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.Bridge models, Dynamic factor models, real-time data flow model

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.Bridge models ; Dynamic factor models ; real-time data flow.

Short-term forecasting of GDP using large monthly datasets - A pseudo real-time forecast evaluation exercise. NBB Working Papers. No. 133, 17 June 2008

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

Real business cycle model for Lithuanian economy

This paper develops and calibrates a small open economy dynamic stochastic general equilibrium model for Lithuania. Approximate solutions together with policy functions are calculated using local and global numerical methods. The impact of di®erent methods to approximate solutions are assessed according sec- ond moments and Euler equation residuals

Real business cycle model for Lithuanian economy

This paper develops and calibrates a small open economy dynamic stochastic general equilibrium model for Lithuania. Approximate solutions together with policy functions are calculated using local and global numerical methods. The impact of di®erent methods to approximate solutions are assessed according sec- ond moments and Euler equation residuals