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In this paper, we examine the predictive ability of automatic and expert-rated media sentiment indicators for German inflation. We find that sentiment indicators are competitive in providing inflation forecasts against a large set of common macroeconomic and financial predictors. Sophisticated linguistic sentiment algorithms and business cycle news rated by experts perform best and are superior to simple word-count indicators and autoregressive forecasts.

In light of the recent large swings in stock and housing prices accompanied by ample global liquidity, the role of monetary policy in the build-up of asset price bubbles has been questioned. This paper will contribute to the debate whether central banks can and should stronger "lean against the wind" of emerging bubbles. Against this background, the paper will reevaluate if new advances in real-time bubble detection, as brought forward by Phillips et al. (2011), can timely detect bubble emergences and collapses. Here, the paper suggests a combination approach of different bubble indicators to account for the uncertainty around start and end dates of asset price bubbles. Additionally, the paper will then investigate if these indicators carry predictive content for inflation, output growth and recession events when the real-time availability of all variables is considered. It finds that a combination approach of asset price bubbles is well suited to detect the most common stock and house price bubbles in the U.S. and shows that this indicator can improve output forecasts, however, only when the real-time availability of real variables is respected.

We produce predictions of the previous, the current, and the next quarter of NorwegianGDP. To this end, we estimate a Bayesian Dynamic Factor model on a panel of 14variables (all followed closely by market operators) ranging from 1990 to 2011. By meansof a real time forecasting exercise we show that the Bayesian Dynamic Factor Model outperformsa standard benchmark model, while it performs equally well than the BloombergSurvey. Additionally, we use our model to produce annual GDP growth rate nowcast. Weshow that our annual nowcast outperform the Norges Bank’s projections of current yearGDP., info:eu-repo/semantics/published

This paper examines which macroeconomic and financial variables are most informative for the federal funds target rate decisions made by the Federal Open Market Committee (FOMC) from a forecasting perspective. The analysis is conducted for the FOMC decision during the period January 1990 - June 2008, using dynamic ordered probit models with a Bayesian endogenous variable selection methodology and real-time data for a set of 33 candidate predictor variables. We find that indicators of economic activity and forward-looking term structure variables as well as survey measures have most predictive ability. For the full sample period, in-sample probability forecasts achieve a hitrate of 90 percent. Based on out-of-sample forecasts for the period January 2001 - June 2008, 82 percent of the FOMC decisions are predicted correctly.

Real-time estimates of output gaps and inflation trends 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 real-time output gap and inflation trend estimates. Our findings indicate that annual revisions to output and inflation data are in part predictable based on their past vintages.

Many macroeconomic series such as US 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 approach is compared to other ways of making use of monthly data to predict quarterly output growth. The MIDAS specification used in the comparison employs 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 of exploiting monthly data compared to alternative methods. We also exploit the best method to use the monthly vintages of the indicators for real-time forecasting.

Economic policy makers, international organisations and private-sector forecasters commonly use short-term forecasts of real GDP growth based on monthly indicators, such as industrial production, retail sales and confidence surveys. An assessment of the reliability of such tools and of the source of potential forecast errors is essential. While many studies have evaluated the size of forecast errors related to model specifications and unavailability of data in real time, few have provided a complete assessment of forecast errors, which should notably take into account the impact of data revision. This paper proposes to bridge this gap. Using four years of data vintages for euro area conjunctural indicators, the paper decomposes forecast errors into four elements (model specification, erroneous extrapolations of the monthly indicators, revisions to the monthly indicators and revisions to the GDP data series) and assesses their relative sizes. The results show that gains in accuracy of forecasts achieved by using monthly data on actual activity rather than surveys or financial indicators are offset by the fact that the former set of monthly data is harder to forecast and less timely than the latter set. While the results presented in the paper remain tentative due to limited data availability, they provide a benchmark which future research may build on.