@techreport{Mittnik2004Forecasting,
abstract = {The paper illustrates and evaluates a Kalman filtering method for forecasting German real
GDP at monthly intervals. German real GDP is produced at quarterly intervals but analysts
and decision makers often want monthly GDP forecasts. Quarterly GDP could be regressed
on monthly indicators, which would pick up monthly feedbacks from the indicators to GDP,
but would not pick up implicit monthly feedbacks from GDP onto itself or the indicators. An
efficient forecasting model which aims to incorporate all significant correlations in monthlyquarterly
data should include all significant monthly feedbacks. We do this with estimated
VAR(2) models of quarterly GDP and up to three monthly indicator variables, estimated
using a Kalman-filtering-based maximum-likelihood estimation method. Following the
method, we estimate monthly and quarterly VAR(2) models of quarterly GDP, monthly
industrial production, and monthly, current and expected, business conditions. The business
conditions variables are produced by the Ifo Institute from its own surveys. We use early insample
data to estimate models and later out-of-sample data to produce and evaluate
forecasts. The monthly maximum-likelihood-estimated models produce monthly GDP
forecasts. The Kalman filter is used to compute the likelihood in estimation and to produce
forecasts. Generally, the monthly German GDP forecasts from 3 to 24 months ahead are
competitive with quarterly German GDP forecasts for the same time-span ahead, produced
using the same method and the same data in purely quarterly form. However, the present
mixed-frequency method produces monthly GDP forecasts for the first two months of a
quarter ahead which are more accurate than one-quarter-ahead GDP forecasts based on the
purely-quarterly data. Moreover, quarterly models based on purely-quarterly data generally
cannot be transformed into monthly models which produce equally accurate intra-quarterly
monthly forecasts.},
author = {Stefan Mittnik and Peter A. Zadrozny},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {C32; E37; 330; mixed-frequency data; VAR models; maximum-likelihood estimation; Kalman filter; Konjunkturprognose; Prognoseverfahren; VAR-Modell; Maximum-Likelihood-Methode; Zustandsraummodell; Sch\"{a}tzung; Deutschland},
language = {eng},
number = {1203},
title = {Forecasting quarterly German GDP at monthly intervals using monthly IFO business conditions data},
type = {CESifo working papers},
url = {http://hdl.handle.net/10419/18842},
year = {2004}
}
