The Role of Commercial Banks and Financial Intermediaries in the New Consensus Macroeconomics (NCM): A Preliminary and Critical Appraisal of Old and New Models

In response to the economic crisis unleashed by the Covid-19 pandemic, the EU authorities have launched extraordinary fiscal and monetary measures in support of member states. The impact of these measures is of great significance for Italy, the EU thirdlargest economy, which as a result of the pandemic has suffered a dramatic decline in GDP, and a further rise in the government debt to GDP ratio. Building on a stock-flow consistent, structural macroeconometric model, this paper shows that the currently planned EU measures are insufficient to boost the recovery of the Italian economy, and to ensure the sustainability of its government debt. The paper also assesses two potential alternative policies. A fiscal consolidation (i.e. austerity) policy would exacerbate the decline in GDP and further deteriorate the government debt to GDP ratio. By contrast, a money-financed fiscal stimulus policy could lead the Italian economy on a path of sustainable growth, with positive outcomes for employment and government finances.

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Public debt forecasts and machine learning: the Italian case

Sica,

Altınbaş,

Marini

2023

JES

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PurposePublic debt forecasts represent a key policy issue. Many methodologies have been employed to predict debt sustainability, including dynamic stochastic general equilibrium models, the stock flow consistent method, the structural vector autoregressive model and, more recently, the neuro-fuzzy method. Despite their widespread application in the empirical literature, all of these approaches exhibit shortcomings that limit their utility. The present research adopts a different approach to public debt forecasts, that is, the random forest, an ensemble of machine learning.Design/methodology/approachUsing quarterly observations over the period 2000–2021, the present research tests the reliability of the random forest technique for forecasting the Italian public debt.FindingsThe results show the large predictive power of this method to forecast debt-to-GDP fluctuations, with no need to model the underlying structure of the economy.Originality/valueCompared to other methodologies, the random forest method has a predictive capacity that is granted by the algorithm itself. The use of repeated learning, training and validation stages provides well-defined parameters that are not conditional to strong theoretical restrictions This allows to overcome the shortcomings arising from the traditional techniques which are generally adopted in the empirical literature to forecast public debt.

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The Role of Commercial Banks and Financial Intermediaries in the New Consensus Macroeconomics (NCM): A Preliminary and Critical Appraisal of Old and New Models

Cited by 3 publications

References 74 publications

Transformation of the Institution of Money in the Digital Epoch

Transformation of the Institution of Money in the Digital Epoch

Are EU Policies Effective to Tackle the Covid-19 Crisis? The Case of Italy

Public debt forecasts and machine learning: the Italian case

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