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

Riccardo Corradini
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Riccardo Corradini: DIPS Department, Division for Data Analysis and Economic, Social and Environmental Research, ISTAT Italian National Institute of Statistics, 00198 Rome, Italy

J, 2019, vol. 2, issue 4, 1-53

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.

Keywords: Industrial Production Index; forecasting; disaggregation; Kalman filter (search for similar items in EconPapers)
JEL-codes: I1 I10 I12 I13 I14 I18 I19 (search for similar items in EconPapers)
Date: 2019
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