Understanding and Forecasting Aggregate and Disaggregate Price Dynamics

The issue of forecast aggregation is to determine whether it is better to forecast a series directly or instead construct forecasts of its components and then sum these component forecasts. Notwithstanding some underlying theoretical results, it is gener- ally accepted that forecast aggregation is an empirical issue. Empirical results in the literature often go unexplained. This leaves forecasters in the dark when confronted with the option of forecast aggregation. We take our empirical exercise a step further by considering the underlying issues in more detail. We analyse two price datasets, one for the United States and one for the Euro Area, which have distinctive dynamics and provide a guide to model choice. We also consider multiple levels of aggregation for each dataset. The models include an autoregressive model, a factor augmented autoregressive model, a large Bayesian VAR and a time-varying model with stochastic volatility. We find that once the appropriate model has been found, forecast aggrega- tion can significantly improve forecast performance. These results are robust to the choice of data transformation.

Are sectoral stock prices useful for predicting euro area GDP?

This paper evaluates how well sectoral stock prices forecast future economic activity compared to traditional predictors such as the term spread, dividend yield, exchange rates and money growth. The study is applied to euro area financial asset prices and real economic growth, covering the period 1973 to 2006. The paper finds that the term spread is the best predictor of future growth in the period leading up to the introduction of Monetary Union. After 1999, however, sectoral stock prices in general provide more accurate forecasts than traditional asset price measures across all forecast horizons.

Federal Reserve Information During the Great Moderation

Using data from the period 1970-1991, Romer and Romer (2000) showed that Federal Reserve forecasts of inflation and output were superior to those provided by commercial forecasters. In this paper, we show that this superior forecasting performance deteriorated after 1991. Over the decade 1992-2001, the superior forecast accuracy of the Fed held only over a very short time horizon and was limited to its forecasts of inflation. In addition, the performance of both the Fed and the commerical forecasters in predicting inflation and output, relative to that of "naive" benchmark models, dropped remarkably during this period.

Does global liquidity help to forecast US inflation?

We construct a measure of global liquidity using the growth rates of broad money for the G7 economies. Global liquidity produces forecasts of US inflation that are significantly more accurate than the forecasts based on US money growth, Phillips curve, autoregressive and moving average models. The marginal predictive power of global liquidity is strong at three years horizons. Results are robust to alternative measures of inflation.

Comparing Alternative Predictors Based on Large-Panel Factor Models

This paper compares the predictive ability of the factor models of Stock and Watson (2002) and Forni, Hallin, Lippi, and Reichlin (2005) using a “large” panel of US macroeconomic variables. We propose a nesting procedure of comparison that clarifies and partially overturns the results of similar exercises in the literature. As in Stock and Watson (2002), we find that efficiency improvements due to the weighting of the idiosyncratic components do not lead to significant more accurate forecasts. In contrast to Boivin and Ng (2005), we show that the dynamic restrictions imposed by the procedure of Forni, Hallin, Lippi, and Reichlin (2005) are not harmful for predictability. Our main conclusion is that for the dataset at hand the two methods have a similar performance and produce highly collinear forecasts.

Are some forecasters really better than others?

In any dataset with individual forecasts of economic variables, some forecasters will perform better than others. However, it is possible that these ex post differences reflect sampling variation and thus overstate the ex ante differences between forecasters. In this paper, we present a simple test of the null hypothesis that all forecasters in the US Survey of Professional Forecasters have equal ability. We construct a test statistic that reflects both the relative and absolute performance of the forecaster and use bootstrap techniques to compare the empirical results with the equivalents obtained under the null hypothesis of equal forecaster ability. Results suggest little support for the idea that the best forecasters are actually innately better than others, though there is evidence that a relatively small group of forecasters perform very poorly.Forecasting, Bootstrap

(Un)Predictability and Macroeconomic Stability

This paper documents a new stylized fact of the greater macroeconomic stability of the U.S. economy over the last two decades. Using 131 monthly time series, three popular statistical methods and the forecasts of the Federal Reserve's Greenbook and the Survey of Professional Forecasters, we show that the ability to predict several measures of inflation and real activity declined remarkably, relative to naive forecasts, since the mid-1980s. This break down in forecast ability appears to be an inherent feature of the most recent period and thus represents a new challenge for competing explanations of the `Great Moderation'.

Sectoral explanations of employment in Europe: the role of services

This paper investigates the determinants of the service sector employment share in the EU-15, for the aggregate service sector, four sub-sectors and twelve service sector branches. Recently, both Europe and the US have experienced an increase in the share of service-related jobs in total employment. Although converging in all European countries, a significant gap in the share of service jobs in Europe relative to the US persists. Understanding the main factors behind this gap is key to achieving higher employment levels in Europe. This paper focuses on the role of barriers in the EU-15 which may have hindered its ability to absorb labour supply and therefore to adjust efficiently to the sectoral reallocation of labour. We find that a crucial role in this process has been played by the institutional framework affecting flexibility in the labour market and by the mismatch between workers' skills and job vacancies.

The Fed and the Stock Market

The Fed closely monitors the stock market and the stock market continuously forms expectations about the Fed decisions. What does this imply for the relation between the fed funds rate and the S&P500? We find that the answer depends on the conditions prevailing on the financial market. During periods of high (low) volatility in asset price inflation an unexpected 5 fall in the stock market index implies that the Fed cuts the interest rate by 19 ($6$) basis points while an unanticipated policy tightening of 50 basis points causes a 4.7 (2.3) decline in the S&P500. The Fed reaction to asset price return is however statistically different from zero only in the high volatility regime, whereas the fall in asset price return following an interest rate rise is highly significant during normal times onlyasset price volatility, nonlinear policy, threshold SVAR, system GMM.

Now-casting Irish GDP

In this paper we present "now-casts" of Irish GDP using timely data from a panel data set of 41 different variables. The approach seeks to resolve two issues which commonly confront forecastors of GDP - how to parsimoniously avail of the many different series, which can potentially influence GDP and how to reconcile the within-quarterly release of many of these series with the quarterly estimates of GDP? The now-casts in this paper are generated by firstly, using dynamic factor analysis to extract a common factor from the panel data set and, secondly, through use of bridging equations to relate the monthly data to the quarterly GDP estimates. We conduct an out-of-sample forecasting simulation exercise, where the results of the now-casting exercise are compared with those of a standard benchmark model.