A set of state space models at an high disaggregation level to forecast Italian Industrial Production

Riccardo Corradini

MPRA Paper from University Library of Munich, Germany

Abstract: Normally econometric models that forecast Italian Industrial Production Index do not exploit pieces of information already available at time t+1 for its own main industry groupings. A new strategy is sketched here using state space models and aggregating the estimates to obtain improved results. The endogenous variables available at time t+1 are Consumption of Electricity, Compressed Natural Gas distributed on its own net, Production of Compressed Natural Gas, Registration of commercial vehicles for Italy, Germany, France and Spain. Unfortunately for the other main industry groupings there are not available variables not prone to high revisions. A new strategy exploiting univariate or bivariate state space models for these time series is used. The issue coming out from holidays taken during Tuesday or Friday will be tackled. How to handle in-sample forecast with different aggregating weights will be considered for the period before the first of January of 2010 where is impossible to use the same structure for the base year 2010.

Keywords: Industrial Production Index; forecasting; Vector Autoregressive Models; disaggregation; Kalman filter; unobserved components models (search for similar items in EconPapers)
JEL-codes: C43 C51 C53 Q47 (search for similar items in EconPapers)
Date: 2018-02-07, Revised 2018-02-12
New Economics Papers: this item is included in nep-ene
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