Dealing with dimension reduction in financial panel data

Alessandro Bitetto (), Paola Cerchiello () and Charilaos Mertzanis ()
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Alessandro Bitetto: University of Pavia
Paola Cerchiello: University of Pavia
Charilaos Mertzanis: Abu Dhabi University

No 207, DEM Working Papers Series from University of Pavia, Department of Economics and Management

Abstract: In this paper, we present a fully data-driven statistical approach to building a synthetic index based on intrinsic information of the considered ecosystem, namely the financial one. Among the several methods made available in the literature, we propose the employment of a Dynamic Factor Model approach which allows us to fully and correctly compare observations at hand in space and time. We contribute to the research field by offering a statistically sound methodology which goes beyond state of the art techniques on dimension reduction, mainly based on Principal Component Analysis. We adopt a country by country fitting strategy to elicit the inner country specific characteristics and then we combine results together by means of a Vector Autoregressive and Kalman filter approach. To this aim, we analyze a set of 17 Financial Soundness Indicators provided by the International Monetary Fund ranging from 2006 to 2017 for 140 countries that span the globe, including both strong and developing economies.

Keywords: Financial stability; Financing constraints; Data-driven; Dynamic Factor Model; State-space model; dimension reduction (search for similar items in EconPapers)
Pages: 14
Date: 2022-06
New Economics Papers: this item is included in nep-ets and nep-fdg
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