing Greenbook and Reduced Form Forecasts using a Large Realtime Dataset

Many recent papers have found that atheoretical forecasting methods using many predictors give better predictions for key macroeconomic variables than various small-model methods. The practical relevance of these results is open to question, however, because these papers generally use ex post revised data not available to forecasters and because no comparison is made to best actual practice. We provide some evidence on both of these points using a new large dataset of vintage data synchronized with the Fed's Greenbook forecast. This dataset consists of a large number of variables, as observed at the time of each Greenbook forecast since 1979. Thus, we can compare real-time large dataset predictions to both simple univariate methods and to the Greenbook forecast. For inflation we find that univariate methods are dominated by the best atheoretical large dataset methods and that these, in turn, are dominated by Greenbook. For GDP growth, in contrast, we find that once one takes account of Greenbook's advantage in evaluating the current state of the economy, neither large dataset methods nor the Greenbook process offers much advantage over a univariate autoregressive forecast.

Cracking the Conundrum

From 2004 to 2006, the FOMC raised the target federal funds rate by 4.25%, yet long-maturity yields and forward rates fell. We consider several possible explanations for this "conundrum." The most likely, in our view, is a fall in the term premium, probably associated with some combination of diminished macroeconomic and financial market volatility, more predictable monetary policy, and the state of the business cycle.

Identifying the Effects of Monetary Policy Shocks on Exchange Rates Using High Frequency Data

This paper proposes a new approach to identifying the effects of monetary policy shocks in an international vector autoregression. Using high-frequency data on the prices of Fed Funds futures contracts, we measure the impact of the surprise component of the FOMC-day Federal Reserve policy decision on financial variables, such as the exchange rate and the foreign interest rate. We show how this information can be used to achieve identification without having to make the usual strong assumption of a recursive ordering.

Identifying the effects of monetary policy shocks on exchange rates using high frequency data

This paper proposes a new approach to identifying the effects of monetary policy shocks in an international vector autoregression. Using high-frequency data on the prices of eurodollar contracts, we measure the impact of the surprise component of the FOMC-day Federal Reserve policy decision on financial variables, such as the exchange rate and the foreign interest rate. We show how this information can be used to achieve identification without having to make the usual strong assumption of a recursive ordering. JEL Classification: C32, E52, F30Exchange Rates, High Frequency Data, Identification, monetary policy, Vector autoregression

What does Monetary Policy do to Long-Term Interest Rates at the Zero Lower Bound?

The federal funds rate has been stuck at the zero bound for over two years and the Fed has turned to unconventional monetary policies, such as large scale asset purchases to provide stimulus to the economy. This paper uses a structural VAR with daily data to identify the effects of monetary policy shocks on various longer-term interest rates during this period. The VAR is identified using the assumption that monetary policy shocks are heteroskedastic: monetary policy shocks have especially high variance on days of FOMC meetings and certain speeches, while there is nothing unusual about these days from the perspective of any other shocks to the economy. A complementary high-frequency event-study approach is also used. I find that stimulative monetary policy shocks lower Treasury and corporate bond yields, but the effects die off fairly fast, with an estimated half-life of about two months.

Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach

Employing a large number of real and financial indicators, we use Bayesian Model Averaging (BMA) to forecast real-time measures of economic activity. Importantly, the predictor set includes option-adjusted credit spread indexes based on bond portfolios sorted by maturity and credit risk as measured by the issuer’s “distance-to-default.” The portfolios are constructed directly from the secondary market prices of outstanding senior unsecured bonds issued by a large number of U.S. corporations. Our results indicate that relative to an autoregressive benchmark, BMA yields consistent improvements in the prediction of the growth rates of real GDP, business fixed investment, industrial production, and employment, as well as of the changes in the unemployment rate, at horizons from the current quarter (i.e., “nowcasting”) out to four quarters hence. The gains in forecast accuracy are statistically significant and economically important and owe exclusively to the inclusion of our portfolio credit spreads in the set of predictors—BMA consistently assigns a high posterior weight to models that include these financial indicators.

Basins of attraction in forced systems with time-varying dissipation

We consider dissipative periodically forced systems and investigate cases in which having information as to how the system behaves for constant dissipation may be used when dissipation varies in time before settling at a constant final value. First, we consider situations where one is interested in the basins of attraction for damping coefficients varying linearly between two given values over many different time intervals: we outline a method to reduce the computation time required to estimate numerically the relative areas of the basins and discuss its range of applicability. Second, we observe that sometimes very slight changes in the time interval may produce abrupt large variations in the relative areas of the basins of attraction of the surviving attractors: we show how comparing the contracted phase space at a time after the final value of dissipation has been reached with the basins of attraction corresponding to that value of constant dissipation can explain the presence of such variations. Both procedures are illustrated by application to a pendulum with periodically oscillating support.Comment: 16 pages, 13 figures, 7 table

Refining Set-Identification in VARs through Independence

Identification in VARs has traditionally mainly relied on second moments. Some researchers have considered using higher moments as well, but there are concerns about the strength of the identification obtained in this way. In this paper, we propose refining existing identification schemes by augmenting sign restrictions with a requirement that rules out shocks whose higher moments significantly depart from independence. This approach does not assume that higher moments help with identification; it is robust to weak identification. In simulations we show that it controls coverage well, in contrast to approaches that assume that the higher moments deliver point-identification. However, it requires large sample sizes and/or considerable non-normality to reduce the width of confidence intervals by much. We consider some empirical applications. We find that it can reject many possible rotations. The resulting confidence sets for impulse responses may be non-convex, corresponding to disjoint parts of the space of rotation matrices. We show that in this case, augmenting sign and magnitude restrictions with an independence requirement can yield bigger gains