Dickey–Fuller test
In
Explanation
A simple
where is the variable of interest, is the time index, is a coefficient, and is the
The regression model can be written as
where is the first difference operator and . This model can be estimated, and testing for a unit root is equivalent to testing . Since the test is done over the residual term rather than raw data, it is not possible to use standard t-distribution to provide critical values. Therefore, this statistic has a specific distribution simply known as the Dickey–Fuller table.
There are three main versions of the test:
1. Test for a unit root:
2. Test for a unit root with constant:
3. Test for a unit root with constant and deterministic time trend:
Each version of the test has its own critical value which depends on the size of the sample. In each case, the null hypothesis is that there is a unit root, . The tests have low
The intuition behind the test is as follows. If the series is stationary (or trend-stationary), then it has a tendency to return to a constant (or deterministically trending) mean. Therefore, large values will tend to be followed by smaller values (negative changes), and small values by larger values (positive changes). Accordingly, the level of the series will be a significant predictor of next period's change, and will have a negative coefficient. If, on the other hand, the series is integrated, then positive changes and negative changes will occur with probabilities that do not depend on the current level of the series; in a random walk, where you are now does not affect which way you will go next.
It is notable that
may be rewritten as
with a deterministic trend coming from and a stochastic intercept term coming from , resulting in what is referred to as a stochastic trend.[2]
There is also an extension of the Dickey–Fuller (DF) test called the augmented Dickey–Fuller test (ADF), which removes all the structural effects (autocorrelation) in the time series and then tests using the same procedure.
Dealing with uncertainty about including the intercept and deterministic time trend terms
Which of the three main versions of the test should be used is not a minor issue. The decision is important for the size of the unit root test (the probability of rejecting the null hypothesis of a unit root when there is one) and the power of the unit root test (the probability of rejecting the null hypothesis of a unit root when there is not one). Inappropriate exclusion of the intercept or deterministic time trend term leads to
Use of prior knowledge about whether the intercept and deterministic time trend should be included is of course ideal but not always possible. When such prior knowledge is unavailable, various testing strategies (series of ordered tests) have been suggested, e.g. by Dolado, Jenkinson, and Sosvilla-Rivero (1990)
See also
References
- JSTOR 2286348.
- ISBN 978-0-471-23065-6.
- JSTOR 3585053.
- hdl:10016/3321.
- S2CID 18656808.
- ^ Hacker, R. S.; Hatemi-J, A. (2010). "The Properties of Procedures Dealing with Uncertainty about Intercept and Deterministic Trend in Unit Root Testing". CESIS Electronic Working Paper Series, Paper No. 214. Centre of Excellence for Science and Innovation Studies, The Royal Institute of Technology, Stockholm, Sweden.
- ^ Hacker, Scott (2010-02-11). "The Effectiveness of Information Criteria in Determining Unit Root and Trend Status". Working Paper Series in Economics and Institutions of Innovation. 213. Stockholm, Sweden: Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
Further reading
- Enders, Walter (2010). Applied Econometric Time Series (Third ed.). New York: Wiley. pp. 206–215. ISBN 978-0470-50539-7.
- Hatanaka, Michio (1996). Time-Series-Based Econometrics: Unit Roots and Cointegration. New York: Oxford University Press. pp. 48–49. ISBN 978-0-19-877353-5.
External links
- Statistical tables for unit-root tests – Dickey–Fuller table
- How to do a Dickey-Fuller Test Using Excel