Outliers and persistence in threshold autoregressive processes

Yamin Ahmad 1  and Luiggi Donayre 2
  • 1 Department of Economics, University of Wisconsin – Whitewater, 800 W Main Street, Whitewater, WI 53190, USA
  • 2 Department of Economics, University of Minnesota – Duluth, 1318 Kirby Drive, Duluth, MN 55812, USA
Yamin Ahmad and Luiggi Donayre

Abstract

This paper uses Monte Carlo simulations to investigate the effects of outlier observations on the properties of linearity tests against threshold autoregressive (TAR) processes. By considering different specifications and levels of persistence for the data-generating processes, we find that additive outliers distort the size of the test and that the distortion increases with the level of persistence. In addition, we also find that larger additive outliers can help to improve the power of the test in the case of persistent TAR processes.

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SNDE recognizes that advances in statistics and dynamical systems theory can increase our understanding of economic and financial markets. The journal seeks both theoretical and applied papers that characterize and motivate nonlinear phenomena. Researchers are required to assist replication of empirical results by providing copies of data and programs online. Algorithms and rapid communications are also published.

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