real-time data

February 1, 2011

We use TVP models and real-time data to describe the evolution of the leading properties of the yield spread for output growth in five European economies and in the US over the last decades and until the third quarter of 2010. We evaluate the predictive performance of benchmark term-structure models and identify structural breaks in the marginal processes of term spreads and government bond yields to shed light on the dynamic characteristics of the yield curve. Econometric analysis shows that: (i) the predictive content of the term spread is not always significant over time and across countries; (ii) the spread significantly contributes to the forecast performance of simple growth regressions in Europe, but not in the US in recent years; (iii) the variance of the random shocks to the term spreads tends to fall in all countries. This decline is accompanied by vanishing leading properties from the mid-1990s. Such properties reappear after 2008.

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June 1, 2008

This paper argues that any assessment on the intentional stance of fiscal policy should be based upon all the information available to policymakers at the time of fiscal planning. In particular, real-time data on the discretionary fiscal policy "instrument", the structural primary balance, should be used in the estimation of fiscal policy reaction functions. In fact, the ex-post realization of discretionary fiscal measures may end up to be drastically different from what was planned by fiscal authorities in the budget law. When fiscal policy rules are estimated on real-time data, our results indicate that OECD countries often planned a counter-cyclical fiscal stance, especially during economic expansions, whereas conventional findings based on revised data point towards pro-cyclicality. This finding calls into question the effectiveness of discretionary fiscal policies to fine tune the business cycle, as (pro-cyclical) actual outcomes tend to deviate from (counter-cyclical) fiscal plans. Furthermore, we test whether threshold effects might be at play in the reaction of fiscal policy to the economic cycle and to public debt accumulation. It emerges that the intended cyclical behavior of fiscal policy is characterized by two regimes, and that the switch between them is likely to occur when output is close to its equilibrium level. On the other hand, the use of revised data does not allow to identify any threshold effect.

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October 1, 2005

This paper describes a method for calculating daily real-time estimates of the current state of the US economy. The estimates are computed from data on scheduled US macroeconomic announcements using an econometric model that allows for variable reporting lags, temporal aggregation, and other complications in the data. The model can be applied to find real-time estimates of GDP, inflation, unemployment or any other macroeconomic variable of interest. In this paper I focus on the problem of estimating the current level of and growth rate in GDP. I construct daily real-time estimates of GDP that incorporate public information known on the day in question. The real-time estimates produced by the model are uniquely suited to studying how perceived developments the macro economy are linked to asset prices over a wide range of frequencies. The estimates also provide, for the first time, daily time series that can be used in practical policy decisions.

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October 1, 2005

We document the empirical properties of revisions to major macroeconomic variables in the United States. Our findings suggest that they do not satisfy simple desirable statistical properties. In particular, we find that these revisions do not have a zero mean, which indicates that the initial announcements by statistical agencies are biased. We also find that the revisions are quite large compared to the original variables and they are predictable using the information set at the time of the initial announcement, which means that the initial announcements of statistical agencies are not rational forecasts. We also provide evidence that professional forecasters ignore this predictability.

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