Merge pull request #145 from pedrovma/main

Updating to version 1.6.0

spreg/diagnostics.py

@@ -13,7 +13,7 @@
 import numpy.linalg as la
 import scipy.sparse as SP
 from scipy import stats
-
+from .ols import BaseOLS
 from .utils import spmultiply, sphstack, spmin, spmax
 
 
@@ -34,19 +34,23 @@
  "vif",
  "likratiotest",
  "constant_check",
+ "dwh",
 ]
 
 
-def f_stat(reg):
+def f_stat(reg,df=0):
  """
- Calculates the f-statistic and associated p-value of the
- regression. :cite:`Greene2003`.
+ Calculates the f-statistic and associated p-value for multiple
+ coefficient constraints :cite:`Greene2003`.
  (For two stage least squares see f_stat_tsls)
+ (default is F statistic for regression)
 
  Parameters
  ----------
  reg : regression object
  output instance from a regression model
+ df : number of coefficient constraints 
+ (zero constraint for last df coefficients in betas)
 
  Returns
  ----------
@@ -94,12 +98,21 @@ def f_stat(reg):
  k = reg.k # (scalar) number of ind. vars (includes constant)
  n = reg.n # (scalar) number of observations
  utu = reg.utu # (scalar) residual sum of squares
- predy = reg.predy # (array) vector of predicted values (n x 1)
- mean_y = reg.mean_y # (scalar) mean of dependent observations
- Q = utu
- U = np.sum((predy - mean_y) ** 2)
- fStat = (U / (k - 1)) / (Q / (n - k))
- pValue = stats.f.sf(fStat, k - 1, n - k)
+ # default case, all coefficients
+ if df == 0:
+ r = k-1
+ predy = reg.predy # (array) vector of predicted values (n x 1)
+ mean_y = reg.mean_y # (scalar) mean of dependent observations
+ U = np.sum((predy - mean_y) ** 2)
+ else: # F test on last df coefficients
+ y = reg.y
+ r = df
+ x0 = reg.x[:,:-r]
+ olsr = BaseOLS(y,x0) # constrained regression
+ rtr = olsr.utu
+ U = rtr - utu
+ fStat = (U / r) / (utu / (n - k))
+ pValue = stats.f.sf(fStat, r, n - k)
  fs_result = (fStat, pValue)
  return fs_result
 
@@ -1376,6 +1389,40 @@ def likratiotest(reg0, reg1):
  likratio = {"likr": likr, "df": 1, "p-value": pvalue}
  return likratio
 
+def dwh(reg):
+ """
+ Durbin-Wu-Hausman test on endogeneity of variables
+
+ A significant test statistic points to endogeneity
+
+ Parameters
+ ----------
+ reg : regression object
+ output instance from a regression model
+
+ Returns
+ -------
+ dwh : tuple with value of F-statistic in augmented regression
+ and associated p-value
+
+ """
+ n = reg.n 
+ ny = reg.yend.shape[1] # number of endogenous variables
+ qq = reg.h # all exogenous and instruments
+ xx = reg.z # all exogenous and endogenous
+ # get predicted values for endogenous variables on all instruments
+ py = np.zeros((n,ny))
+ for i in range(ny):
+ yy = reg.yend[:, i].reshape(n,1)
+ ols1 = BaseOLS(y=yy,x=qq)
+ yp = ols1.predy
+ py[0:n,i] = yp.flatten()
+ nxq = sphstack(xx, py)
+ # F-test in augmented regression
+ ols2 = BaseOLS(y=reg.y, x=nxq)
+ dwh = f_stat(ols2, df=ny)
+ return dwh 
+
 
 def _test():
  import doctest