NAG CL Interface
g01ewc (prob_​dickey_​fuller_​unit)

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1 Purpose

g01ewc returns the probability associated with the lower tail of the distribution for the Dickey–Fuller unit root test statistic.

2 Specification

#include <nag.h>
double  g01ewc (Nag_TS_URProbMethod method, Nag_TS_URTestType type, Integer n, double ts, Integer nsamp, Integer state[], NagError *fail)
The function may be called by the names: g01ewc, nag_stat_prob_dickey_fuller_unit or nag_prob_dickey_fuller_unit.

3 Description

If the root of the characteristic equation for a time series is one then that series is said to have a unit root. Such series are nonstationary. g01ewc is designed to be called after g13awc and returns the probability associated with one of three types of (augmented) Dickey–Fuller test statistic: τ, τμ or ττ, used to test for a unit root, a unit root with drift or a unit root with drift and a deterministic time trend, respectively. The three types of test statistic are constructed as follows:
  1. 1.To test whether a time series, yt, for t=1,2,,n, has a unit root the regression model
    yt = β1 yt-1 + i=1 p-1 δi yt-i +εt  
    is fit and the test statistic τ constructed as
    τ = β^1 σ11  
    where is the difference operator, with yt = yt- yt-1 , and where β^1 and σ11 are the least squares estimate and associated standard error for β1 respectively.
  2. 2.To test for a unit root with drift the regression model
    yt = β1 yt-1 + i=1 p-1 δi yt-i +α +εt  
    is fit and the test statistic τμ constructed as
    τμ = β^1 σ11 .  
  3. 3.To test for a unit root with drift and deterministic time trend the regression model
    yt = β1 yt-1 + i=1 p-1 δi yt-i +α +β2t +εt  
    is fit and the test statistic ττ constructed as
    ττ = β^1 σ11 .  
All three test statistics: τ, τμ and ττ can be calculated using g13awc.
The probability distributions of these statistics are nonstandard and are a function of the length of the series of interest, n. The probability associated with a given test statistic, for a given n, can, therefore, only be calculated by simulation as described in Dickey and Fuller (1979). However, such simulations require a significant number of iterations and are, therefore, prohibitively expensive in terms of the time taken. As such g01ewc also allows the probability to be interpolated from a look-up table. Two such tables are provided, one from Dickey (1976) and one constructed as described in Section 9. The three different methods of obtaining an estimate of the probability can be chosen via the method argument. Unless there is a specific reason for choosing otherwise, method=Nag_ViaLookUp should be used.

4 References

Dickey A D (1976) Estimation and hypothesis testing in nonstationary time series PhD Thesis Iowa State University, Ames, Iowa
Dickey A D and Fuller W A (1979) Distribution of the estimators for autoregressive time series with a unit root J. Am. Stat. Assoc. 74 366 427–431

