Factor‐Augmented Bridge Models (FABM) and Soft Indicators to Forecast Italian Industrial Production

Abstract

This paper presents a new forecasting approach straddling the conventional methods applied to the Italian industrial production index. Specifically, the proposed method treats factor models and bridge models as complementary ingredients feeding a unique model specification. We document that the proposed approach improves upon traditional bridge models by making efficient use of the information conveyed by a large amount of survey data on manufacturing activity. Different factor algorithms are compared and, under the provision that a large estimation window is used, partial least squares outperform principal component-based alternatives.

Using social network and semantic analysis to analyze online travel forums and forecast tourism demand

“…To estimate the forecasting model ( 3) we followed a three-steps algorithm as suggested by Girardi, Guardabascio and Ventura [82]. The Factor Augmented Autoregressive Model (FAAR) is obtained estimating the Autoregressive Model (AR) and subsequently constructing the Factor Model (FM) on SSI indicators, to capture possible useful information not included in the AR model.…”

Financial modeling trends for production companies in the context of Industry 4.0

“…Nevertheless, different indicators' publication time, frequency, and appropriate indicators' selection are the arising problems. A bridge model and a factory-based model were used in forecasting the industrial production index (IPI) in Italy (Girardi et al, 2016). IPI is defined as a high-frequency manufacturing indicator that is extremely important to the whole business cycle fluctuations and in forecasting shortterm periods of GDP (Bulligan et al, 2010; Boero & Lampis, 2016).…”

“…PMI's variables are as follows: in manufacturing -output, new orders, employment, input costs, output prices, backlogs of work, export orders, the number of purchases, suppliers' delivery times, stocks of purchases, stocks of finished goods, future outputs; in services -business activity, incoming new business, input costs, prices charged, business outstanding, business expec-tations (Markit Economics, 2017). Bridge model is characterized by fewer indicators and more space for interpretation possibilities, unlike the factory-based model that assesses a huge amount of data and has limited economic interpretation possibility (Girardi et al, 2016). Nonlinear data, like seasonality, is a frequent forecasting problem.…”

A Set of State–Space Models at a High Disaggregation Level to Forecast Italian Industrial Production

“…The Supplementary Materials presents two videos (see Supplementary Video S1 and Supplementary Video S2) that show the evolution of the p-values of the multivariate Q-statistic (see Gómez [12]) applied to vectorized residuals and squared vectorized residuals over 36 lags applied to the basic model. A similar model that does not exploit public available data (i.e., railway transportation data provided by Trenitalia Cargo and monthly level of temperatures) was described by Ventura et al (see [25]) and does not exploit Kalman filtering. Their model is summarized by the following nested models:…”

“…In this way, it focuses on ninety per cent of all the information available on the web for the whole index from January 2001 up to now. The factors are not extracted and all the raw data on surveys are inserted as they are published (not seasonally adjusted, as by Costantini [5] or log-linearised as by Bruno et al [4] and, again, by Ventura et al [25]). The Italian Industrial Production Index, the Truck Toll Index and the consumption of electricity are expressed as logarithms.…”