The following is a paper concerned with the possibility of recording (to a very limited extent) the economic data on inflation in Real-Time. In Transfinancial Economics it may well become possible to record changes in inflation to the "fullest possible" extent in Real-Time. http://www.p2pfoundation.net/Transfinancial_Economics..
TITLE OF PAPER
Nowcasting GDP and Inflation: The Real-Time
Informational Content of Macroeconomic Data Releases¤
Domenico Giannone, ECARES and European Central Bank,
Lucrezia Reichlin, European Central Bank and CEPR
David Small, Board of Governors, Federal Reserve
This version: September 2005
Monetary policy decisions in real time are based on assessments of current and future
economic conditions using incomplete data. Since most data are released with a lag and
are subsequently revised, the reconstruction of current-quarter GDP, inflation and other
key variables is an important task for central banks and one to which they devote a
considerable amount of resources. Current-quarter numbers are also important because,
in the short-run, there is a greater degree of forecastability than in the long run. For
example, Giannone, Reichlin, and Sala (2004) (GRS from now on) document that, in
forecasting GDP beyond the ¯rst quarter, the forecasts of the Federal Reserve sta® and
of standard statistical models do not perform better than that of a constant growth
rate. Current-quarter estimates are particularly relevant because they are inputs for
model-based longer term forecasting exercises.
Nowcasts are constructed at central banks using both simple models and qualitative
judgment. Those exercises involve the analysis of a large amount of information and a
judgment on the relative weight to attribute to various data series. As new information
becomes available throughout the month, the nowcasts and forecasts may be adjusted in
response to changes in both the values of the data series and the implicit relative weights
applied to those series. Typically, central banks and markets pay particular attention
to certain data releases either because they arrive earlier, and can therefore convey
news on key variables such as GDP, or because they are inputs in their estimates (e.g.
industrial production or the Employment Report for GDP). In principle, however, any
release, no matter at what frequency, may potentially affect current-quarter estimates
and their precision. From the point of view of the short-term forecaster, there is no
reason to throw away any information.
This paper provides a framework that formalizes the updating of the nowcast and
forecast of output and in°ation as data are released throughout the month and that
can be used to evaluate the marginal impact of new data releases on the precision of the
now/forecast as well as the marginal contribution of different groups of variables. In
the empirics, we focus on the nowcast and we use intra-month releases of monthly time
series to construct (possibly) progressively more accurate current-quarter estimates.
Our approach allows us to consider a large number of monthly time series (in principle
all the potentially relevant ones) within the same forecasting model. Moreover, the
model takes into account the non-synchronicity of the releases by exploiting vintages
of panel data which are unbalanced at the end of the sample.
FOR MORE DETAILS OF THE ABOVE. READ LINK BELOW.