The function may be called by the names: g01ewc, nag_stat_prob_dickey_fuller_unit or nag_prob_dickey_fuller_unit.
3Description
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: $\tau $, ${\tau}_{\mu}$ or ${\tau}_{\tau}$, 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.To test whether a time series, ${y}_{t}$, for $\mathit{t}=1,2,\dots ,n$, has a unit root the regression model
is fit and the test statistic $\tau $ constructed as
$$\tau =\frac{{\hat{\beta}}_{1}}{{\sigma}_{11}}$$
where $\nabla $ is the difference operator, with $\nabla {y}_{t}={y}_{t}-{y}_{t-1}$, and where ${\hat{\beta}}_{1}$ and ${\sigma}_{11}$ are the least squares estimate and associated standard error for ${\beta}_{1}$ respectively.
2.To test for a unit root with drift the regression model
All three test statistics: $\tau $, ${\tau}_{\mu}$ and ${\tau}_{\tau}$ 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, ${\mathbf{method}}=\mathrm{Nag\_ViaLookUp}$ should be used.
4References
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
5Arguments
1: $\mathbf{method}$ – Nag_TS_URProbMethodInput
On entry: the method used to calculate the probability.
${\mathbf{method}}=\mathrm{Nag\_ViaLookUp}$
The probability is interpolated from a look-up table, whose values were obtained via simulation.
If the test statistic was calculated using g13awc the value of type and n must not change between calls to g01ewcandg13awc.
5: $\mathbf{nsamp}$ – IntegerInput
On entry: if ${\mathbf{method}}=\mathrm{Nag\_ViaSimulation}$, the number of samples used in the simulation; otherwise nsamp is not referenced and need not be set.
Constraint:
if ${\mathbf{method}}=\mathrm{Nag\_ViaSimulation}$, ${\mathbf{nsamp}}>0$.
Note: the dimension, $\mathit{dim}$, 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 ${\mathbf{method}}=\mathrm{Nag\_ViaSimulation}$, 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 ${\mathbf{method}}=\mathrm{Nag\_ViaSimulation}$, state contains updated information on the state of the generator otherwise a zero length vector is returned.
7: $\mathbf{fail}$ – NagError *Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).
6Error Indicators and Warnings
NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_BAD_PARAM
On entry, argument $\u27e8\mathit{\text{value}}\u27e9$ had an illegal value.
NE_INT
On entry, ${\mathbf{n}}=\u27e8\mathit{\text{value}}\u27e9$. Constraint: if ${\mathbf{method}}\ne \mathrm{Nag\_ViaSimulation}$, ${\mathbf{n}}>0$.
On entry, ${\mathbf{n}}=\u27e8\mathit{\text{value}}\u27e9$. Constraint: if ${\mathbf{method}}=\mathrm{Nag\_ViaSimulation}$ and ${\mathbf{type}}=\mathrm{Nag\_UnitRoot}$, ${\mathbf{n}}>2$.
On entry, ${\mathbf{n}}=\u27e8\mathit{\text{value}}\u27e9$. Constraint: if ${\mathbf{method}}=\mathrm{Nag\_ViaSimulation}$ and ${\mathbf{type}}=\mathrm{Nag\_UnitRootWithDriftAndTrend}$, ${\mathbf{n}}>4$.
On entry, ${\mathbf{n}}=\u27e8\mathit{\text{value}}\u27e9$. Constraint: if ${\mathbf{method}}=\mathrm{Nag\_ViaSimulation}$ and ${\mathbf{type}}=\mathrm{Nag\_UnitRootWithDrift}$, ${\mathbf{n}}>3$.
NE_INTERNAL_ERROR
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.
NE_INVALID_STATE
On entry, ${\mathbf{method}}=\mathrm{Nag\_ViaSimulation}$ and the state vector has been corrupted or not initialized.
NE_NO_LICENCE
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.
NE_SAMPLE
On entry, ${\mathbf{nsamp}}=\u27e8\mathit{\text{value}}\u27e9$. Constraint: if ${\mathbf{method}}=\mathrm{Nag\_ViaSimulation}$, ${\mathbf{nsamp}}>0$.
NW_EXTRAPOLATION
The supplied input values were outside the range of at least one look-up table, therefore, extrapolation was used.
7Accuracy
When ${\mathbf{method}}=\mathrm{Nag\_ViaLookUp}$, the probability returned by this function is unlikely to be accurate to more than $4$ or $5$ decimal places, for ${\mathbf{method}}=\mathrm{Nag\_ViaLookUpOriginal}$ 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 ${\mathbf{method}}=\mathrm{Nag\_ViaSimulation}$ the accuracy of the returned probability is controlled by the number of simulations performed (i.e., the value of nsamp used).
8Parallelism and Performance
Background information to multithreading can be found in the Multithreading documentation.
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.
9Further Comments
When ${\mathbf{method}}=\mathrm{Nag\_ViaLookUp}$ or $\mathrm{Nag\_ViaLookUpOriginal}$ the probability returned is constructed by interpolating from a series of look-up tables. In the case of ${\mathbf{method}}=\mathrm{Nag\_ViaLookUpOriginal}$ the look-up tables are taken directly from Dickey (1976) and the interpolation is carried out using
e01sjc and
e01skc.
For ${\mathbf{method}}=\mathrm{Nag\_ViaLookUp}$ the look-up tables were constructed as follows:
(i)A sample size, $n$ was chosen.
(ii)${2}^{28}$ simulations were run.
(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.
(iv)A series of quantiles were calculated from the sample of ${2}^{28}$ test statistics. The quantiles were calculated at intervals of $0.0005$ between $0.0005$ and $0.9995$.
(v)A spline was fit to the quantiles using e02bec.
This process was repeated for $\mathrm{n}=25,50,75,100,150,200,250,300,350,400,450,500,600,700,800,900,1000,1500,2000,2500,5000,10000$, resulting in $22$ splines.
Given the $22$ splines, and a user-supplied sample size, $n$ and test statistic, $\tau $, an estimated $p$-value is calculated as follows:
(i)Evaluate each of the $22$ splines, at $\tau $, using e02bec. If, for a particular spline, the supplied value of $\tau $ 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.
(ii)Fit a spline through these $22$ points using e01bec.