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Prediction and Simulation Using Simple Models Characterized by Nonstationarity and Seasonality

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  • Norman Swanson

    () (Rutgers University)

  • Richard Urbach

    (Conning Germany Gmbh)

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).

Suggested Citation

  • Norman Swanson & Richard Urbach, 2013. "Prediction and Simulation Using Simple Models Characterized by Nonstationarity and Seasonality," Departmental Working Papers 201323, Rutgers University, Department of Economics.
  • Handle: RePEc:rut:rutres:201323
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    References listed on IDEAS

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    Cited by:

    1. Hammoudeh, Shawkat & McAleer, Michael, 2015. "Advances in financial risk management and economic policy uncertainty: An overview," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 1-7.
    2. Swanson, Norman R. & Urbach, Richard, 2015. "Prediction and simulation using simple models characterized by nonstationarity and seasonality," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 312-323.

    More about this item

    Keywords

    seasonal unit root; periodic autoregression; difference stationary;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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