Cecilia Frale

FaMIDAS: A Mixed Frequency Factor Model with MIDAS Structure

In this paper a dynamic factor model with mixed frequency is proposed (FaMIDAS), where the past observations of high frequency indicators are used following the MIDAS approach. This structure is able to represent with richer dynamics the information content of the economic indicators and produces smoothed factors and forecasts. In addition, the Kalman filter is applied, which is particularly suited for dealing with unbalanced data set and revisions in the preliminary data. In the empirical application for the Italian quarterly GDP the short-term forecasting performance is evaluated against other mixed frequency models in a pseudo-real time experiment, also allowing for pooled forecast from factor models.

A Monthly Volatility Index for the US Economy

We estimate the monthly volatility of the US economy from 1968 to 2006 by extending the coincidentindex model of Stock and Watson (1991). Our volatility index, which we call VOLINX, hasfour applications. First, it sheds light on the Great Moderation. VOLINX captures the decrease in thevolatility in the mid-80s as well as the different episodes of stress over the sample period. In the 70sand early 80s the stagflation and the two oil crises marked the pace of the volatility whereas 09/11 is themost relevant shock after the moderation. Second, it helps to understand the economic indicators thatcause volatility. While the main determinant of the coincident index is industrial production, VOLINXis mainly affected by employment and income. Third, it adapts the confidence bands of the forecasts.In and out-of-sample evaluations show that the confidence bands may differ up to 50% with respect to amodel with constant variance. Last, the methodology we use permits us to estimate monthly GDP, whichhas conditional volatility that is partly explained by VOLINX. These applications can be used by policymakers for monitoring and surveillance of the stress of the economy.

New proposals for the quantification of qualitative survey data

In this paper we deal with several issues related to the quantification of business surveys. In particular, we propose and compare new ways of scoring the ordinal responses concerning the qualitative assessment of the state of the economy, such as the spectral envelope and cumulative logit unobserved components models, and investigate the nature of seasonality in the series. We conclude with an evaluation of the type of business cycle fluctuations that is captured by the qualitative surveys.

The US Housing Bust and Soaring Oil Prices: What Next for the World Economy?

This paper estimates the impact of the ongoing housing bust and oil price boom on the US and European economies. It finds that large house price movements (changes in construction investment) are useful to predict exceptionally bad and good times for the US economy, but not for most large European countries. In Europe housing market developments have led to extreme values of GDP, mainly in the UK, Spain and some Nordic countries. Exceptionally good or bad times are defined as realisations of the output gap (the difference between actual and trend GDP) that fall in the % tail of the distribution. Our definition of a ‘bad time’ thus does not necessarily imply a recession, which is officially defined as two consecutive quarters of falling GDP (and employment). A prolonged period of sub par growth could also lead to an equivalent output gap. Our model allows us to estimate the probability of the US and European economies experiencing exceptionally bad times. We find that the probability for the US is over 50% if one assumes that house prices will continue to fall throughout 2008. Adding the high oil price to the picture increases this probability to over 80%. For most European countries we find a much lower probability; except for Spain, where the probability of a large output gap will rise to over 85 % by the end of 2008 if house prices were to fall as much as in the US.

Do Privatizations Boost Household Shareholding? Evidence from Italy

It is believed that privatizations substantially contributed to boost stock markets through the 1980s and 1990s. However, trough which channels did that materialize? We test whether privatizations –improving households’ acquaintance with the risk and return characteristics of stocks through the massive accompanying advertising campaigns– boosted demand for stocks by enlarging the set of households willing to invest in shares. We use a unique micro-data set collected for a large sample of Italian households on Public Offerings (PO) during 1995-99, the climax of privatizations in Italy. We show that advertising increased the notoriety of the incoming PO at households, and through this furthered households’ propensity to subscribe that PO. Furthermore, the propensity to subscribe the incoming PO also increased as households became better informed about past privatizations. Thus, privatizations expanded households’ share participation in Italy.