03 fév 2011
An Evaluation of the Growth and Unemployment Forecasts in the ECB Survey of Professional Forecasters
In this paper we provide a comprehensive evaluation of the euro area GDP growth and unemployment rate forecasts collected in the quarterly ECB Survey of Professional Forecasters (SPF) over the period 1999Q1–2008Q4. Our results suggest that while SPF forecasts generally appear to be slightly superior to naïve and purely backwardlooking benchmarks, forecast errors nonetheless exhibit a high degree of persistence. In addition, our analysis of the heterogeneity across individual SPF replies suggests that the broad pattern of the individual forecasts is essentially the same as that of the aggregate SPF results. This may refl ect a high degree of commonality in the information available (and not available) to panel members, thus leading them to "get it wrong" (or right) not only in the aggregate, but also individually. In particular, although a small number of forecasters perform substantially above average for some variables and horizons, none does so systematically for all variables and all horizons. Lastly, we have presented and assessed the information about forecast uncertainty provided by the SPF. In line with other studies based on the US SPF, disagreement among panel members does not appear to be a good proxy for overall macroeconomic uncertainty, i.e ., a high degree of consensus is not necessarily an indication of a low level of forecast uncertainty. Our analysis also suggests that, at the individual level, panel members may not fully internalise the overall level of macroeconomic uncertainty. For example, compared with the level of uncertainty indicated by the historical volatility of actual GDP growth and the unemployment rate, the perceptions of individual panel members about uncertainty appear quite low. This possible underestimation of overall uncertainty is much less severe when densities are aggregated across forecasters.
03 fév 2011
Application of Three Non-Linear Econometric Approaches to Identify Business Cycles in Peru
I use three non-linear econometric models to identify and analyze business cycles in the Peruvian economy for the period 1980:1-2008:4. The models are the Smooth Transition Autoregressive (STAR) model suggested by Teräsvirta (1994), the extended version of the MarkovSwitching model proposed by Hamilton (1989), and the plucking model of Friedman (1964, 1993). The results indicate strong rejection of the null hypothesis of linearity. The majority of models identify quarters concentrated around 1988-1989 and 1990-1991 as recession times. Other important events which happened in the Peruvian economy (natural disaster in 1983, effects of the Asian and Russian crises in 1990s, terrorist activities in 1980s) are not selected except as atypical observations. Most of models also identify the period 1995:1-2008:4 as a very long and stable period of moderate-high growth rates. From the perspective of the Peruvian economic history and from a statistical point of view, the MSIAH(3) model is the preferred model.
03 fév 2011
The Industrial Cycle of Milan as an Accurate Leading Indicator for the Italian Business Cycle
A coincident business cycle indicator for the Milan area is built on the basis of a monthly industrial survey carried out by Assolombarda, the largest territorial entrepreneurial association in Italy. The indicator is extracted from three time series concerning the production level and the domestic and foreign order book as declared by some 250 Assolombarda associates.
03 fév 2011
Forecasting Aggregated Time Series Variables
Aggregated times series variables can be forecasted in different ways. For example, they may be forecasted on the basis of the aggregate series or forecasts of disaggregated variables may be obtained fi rst and then these forecasts may be aggregated. A number of forecasts are presented and compared. Classical theoretical results on the relative effi ciencies of different forecasts are reviewed and some complications are discussed which invalidate the theoretical results. Contemporaneous as well as temporal aggregation are considered. JEL classifi cation : C22, C32 Key Words : Autoregressive moving-average process, contemporaneous aggregation, temporal aggregation, vector autoregressive moving-average process
03 fév 2011
Markov-Switching and the Ifo Business Climate
Business cycle indicators are used to assess the economic situation of countries or regions. They are closely watched by the public, but are not easy to interpret. Does a current movement of the indicator signal a turning point or not? With the help of Markov Switching Models movements of indicators can be transformed in probability statements. In this article, the most important leading indicator of the German business cycle, the Ifo Business Climate, is described by a Markov Switching Model. Real-time probabilities for the current business-cycle regime are derived and presented in an innovative way: as the Ifo traffi c lights. JEL Classifi cation: E32, C22 Keywords: Ifo business climate, growth cycle, turning points, Markov-switching