Ana Beatriz Galvão

Macroeconomic Forecasting With Mixed-Frequency Data

Many macroeconomic series, such as U.S. real output growth, are sampled quarterly, although potentially useful predictors are often observed at a higher frequency. We look at whether a mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth. The MIDAS specification used in the comparison uses a novel way of including an autoregressive term. We find that the use of monthly data on the current quarter leads to significant improvement in forecasting current and next quarter output growth, and that MIDAS is an effective way to exploit monthly data compared with alternative methods.

Chapter 2 Combining Predictors & Combining Information in Modelling: Forecasting US Recession Probabilities and Output Growth

Abstract We consider combining forecasts versus combining information in modelling, in the context of predicting regime probabilities and output growth in the US. The simple models whose forecasts we combine each use one of the leading indicators that comprise the Conference Board Composite Leading Indicator as explanatory variables. Combining this information set in modelling is achieved by using a relatively simple model selection strategy. For predicting output growth, our findings support pooling the forecasts of the single-indicator models, while the results are more mixed for predicting recessions and recession probabilities. Our results are not affected by allowing for non-linearities in the output growth regressions, although issues to do with the vintages of data used to estimate the models and evaluate the forecasts are important.

Improving Real-Time Estimates of Output and Inflation Gaps With Multiple-Vintage Models

Real-time estimates of output gaps and inflation gaps differ from the values that are obtained using data available long after the event. Part of the problem is that the data on which the real-time estimates are based is subsequently revised. We show that vector-autoregressive models of data vintages provide forecasts of post-revision values of future observations and of already-released observations capable of improving estimates of output and inflation gaps in real time. Our findings indicate that annual revisions to output and inflation data are in part predictable based on their past vintages. This article has online supplementary materials.

Volatilidade e causalidade: evidências para o mercado à vista e futuro de índice de ações no Brasil

The objective of this paper is to evaluate the evidence of causality between the future and spot markets of stocks in Brazil, verifying if the former has unstabilized the latter, thus increasing its volatility. We analyse a period that includes external shocks and changes in the exchange rate policy. Causality from future to spot markets is tested using both the volatilities cross-correlogram and a bivariate GARCH model. The results allow us to state that the spot market leads the information transmission of the market. Thus the future market does not cause increase of volatility in the spot market.

Predicting Early Data Revisions to U.S. GDP and the Effects of Releases on Equity Markets

The effects of data uncertainty on real-time decision-making can be reduced by predicting data revisions to U.S. GDP growth. We show that survey forecasts efficiently 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 the data revision implied by the release of the third estimate. We use forecasting models to measure the impact of surprises in GDP announcements on equity markets, and to analyze the effects of anticipated future revisions on announcement-day returns. We show that the publication of better than expected third-release GDP figures provides a boost to equity markets, and if future upward revisions are expected, the effects are enhanced during recessions.

Financial Stress Regimes and the Macroeconomy

We identify financial stress regimes using a model that explicitly links financial variables with the macroeconomy. The financial stress regimes are identified using a large unbalanced panel of financial variables with an embedded method for variable selection and, empirically, are strongly correlated with NBER recessions. The empirical results on the selection of financial variables support the use of credit spreads to identify asymmetries in the responses of economic activity and prices to financial shocks. We use a novel factor-augmented vector autoregressive model with smooth regime changes (FASTVAR). The unobserved financial factor is jointly estimated with the parameters of a logistic function that describes the probabilities of the financial stress regime over time.

The effects of the monetary policy stance on the transmission mechanism

Abstract This paper contributes to the literature on changes in the transmission mechanism of monetary policy by introducing a model whose parameter evolution explicitly depends on the stance of monetary policy. The model, a structural break endogenous threshold VAR, also captures changes in the variance of shocks, and allows for a break in the parameters at an estimated time. We show that the transmission is asymmetric depending on the extention of the deviation of the actual policy rate from the one required by the Taylor rule. When the policy stance is tight – actual rate is higher than the one implied by the Taylor rule – contractionary shocks have stronger negative effects on output and prices.

Data Revisions and Real-time Probabilistic Forecasting of Macroeconomic Variables

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 uncertainty. This method is applied to density forecasting in the presence of time-varying heteroscedasticity, and is shown in principle to improve real-time density forecasts. We show that the magnitude of the expected improvements depends on the nature of the data revisions.