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Mendes, D. A. & Mendes, V. (2019). A NONLINEAR FACTOR ANALYSIS FOR LARGE SETS OF MACROECONOMIC TIME SERIES. JuliaCon2019.

D. E. Mendes and V. M. Mendes,  "A NONLINEAR FACTOR ANALYSIS FOR LARGE SETS OF MACROECONOMIC TIME SERIES", in JuliaCon2019, 2019

@misc{mendes2019_1733300818372,
	author = "Mendes, D. A. and Mendes, V.",
	title = "A NONLINEAR FACTOR ANALYSIS FOR LARGE SETS OF MACROECONOMIC TIME SERIES",
	year = "2019",
	url = "https://juliacon.org/2019/"
}

TY  - CPAPER
TI  - A NONLINEAR FACTOR ANALYSIS FOR LARGE SETS OF MACROECONOMIC TIME SERIES
T2  - JuliaCon2019
AU  - Mendes, D. A.
AU  - Mendes, V.
PY  - 2019
UR  - https://juliacon.org/2019/
AB  - Dynamic factor models are frequently used for empirical research
in macroeconomics. Several papers in this area have argued
that the co-movements of large panels of macroeconomic and financial
data can be captured by a relatively few common unobserved
(latent) factors. The main purpose of this paper is to analyze
and compare the transmission mechanism of monetary policy
in the USA, by using a FAVAR model based on two different
approaches, in order to include information from large data
sets. The first approach consists of a classical linear methodology
where the factors are obtained through a principal component
analysis (PCA), while the second one employs a nonlinear factor
algorithm based on independent component analysis (ICA) and
on a Nonlinear PCA. In comparison to PCA, the factors extracted
by nonlinear methods provide a better performance in the Factor
Augmented VAR model, which can be illustrated by Impulse
Response Functions and forecasting. We perform the dynamic inference
on the model by using a dynamic bayesian estimation.
ER  -