Topics in Time Series
Humberto Barreto, Frank Howland · Cambridge University Press eBooks · 2005
It seems necessary, then, that all commercial fluctuations should be investigated according to the same scientific methods with which we are familiar in other complicated sciences, such especially as meteorology and terrestrial magnetism. Every kind of periodic fluctuation, whether daily, weekly, monthly, quarterly, or yearly, must be detected and exhibited, not only as a study in itself, but because we must ascertain and eliminate such periodic variations before we can correctly exhibit those which are irregular and non-periodic, and probably of more interest and importance. W. S. Jevons Introduction In this chapter we discuss further topics relating to time series analysis. Time series econometrics is a vast field. Our aim in this chapter is to expose you to some of the main techniques for modeling time series and to call attention to important issues pertaining to the data generation process for variables that change over time. Sections 21.2 through 21.4 demonstrate basic techniques for dealing with time series using a trend term and dummy variables and making seasonal adjustments. Sections 21.5 and 21.6 examine important issues pertaining to the data generation process. For OLS to produce consistent estimates of parameters, time series must be stationary and cannot be strongly dependent. Section 21.5 examines the issue of stationarity, while Section 21.6 tackles the subject of weak dependence. In time series, lagged dependent variables are very often included as regressors. Section 21.7 discusses lagged dependent variables in general and Section 21.8 contains a practical example of the use of lagged dependent variables in the estimation of money demand.