Short-term estimates of euro area real GDP by means of monthly data

The first official data releases of quarterly real GDP for the euro area are published about eight weeks after the end of the reference quarters. Meanwhile, ongoing economic developments must be assessed from various, more readily available, monthly indicators. We examine in the context of univariate forecasting equations to what extent monthly indicators provide useful information for predicting euro area real GDP growth over the current and the next quarter. In particular, we investigate the performance of the equations under the case that the monthly indicators are only partially available within the quarter. For this purpose, we use time series models to forecast the missing observations of monthly indicators. We then examine GDP forecasts under different amounts of monthly information. We find that already a limited amount of monthly information improves the predictions for current-quarter GDP growth to a considerable extent, compared with ARIMA forecasts. JEL Classification: C22, C53bridge equations, Conjunctural analysis, incomplete monthly information

A look into the factor model black box: publication lags and the role of hard and soft data in forecasting GDP

We derive forecast weights and uncertainty measures for assessing the role of individual series in a dynamic factor model (DFM) to forecast euro area GDP from monthly indicators. The use of the Kalman filter allows us to deal with publication lags when calculating the above measures. We find that surveys and financial data contain important information beyond the monthly real activity measures for the GDP forecasts. However, this is discovered only, if their more timely publication is properly taken into account. Differences in publication lags play a very important role and should be considered in forecast evaluation. JEL Classification: E37, C53Dynamic Factor Models, filter weights, forecasting

Estimating and forecasting the euro area monthly national accounts from a dynamic factor model

We estimate and forecast growth in euro area monthly GDP and its components from a dynamic factor model due to Doz et al. (2005), which handles unbalanced data sets in an efficient way. We extend the model to integrate interpolation and forecasting together with cross-equation accounting identities. A pseudo real-time forecasting exercise indicates that the model outperforms various benchmarks, such as quarterly time series models and bridge equations in forecasting growth in quarterly GDP and its components. JEL Classification: E37, C53Dynamic Factor Models, Interpolation, nowcasting

Short-term forecasts of euro area GDP growth

Global financial integration unlocks a huge potential for international risk sharing. We examine the degree to which international equity holdings act as a risk sharing device in industrial and emerging economies. We split equity returns into investment income (dividend distribution) and capital gains to investigate which of the two channels delivers the largest potential for risk sharing. Our evidence suggests that net capital gains are a more potent channel of risk sharing. They behave in a countercyclical way, that is they tend to be positive (negative) when the domestic economy is growing more slowly (rapidly) than the rest of the world. Countries with more countercyclical net capital gains experience improved consumption risk sharing. The empirical analysis furthermore suggests that these risk sharing properties of net capital gains have increased through time, in particular in the 1990s and early-2000s, on the back of a declining equity home bias and financial market deepening. JEL Classification: E52, C33, C53consumption smoothing, Cross-Border Investment, International portfolio diversification, International risk sharing, Valuation effects

Short-term forecasting of GDP using large monthly datasets - a pseudo real-time forecast evaluation exercise

This paper evaluates different models for the short-term forecasting of real GDP growth in ten selected European countries and the euro area as a whole. Purely quarterly models are compared with models designed to exploit early releases of monthly indicators for the nowcast and forecast of quarterly GDP growth. Amongst the latter, we consider small bridge equations and forecast equations in which the bridging between monthly and quarterly data is achieved through a regression on factors extracted from large monthly datasets. The forecasting exercise is performed in a simulated real-time context, which takes account of publication lags in the individual series. In general, we find that models that exploit monthly information outperform models that use purely quarterly data and, amongst the former, factor models perform best. JEL Classification: E37, C53.Bridge models, Dynamic factor models, real-time data flow.

Prognose der österreichischen Wirtschaft 1997/98: Jahresmodell LIMA/97 ; Ökonometrisches Forschungsprogramm des Instituts für Höhere Studien

aus dem Inhaltsverzeichnis: Einleitung und Zusammenfassung; Die internationale Konjunktur; Die österreichische Außenwirtschaft; Perspektiven der Inlandskonjunktur; Monetäre Prognose

untersuchungen zur dynamischen struktur des wiener aktienmarkts

summary: the present paper investigates whether the conditional expectations of returns and volatilities of selected stocks traded on the vienna stock exchange can be explained by a common market factor. univariate garch(1,1)-estimates of conditional volatilities are compared with several modifications of the factor-arch-model (engle, ng & rothschild, 1990). it is shown that an extended one-factor-model allowing for a short-term memory of stock-specific shocks fits the data as well as univariate estimates . secondly, conditional expectations are estimated on the basis of a var(1)-process. in order to account for the heteroscedasticity of the returns wls-estimators are used. subsequently, the most predictible portfolio is constructed by a canonical analysis (box & tiao, 1977) of the var(1). the results depend strongly on the weights given to high volatile periods.

An Analysis of Austrian Output Growth at a Sectoral Level

Summary: Recently, Pesaran, Pierse & Lee (1992) argued that estimation of aggregate output persistence may be more accurate when based on disaggregated data. They proposed a multisectoral approach based on a VAR of sectoral output growth. This work applies their methodology to Austrian output data with particular emphasis on the long-run impact of foreign shocks. I compare persistence estimates based on a VAR-model of sectoral output growth rates with those obtained from univariate ARIMA modelsof aggregate output. Furthermore sectoral response to innovations in several exogenous variables is estimated. Innovations in European OECD GNP appear to have high persistence, whereas long-run influence of 3-month interest is not significant. Persistence estimates differ remarkably across sectors in accordance with informal characterizations of sectoral response. Influence of oil price and exchange rate innovations is statistically insignificant.