Abstract
>Economic time series often exhibit strong seasonal variation. For example, an investor in mortgage-backed securities might be interested in predicting future housing starts, and these are usually much lower in the winter months compared to the rest of the year.
Keywords
- ARIMA Model
- Residual Correlation
- Multivariate Time Series
- ARMA Process
- Heteroskedasticity Consistent Standard Error
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Beran, J. (1992) Statistical methods for data with long-range dependence. Statistical Science, 7, 404–427.
Beran, J. (1994) Statistics for Long-Memory Processes, Chapman & Hall, Boca Raton, FL.
Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (2008) Times Series Analysis: Forecasting and Control, 4th ed., Wiley, Hoboken, NJ.
Bühlmann, P. (2002) Bootstraps for time series. Statistical Science, 17, 52–72.
Davison, A. C. and Hinkley, D. V. (1997) Bootstrap Methods and Their Applications, Cambridge University Press, Cambridge.
Enders, W. (2004) Applied Econometric Time Series, 2nd ed., Wiley, New York.
Hamilton, J. D. (1994) Time Series Analysis, Princeton University Press, Princeton, NJ.
Lahiri, S. N. (2003) Resampling Methods for Dependent Data, Springer, New York.
Newey, W. and West, K. (1987) A simple, positive semidefinite, heteroscedasticity and autocorrelation consistent covariance matrix. Econometrica, 55, 703–708.
Reinsel, G. C. (2003) Elements of Multivariate Time Series Analysis, 2nd ed., Springer, New York.
Stock, J. H. and Watson, M. W. (2005). An empirical comparison of methods for forecasting using many predictors, manuscript http://www4.ncsu.edu/~arhall/beb_4.pdf
White, H. (1980) A heteroscedasticity consistent covariance matrix estimator and a direct test for heteroscedasticity. Econometrica, 48, 827–838.
Zeileis, A. (2004) Econometric computing with HC and HAC covariance matrix estimators. Journal of Statistical Software, 11(10), 1–17.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Science+Business Media New York
About this chapter
Cite this chapter
Ruppert, D., Matteson, D.S. (2015). Time Series Models: Further Topics. In: Statistics and Data Analysis for Financial Engineering. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2614-5_13
Download citation
DOI: https://doi.org/10.1007/978-1-4939-2614-5_13
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-2613-8
Online ISBN: 978-1-4939-2614-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)