5 Arguments

1: method Nag_TS_URProbMethod Input
On entry: the method used to calculate the probability.
The probability is interpolated from a look-up table, whose values were obtained via simulation.
The probability is interpolated from a look-up table, whose values were obtained from Dickey (1976).
The probability is obtained via simulation.
The probability calculated from the look-up table should give sufficient accuracy for most applications.
Suggested value: method=Nag_ViaLookUp.
Constraint: method=Nag_ViaLookUp, Nag_ViaLookUpOriginal or Nag_ViaSimulation.
2: type Nag_TS_URTestType Input
On entry: the type of test statistic, supplied in ts.
Constraint: type=Nag_UnitRoot, Nag_UnitRootWithDrift or Nag_UnitRootWithDriftAndTrend.
3: n Integer Input
On entry: n, the length of the time series used to calculate the test statistic.
  • if methodNag_ViaSimulation, n>0;
  • if method=Nag_ViaSimulation and type=Nag_UnitRoot, n>2;
  • if method=Nag_ViaSimulation and type=Nag_UnitRootWithDrift, n>3;
  • if method=Nag_ViaSimulation and type=Nag_UnitRootWithDriftAndTrend, n>4.
4: ts double Input
On entry: the Dickey–Fuller test statistic for which the probability is required. If
ts must contain τ.
ts must contain τμ.
ts must contain ττ.
If the test statistic was calculated using g13awc the value of type and n must not change between calls to g01ewc and g13awc.
5: nsamp Integer Input
On entry: if method=Nag_ViaSimulation, the number of samples used in the simulation; otherwise nsamp is not referenced and need not be set.
Constraint: if method=Nag_ViaSimulation, nsamp>0.
6: state[dim] Integer Communication Array
Note: the dimension, dim, of this array is dictated by the requirements of associated functions that must have been previously called. This array MUST be the same array passed as argument state in the previous call to nag_rand_init_repeatable (g05kfc) or nag_rand_init_nonrepeatable (g05kgc).
On entry: if method=Nag_ViaSimulation, state must contain information on the selected base generator and its current state; otherwise state is not referenced and may be NULL.
On exit: if method=Nag_ViaSimulation, state contains updated information on the state of the generator otherwise a zero length vector is returned.
7: fail NagError * Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
On entry, argument value had an illegal value.
On entry, n=value.
Constraint: if methodNag_ViaSimulation, n>0.
On entry, n=value.
Constraint: if method=Nag_ViaSimulation and type=Nag_UnitRoot, n>2.
On entry, n=value.
Constraint: if method=Nag_ViaSimulation and type=Nag_UnitRootWithDriftAndTrend, n>4.
On entry, n=value.
Constraint: if method=Nag_ViaSimulation and type=Nag_UnitRootWithDrift, n>3.
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
On entry, method=Nag_ViaSimulation and the state vector has been corrupted or not initialized.
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
On entry, nsamp=value.
Constraint: if method=Nag_ViaSimulation, nsamp>0.
The supplied input values were outside the range of at least one look-up table, therefore, extrapolation was used.

7 Accuracy

When method=Nag_ViaLookUp, the probability returned by this function is unlikely to be accurate to more than 4 or 5 decimal places, for method=Nag_ViaLookUpOriginal this accuracy is likely to drop to 2 or 3 decimal places (see Section 9 for details on how these probabilities are constructed). In both cases the accuracy of the probability is likely to be lower when extrapolation is used, particularly for small values of n (less than around 15). When method=Nag_ViaSimulation the accuracy of the returned probability is controlled by the number of simulations performed (i.e., the value of nsamp used).

8 Parallelism and Performance

g01ewc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
g01ewc makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

When method=Nag_ViaLookUp or Nag_ViaLookUpOriginal the probability returned is constructed by interpolating from a series of look-up tables. In the case of method=Nag_ViaLookUpOriginal the look-up tables are taken directly from Dickey (1976) and the interpolation is carried out using e01sjc and e01skc. For method=Nag_ViaLookUp the look-up tables were constructed as follows:
  1. (i)A sample size, n was chosen.
  2. (ii)228 simulations were run.
  3. (iii)At each simulation, a time series was constructed as described in chapter five of Dickey (1976). The relevant test statistic was then calculated for each of these time series.
  4. (iv)A series of quantiles were calculated from the sample of 228 test statistics. The quantiles were calculated at intervals of 0.0005 between 0.0005 and 0.9995.
  5. (v)A spline was fit to the quantiles using e02bec.
This process was repeated for n=25,50,75,100,150,200,250,300,350,400,450,500,600,700,800,900,1000,1500,2000,2500,5000,10000, resulting in 22 splines.
Given the 22 splines, and a user-supplied sample size, n and test statistic, τ, an estimated p-value is calculated as follows:
  1. (i)Evaluate each of the 22 splines, at τ, using e02bec. If, for a particular spline, the supplied value of τ lies outside of the range of the simulated data, then a third-order Taylor expansion is used to extrapolate, with the derivatives being calculated using e02bcc.
  2. (ii)Fit a spline through these 22 points using e01bec.
  3. (iii)Estimate the p-value using e01bfc.

10 Example

See g13awc.