Book contents
- Frontmatter
- Contents
- Acknowledgments
- List of Contributors
- Introduction
- 1 The ET Interview: Professor Clive Granger
- PART ONE SPECTRAL ANALYSIS
- PART TWO SEASONALITY
- PART THREE NONLINEARITY
- 6 Non-Linear Time Series Modeling
- 7 Using the Correlation Exponent to Decide Whether an Economic Series is Chaotic
- 8 Testing for Neglected Nonlinearity in Time Series Models: A Comparison of Neural Network Methods and Alternative Tests
- 9 Modeling Nonlinear Relationships Between Extended-Memory Variables
- 10 Semiparametric Estimates of the Relation Between Weather and Electricity Sales
- PART FOUR METHODOLOGY
- PART FIVE FORECASTING
- Index
8 - Testing for Neglected Nonlinearity in Time Series Models: A Comparison of Neural Network Methods and Alternative Tests
Published online by Cambridge University Press: 06 July 2010
- Frontmatter
- Contents
- Acknowledgments
- List of Contributors
- Introduction
- 1 The ET Interview: Professor Clive Granger
- PART ONE SPECTRAL ANALYSIS
- PART TWO SEASONALITY
- PART THREE NONLINEARITY
- 6 Non-Linear Time Series Modeling
- 7 Using the Correlation Exponent to Decide Whether an Economic Series is Chaotic
- 8 Testing for Neglected Nonlinearity in Time Series Models: A Comparison of Neural Network Methods and Alternative Tests
- 9 Modeling Nonlinear Relationships Between Extended-Memory Variables
- 10 Semiparametric Estimates of the Relation Between Weather and Electricity Sales
- PART FOUR METHODOLOGY
- PART FIVE FORECASTING
- Index
Summary
In this paper a new test, the neural network test for neglected nonlinearity, is compared with the Keenan test, the Tsay test, the White dynamic information matrix test, the McLeod–Li test, the Ramsey RESET test, the Brock–Dechert–Scheinkman test, and the Bispectrum test. The neural network test is based on the approximating ability of neural network modeling techniques recently developed by cognitive scientists. This test is a Lagrange multiplier test that statistically determines whether adding ‘hidden units’ to the linear network would be advantageous. The performance of the tests is compared using a variety of non-linear artificial series including bilinear, threshold autoregressive, and nonlinear moving average models, and the tests are applied to actual economic time series. The relative performance of the neural network test is encouraging. Our results suggest that it can play a valuable role in evaluating model adequacy. The neural network test has proper size and good power, and many of the economic series tested exhibit potential nonlinearities.
INTRODUCTION
Specification and estimation of linear time series models are well-established procedures, based on ARIMA univariate models or VAR or VARMAX multivariate models. However, economic theory frequently suggests nonlinear relationships between variables, and many economists appear to believe that the economic system is nonlinear. It is thus interesting to test whether or not a single economic series or group of series appears to be generated by a linear model against the alternative that they are nonlinearly related.
- Type
- Chapter
- Information
- Essays in EconometricsCollected Papers of Clive W. J. Granger, pp. 208 - 229Publisher: Cambridge University PressPrint publication year: 2001
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