Altissimo, F., D. Marchetti and G. Oneto, 1999, “The Italian Business Cycle: New Coincident and Leading Indicators and some Stylised Facts,” ISAE, Documento di Lavoro n. 8.
Bikker, J.A., 1998, “Inflation Forecasting for Aggregates of the EU-7 and EU-14 with Bayesian VAR Models,” Journal of Forecasting, 17, pp. 145–160.
Cogley, T., and J.M. Nason, 1995, “Output Dynamics in Real-Business-Cycle Models,” American Economic Review, vol. 85, pp. 492–511, June.
Emerson, R., and D.F. Hendry, 1996, “An Evaluation of Forecasting Using Leading Indicators,” Journal of Forecasting, 15, pp. 271–291.
Gaiotti, E., 1999, “The Transmission of Monetary Policy Shocks in Italy, 1967-97,” Temi di Discussione del Servizio Studi, Banca d’Italia, n. 363.
Klein, L.R., and E. Sojo, 1989, “Combination of High and Low Frequency Data in Macroeconometric Models,” in Economics in Theory and Practice: An Eclectic Approach, ed. by L.R. Klein and J. Marquez, Dordrecht: Kluwer, 1989, pp. 3-16.
Litterman, R., 1986, “Forecasting with Bayesian Vector Autoregressions—Five Years of Experience,” Journal of Business and Economic Statistics, 4, 1, pp. 25-38.
Parigi, G., and G. Schlitzer, 1995, “Quarterly Forecasts of the Italian Business Cycle by Means of Monthly Economic Indicators,” Journal of Forecasting, 14, pp. 117-141.
Rathjens, P. and R. Robins, 1993, “Forecasting Quarterly Data Using Monthly Information,” Journal of Forecasting, 12, pp. 321-330.
Robertson, P., and E.W. Tallman, 1999, “Vector Autoregressions: Forecasting and Reality,” Federal Reserve Bank of Atlanta Economic Review, 1 st quarter, pp. 4-17.
Sims, C.A., J. Stock, and M. Watson, 1990, “Inference in Linear Time Scries Models with Some Unit Roots,” Econometrica, 58, pp. 113-44.
Author’s affiliation: London School of Economics. This paper was written while I was a summer intern at the Southern European I Division of the IMF, which I thank for hospitality and support. I am indebted to Messrs. Decressin, Krueger, and Monteforte for helpful comments and suggestions.
Of course, another common type is the so-called judgement-based forecast. This type of forecast is predominantly the result of a particular forecaster’s skill at reading the economic tea leaves, interpreting anecdotal evidence, and his or her experience at spotting empirical regularities in the economy. There are a number of obvious shortcomings in this, as highlighted by Robertson and Tallman (1999): (i) their accuracy can be evaluated only after a track record is established; (ii) given the element of subjectivity in such forecasts, changes in the forecasting staff will affect the accuracy of these forecasts; (iii) they are impossible to replicate or validate by independent forecasters; (iv) they normally do not come with a probabilistic assessment of a range of alternative outcomes; (v) they are deemed unable to predict recessions or strong booms.
Of course, the distinction between model-based and judgement-based forecasts cannot be pushed too far. “Successful model specifications also depend heavily on the skill and ingenuity of particular individuals. No model can be left on automatic pilot for long,” (Robertson and Tallman, 1999, page 21).
Exchange rate and interest rate data are available in real time. The industrial production index is released from the Italian Statistical Office (ISTAT) on a monthly basis with a delay of about 45 days (i.e., industrial production data for month x are released mid-month x+2). This compares with the 80 days delay for the first detailed QNA estimates published by ISTAT (that break GDP down into its demand components); from November 2000, ISTAT also publishes a preliminary GDP first estimate 45 days after the end of the quarter. This first estimate is obtained using “statistical techniques of integration” (ISTAT, Stima Preliminare del PIL, third-quarter 2000) and normally subject to bigger revisions than the following QNA estimates. Another potentially important coincident indicator for Italian GDP, quarterly German GDP, is released with a delay of 60 days. There are on average 20 days in a quarter in which one might use current quarter information on German GDP in order to estimate the Italian GDP figure.
It is interesting to note that a persistency pattern holds instead for yearly Italian GDP growth, which shows a serial correlation of 41 percent year on year.
The EU monthly business survey comprises qualitative questions aimed at obtaining information on the current situation and on the short-term (three–four months) trend of the main firm variables (such as order-books, production, finished products stocks, selling prices) as well as on expectations on the general economic trend.
The above-mentioned indicators outperform the consumer confidence indicator in terms of its cyclical properties and its forecasting power.
That is, the Root Mean Square Error (RMSE) of the supply model is lower than the corresponding RMSE of the demand side model. See footnote 11 for a definition.
Whenever Industrial Production is not available for the entire quarter, the monthly series is extrapolated with an ARMA(p,q) model of the form (p=||l,2,3,6,12||,q=||l||).
Forni, Hallin, Lippi and Reichlin (2000) reconcile dynamic principal components analysis with dynamic factor analysis in order to extract indicators from a large panel of economic variables (many variables for many countries). The procedure is used to estimate coincident and leading indicators for the euro area.
See footnote 11 for a definition of the RMSE.
Let R be the actual realisation of the GDP, F be its one step ahead forecast, and T the time horizon for the forecasts. The mean forecast error (MFE) is (1/T) Σ(F-R), whereas the root mean square error (RMSE) is the square root of (1/T) Σ(F-R)2. The mean forecast error is a measure of unbiasedness, whereas the RMSE is a measure of efficiency. Unbiasedness is a necessary condition for efficiency.
Monthly variables are all projected with an ARMA of the form AR(1,2,3,6,12), MA(1). Details are available from the author upon request.
The coefficient estimates were updated using a Kalman filter algorithm: that is, at each point in time the model was estimated through some period before the end of the data set and its performance was evaluated comparing actual and forecast data.