Nonparametric Analysis of Complex Nonlinear Systems

McMaster University, Department of Economics, Working Paper Series, 2016-07

20 Pages Posted: 22 Aug 2016

See all articles by Sonali Das

Sonali Das

CSIR

Jeffrey Racine

Department of Economics - McMaster University

Date Written: July 14, 2016

Abstract

In this paper we propose a nonparametric methodology designed to facilitate the statistical analysis of complex systems. The proposed approach exploits an ensemble of nonparametric techniques including conditional density function estimation, conditional distribution function estimation, conditional mean estimation (regression) and conditional quantile estimation (quantile regression). By exploiting recent developments in nonparametric methodology and also in open source interactive platforms for data visualization and statistical analysis, we are able to provide an approach that facilitates enhanced understanding of complex empirical phenomenon. We illustrate this approach by exploring the inherent complexity of the Southern Ocean system as a carbon sink, measured in terms of fugacity of carbon dioxide at sea surface temperature (f CO2), in relation to a number of oceanic state variables, all measured in situ during the annual South African National Antarctic Expedition (SANAE) austral summer trips from Cape Town to the Antarctic, and back, between 2010 and 2015.

Keywords: Kernel Smoothing; Conditional Density, Distribution, Mean and Quantile Estimation; Exploratory Data Analysis

Suggested Citation

Das, Sonali and Racine, Jeffrey, Nonparametric Analysis of Complex Nonlinear Systems (July 14, 2016). McMaster University, Department of Economics, Working Paper Series, 2016-07, Available at SSRN: https://ssrn.com/abstract=2826732 or http://dx.doi.org/10.2139/ssrn.2826732

Sonali Das

CSIR ( email )

P. O. Box 395
Brummeria
Pretoria, 0001
South Africa

Jeffrey Racine (Contact Author)

Department of Economics - McMaster University ( email )

Hamilton, Ontario L8S 4M4
Canada

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