Working papers results

In this paper we analyze a novel dataset of Business and Consumer Surveys, using dynamic
factor techniques, to produce composite coincident indices (CCIs) at the sectoral
level for the European countries and for Europe as a whole. Few CCIs are available
for Europe compared to the US, and most of them use macroeconomic variables and
focus on aggregate activity. However, there are often delays in the release of macroeconomic
data, later revisions, and differences in the definition of the variables across
countries, while the surveys are timely available, not subject to revision, and fully comparable
across countries. Moreover, there are substantial discrepancies in activity at
the sectoral level, which justifies the interest in a sectoral disaggregation. Compared
to the Confidence Indicators produced by the European Commission, which are based
on a simple average of the aggregate survey answers, we show that factor based CCIs,
using survey answers at a more disaggregate level, produce higher correlation with the
reference series for the majority of sectors and countries.

Monitoring the current status of the economy is quite relevant for policy making
but also for the decisions of private agents, consumers and firms. Since it is difficult
to identify a single variable that provides a good measure of current economic
conditions, it can be preferable to consider a combination of several coincident indicators,
i.e., a composite coincident index (CCI). In this paper, we review the main
statistical techniques for the construction of CCIs, propose a new pooling-based
method, and apply the alternative techniques for constructing CCIs for the largest
European countries in the euro area and for the euro area as a whole. We find that
different statistical techniques yield comparable CCIs, so that it is possible to reach
a consensus on the status of the economy.

This paper addresses the issue of forecasting the term structure.
We provide a unified state-space modelling framework that encom-
passes different existing discrete-time yield curve models. within such
framework we analyze the impact on forecasting performance of two
crucial modelling choices, i.e. the imposition of no-arbitrage restric-
tions and the size of the information set used to extract factors. Using
US yield curve data, we find that: a. macro factors are very useful in
forecasting at medium/long forecasting horizon; b. financial factors
are useful in short run forecasting; c. no-arbitrage models are effec-
tive in shrinking the dimensionality of the parameter space and, when
supplemented with additional macro information, are very effective in
forecasting; d. within no-arbitrage models, assuming time-varying risk
price is more favorable than assuming constant risk price for medium
horizon-maturity forecast when yield factors dominate the informa-
tion set, and for short horizon and long maturity forecast when macro
factors dominate the information set; e. however, given the complex-
ity and the highly non-linear parameterization of no-arbitrage models,
it is very difficult to exploit within this type of models the additional
information offered by large macroeconomic datasets.

Empirical investigations of the effects of fiscal policy shocks share
a common weakness: taxes, government spending and interest rates
are assumed to respond to various macroeconomic variables but not
to the level of the public debt; moreover the impact of fiscal shocks
on the dynamics of the debt-to-GDP ratio are not tracked. We ana-
lyze the effects of fiscal shocks allowing for a direct response of taxes,
government spending and the cost of debt service to the level of the
public debt. We show that omitting such a feedback can result in
incorrect estimates of the dynamic effects of fiscal shocks. In par-
ticular the absence of an effect of fiscal shocks on long-term interest
rates-a frequent finding in research based on VAR's that omit a debt
feedback-can be explained by their mis-specification, especially over
samples in which the debt dynamics appears to be unstable. Using
data for the U.S. economy and the identification assumption proposed
by Blanchard and Perotti (2002) we reconsider the effects of fiscal
policy shocks correcting for these shortcomings.