Ana Beatriz C. Galvão

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Although many macroeconomic series such as US real output growth are sampled quarterly, many potentially useful predictors are observed at a higher frequency. We look at whether a recently developed mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth and inflation. We carry out a number of related real-time forecast(More)
This paper aims at improving the understanding of the transmission of shocks across countries and how this transmission may have changed over time. By employing a model that allows for parameter changes across regimes, we show that transmission of shocks from the US to European countries may depend on the values of transition variables such as financial(More)
In this paper we study the issue of economic integration across borders for the case of Poland’s reunification after the First World War. Using a pooled regression approach and a threshold cointegration framework we find that the Polish interwar economy can be regarded as integrated with some restrictions. Moreover, a significant negative impact of the(More)
We investigate the e¤ects of uncertainty shocks on unemployment dynamics in the post-WWII U.S. recessions via non-linear (Smooth-Transition) VARs. The relevance of uncertainty shocks is found to be much larger than that predicted by standard linear VARs in terms of i) magnitude of the reaction of the unemployment rate to such shocks, ii) welfare costs(More)
Quantile forecasts are central to risk management decisions because of the widespread use of Value-at-Risk. A quantile forecast is the product of two factors: the model used to forecast volatility, and the method of computing quantiles from the volatility forecasts. In this paper we calculate and evaluate quantile forecasts of the daily exchange rate(More)
We show how to improve the accuracy of real-time forecasts from models that include autoregressive terms by estimating the models on ‘lightly-revised’data instead of using data from the latest-available vintage. Forecast accuracy is improved by reorganizing the data vintages employed in the estimation of the model in such a way that the vintages used in(More)
The effects of data uncertainty on real-time decision-making can be reduced by predicting data revisions to US GDP growth. We show that survey forecasts effi ciently predict the revision implicit in the second estimate of GDP growth, but that forecasting models incorporating monthly economic indicators and daily equity returns provide superior forecasts of(More)
Macroeconomic data are subject to revision over time as later vintages are released, yet the usual way of generating real-time out-of-sample forecasts from models effectively makes no allowance for this form of data uncertainty. We analyze a simple method which has been used in the context of point forecasting, and does make an allowance for data(More)