Forecasting M&A deals with MIDAS count model

Javier Ojea Ferreiro (), Wildmer Daniel Gregori () and Michela Nardo ()
Additional contact information
Javier Ojea Ferreiro: Bank of Canada, http://www.bank-banque-canada.ca/
Wildmer Daniel Gregori: European Commission, https://ec.europa.eu/jrc/en
Michela Nardo: European Commission, https://ec.europa.eu/jrc/en

No 2022-15, JRC Working Papers in Economics and Finance from Joint Research Centre, European Commission

Abstract: This report focuses on the forecast of the number of monthly cross-border deals in the European Union. We propose a new model to improve the forecasting properties of a count model of Foreign Direct Investment deals in EU, by taking into account past trends in high-frequency (daily) deal data and the decomposition of the conditional overdispersion into short-term and long-term components. Our model relies on the dynamic behaviour of the first two moments of the distribution of FDI deals to explain the evolution of parameters η and π in the Negative Binomial distribution. We test this model with several subsets of M&A deals from 1998 to 2021 obtaining sizable forecast improvements as compared to benchmark INGARCH models

Keywords: M&A forecasting; MIDAS approach; count process; overdispersion (search for similar items in EconPapers)
JEL-codes: C10 C19 C35 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2022-12
New Economics Papers: this item is included in nep-int
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