g01ep calculates upper and lower bounds for the significance of a Durbin–Watson statistic.


public static void g01ep(
	int n,
	int ip,
	double d,
	out double pdl,
	out double pdu,
	out int ifail
Visual Basic
Public Shared Sub g01ep ( _
	n As Integer, _
	ip As Integer, _
	d As Double, _
	<OutAttribute> ByRef pdl As Double, _
	<OutAttribute> ByRef pdu As Double, _
	<OutAttribute> ByRef ifail As Integer _
Visual C++
static void g01ep(
	int n, 
	int ip, 
	double d, 
	[OutAttribute] double% pdl, 
	[OutAttribute] double% pdu, 
	[OutAttribute] int% ifail
static member g01ep : 
        n : int * 
        ip : int * 
        d : float * 
        pdl : float byref * 
        pdu : float byref * 
        ifail : int byref -> unit 


Type: System..::..Int32
On entry: n, the number of observations used in calculating the Durbin–Watson statistic.
Constraint: n>ip.
Type: System..::..Int32
On entry: p, the number of independent variables in the regression model, including the mean.
Constraint: ip1.
Type: System..::..Double
On entry: d, the Durbin–Watson statistic.
Constraint: d0.0.
Type: System..::..Double%
On exit: lower bound for the significance of the Durbin–Watson statistic, pl.
Type: System..::..Double%
On exit: upper bound for the significance of the Durbin–Watson statistic, pu.
Type: System..::..Int32%
On exit: ifail=0 unless the method detects an error or a warning has been flagged (see [Error Indicators and Warnings]).


Let r=r1,r2,,rnT be the residuals from a linear regression of y on p independent variables, including the mean, where the y values y1,y2,,yn can be considered as a time series. The Durbin–Watson test (see Durbin and Watson (1950)Durbin and Watson (1951) and Durbin and Watson (1971)) can be used to test for serial correlation in the error term in the regression.
The Durbin–Watson test statistic is:
which can be written as
where the n by n matrix A is given by
with the nonzero eigenvalues of the matrix A being λj=1-cosπj/n, for j=1,2,,n-1.
Durbin and Watson show that the exact distribution of d depends on the eigenvalues of a matrix HA, where H is the hat matrix of independent variables, i.e., the matrix such that the vector of fitted values, y^, can be written as y^=Hy. However, bounds on the distribution can be obtained, the lower bound being
and the upper bound being
where ui are independent standard Normal variables.
Two algorithms are used to compute the lower tail (significance level) probabilities, pl and pu, associated with dl and du. If n60 the procedure due to Pan (1964) is used, see Farebrother (1980), otherwise Imhof's method (see Imhof (1961)) is used.
The bounds are for the usual test of positive correlation; if a test of negative correlation is required the value of d should be replaced by 4-d.


Durbin J and Watson G S (1950) Testing for serial correlation in least squares regression. I Biometrika 37 409–428
Durbin J and Watson G S (1951) Testing for serial correlation in least squares regression. II Biometrika 38 159–178
Durbin J and Watson G S (1971) Testing for serial correlation in least squares regression. III Biometrika 58 1–19
Farebrother R W (1980) Algorithm AS 153. Pan's procedure for the tail probabilities of the Durbin–Watson statistic Appl. Statist. 29 224–227
Imhof J P (1961) Computing the distribution of quadratic forms in Normal variables Biometrika 48 419–426
Newbold P (1988) Statistics for Business and Economics Prentice–Hall
Pan Jie–Jian (1964) Distributions of the noncircular serial correlation coefficients Shuxue Jinzhan 7 328–337

Error Indicators and Warnings

Errors or warnings detected by the method:
On entry,nip,
On entry,d<0.0.
An error occured, see message report.


On successful exit at least 4 decimal places of accuracy are achieved.

Parallelism and Performance


Further Comments

If the exact probabilities are required, then the first n-p eigenvalues of HA can be computed and g01jd used to compute the required probabilities with c set to 0.0 and d to the Durbin–Watson statistic.


The values of n, p and the Durbin–Watson statistic d are input and the bounds for the significance level calculated and printed.

Example program (C#): g01epe.cs

Example program data: g01epe.d

Example program results: g01epe.r

See Also