Abstract
Normally, econometric models that forecast the Italian Industrial Production Index do not exploit information already available at time t + 1 for their own main industry groupings. The new strategy proposed here uses state–space models and aggregates the estimates to obtain improved results. The performance of disaggregated models is compared at the same time with a popular benchmark model, a univariate model tailored on the whole index, with persistent not formally registered holidays, a vector autoregressive moving average model exploiting all information published on the web for main industry groupings. Tests for superior predictive ability confirm the supremacy of the aggregated forecasts over three steps horizon using absolute forecast error and quadratic forecast error as a loss function. The datasets are available online.
Highlights
Forecasting industrial production can be a difficult task, but forecasting the sub-components of industrial production at a high disaggregation level can be even more challenging for researchers
The first set exploits quantitative data when they are available for the whole index or for its disaggregated components, using regression methods or seemingly unrelated equations methods as in the work of Bruno et al [4]
The second set exploits so-called common factors that summarize a big set of survey data about industrial production as predictors
Summary
Forecasting industrial production can be a difficult task, but forecasting the sub-components of industrial production at a high disaggregation level can be even more challenging for researchers. It shows how it is possible to cast into a state-space model (without imposing restrictions) a vector autoregressive moving average applied to the whole index that exploits as endogenous variables all freely available data at t + 1 on the web concerning Main Industry Groupings disaggregation from January 2001 to December 2018. For the Industrial Production Index of Non-durable Goods, the seemingly unrelated time series equations are applied to the cumulated monthly data about the industrial natural gas used. For t + 2 and t + 3, a naive autoregressive model of order three using first seasonal weekly differences applied to daily data about consumption and thermoelectric, national natural gas production and transportation allows us to have more reliable estimates for production of electricity, extraction of petroleum and natural gas and distribution of gaseous fuels through mains at time t + 2 and t + 3 (see the Appendixes A and B for further details). VDA a Publication corresponds to amounts of days before the official release on ISTAT website
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