Abstract
Time series data consist of readings on a variable taken at equally intervals of time. How would one compute the mean of a time series of a specified length? Calculating the mean of a sequence of observations might appear to be a trivial problem, as we would just sum all readings and divide by their number. However, if the series is steadily increasing overtime, i.e. exhibits a trend and we make decisions based on this mean, we would certainly not, for example, want to use this parameter as a forecast of the future level of the series. We would also not use the overall mean to make inferences (e.g. as the centre of confidence intervals) at time periods at the beginning or end the series. If we regard our gathered series as but one example of all possible series that could be generated by the same mechanism, we are further faced with the problem of estimating the mean for each time period, as we have a sample only of one item. It is similarly impossible to estimate the variance at any one time period.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Reference
Box, G.E.P., & Pierce, D.A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American Statistical Association, 65, 1509–1526.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Aljandali, A., Tatahi, M. (2018). Time Series Modelling. In: Economic and Financial Modelling with EViews. Statistics and Econometrics for Finance. Springer, Cham. https://doi.org/10.1007/978-3-319-92985-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-92985-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92984-2
Online ISBN: 978-3-319-92985-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)