In and out-of-sample performance of nonlinear models in international price differential forecasting in a commodity country framework

Publication Date : 08/03/2021

Author(s) :

Nicola Rubino.

Volume/Issue :
Volume 9
Issue 1
(03 - 2021)

Abstract :

This paper presents an analysis of a group of small commodity ex- porting countries‘price differentials relative to the US dollar. Using Un- restricted Self exciting Threshold Autoregressive models (SETAR), we model and evaluate sixteen national Consumers‘Price Index (CPI) differentials relative to the US dollar CPI. Out-of-sample forecast accuracy is evaluated through calculation of mean absolute errors measures based on monthly rolling window and recursive forecasts and extended to three additional models, namely a logistic smooth transition regression (LSTAR), an additive nonlinear autoregressive model (AAR) and a simple Neural Network model (NNET). Our preliminary results confirm presence of some form of nonlinearity in most of the countries analyzed. The parsimonious AR(1) model does mot appear to perform any worse than any nonlinear model in the rolling sample exercise. However, its validity in terms of a long run equilibrium driven by Purchasing Power Parity is undermined by the results of the recursive estimates and the outcome of the Diebold-Mariano type tests, which more generally favor the Heckscher commodity points theory. As a policy advice to commodity exporting countries, we find no apparent reason to suggest commodity export price pegging as a generalized foreign exchange policy.

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