Indirect estimation of the monthly transport turnover indicator in Italy

Barbara Guardabascio, Filippo Moauro and Luke Mosley ()
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Luke Mosley: University of Lancaster

Empirical Economics, 2024, vol. 67, issue 2, No 6, 566 pages

Abstract: Abstract The paper discusses the results of a selection of a set of monthly indicators to be used as predictors of the quarterly index of Italian service turnover. A mixed frequency approach based on sparse temporal disaggregation is used, which outperforms the classical methods of the Chow and Lin family, allowing both a high number of regressors by the LASSO method and stable estimates. The application refers to the turnover in transport, a sector strongly affected in 2020 by the dramatic movements due to the COVID-19 pandemic and the resurgence of inflation at the end of 2021. The monthly indicators are selected from 143 time series: 56 series of business surveys in transport about both the climate and frequency of the answers; 18 series from Assaeroporti about both passengers and cargo flights split by national and international routes; 69 series of monthly turnover in industry split by both sector of economic activity and reference market. The sample spans the months from January 2010 to December 2021 for both seasonally adjusted and unadjusted data. Several aspects of the estimation are considered: the stability of selected indicators over the quarters 2017–2021; their forecasting performance; the reliability of the estimates in terms of their monthly pattern.

Keywords: Large datasets; Temporal disaggregation; Model selection; Mixed frequency data; LASSO (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00181-024-02571-6

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