Prediction and Simulation Using Simple Models Characterized by Nonstationarity and Seasonality

Norman Swanson () and Richard Urbach
Additional contact information
Richard Urbach: Conning Germany Gmbh

Departmental Working Papers from Rutgers University, Department of Economics

Abstract: In this paper, we provide new evidence on the empirical usefulness of various simple seasonal models, and underscore the importance of carefully designing criteria by which one judges alternative models. In particular, we underscore the importance of both choice of forecast or simulation horizon and choice between minimizing point or distribution based loss measures. Our empirical analysis centers around the implementation of a series of simulation and prediction experiments, as well as a discussion of the stochastic properties of seasonal unit root models. Our prediction experiments are based on analysis of a group of 14 variables have been chosen to closely mimic the set of indicators used by the Federal Reserve to help in setting U.S. monetary policy, and our simulation experiments are based on a comparison of simulated and historical distributions of said variables using the testing approach of Corradi and Swanson (2007a).

Keywords: seasonal unit root; periodic autoregression; difference stationary (search for similar items in EconPapers)
JEL-codes: C13 C22 C52 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2013-08-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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http://www.sas.rutgers.edu/virtual/snde/wp/2013-23.pdf (application/pdf)

Related works:
Journal Article: Prediction and simulation using simple models characterized by nonstationarity and seasonality (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:rut:rutres:201323

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