Forecasting Corporate Capital Accumulation in Italy: The Role of Survey-Based Information

51 Pages Posted: 16 Apr 2021

Date Written: February 10, 2021

Abstract

While there is a vast macroeconomic literature that singles out the main drivers of capital accumulation in advanced economies during and after the global financial and sovereign debt crises’ recessionary phase, there is much less research seeking to identify both models and variables that possess out-of-sample forecasting ability for gross fixed capital formation. Moreover, micro-founded variables are scarcely employed in macroeconomic forecasting of real investment. We fill this gap by considering a battery of univariate and multivariate time-series models to forecast investment of non-financial corporations in Italy, an interesting case study due to its steep downturn during the two afore-mentioned crises. We find that a vector error correction model augmented with firm survey-based variables accounting for business confidence, demand uncertainty and financing constraints generally outperforms the autoregressive benchmark and a series of competing multivariate time-series models in various, alternative, evaluation samples that take into account the impact of both the global financial crisis and the sovereign debt crisis on forecast accuracy.

Keywords: real investment, forecasting evaluation, firm survey data, vector error correction model

JEL Classification: C32, C52, E22, E27

Suggested Citation

Giordano, Claire and Marinucci, Marco and Silvestrini, Andrea, Forecasting Corporate Capital Accumulation in Italy: The Role of Survey-Based Information (February 10, 2021). Bank of Italy Occasional Paper No. 596, Available at SSRN: https://ssrn.com/abstract=3827509 or http://dx.doi.org/10.2139/ssrn.3827509

Claire Giordano

Bank of Italy ( email )

Via Nazionale 91
Rome, 00184
Italy

Marco Marinucci (Contact Author)

Bank of Italy ( email )

Via Nazionale 91
Rome, 00184
Italy

Andrea Silvestrini

Bank of Italy ( email )

Via Nazionale 91
Rome, 00184
Italy

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