(sin asunto)

[Nan loz]

Hola a todos! Necesito ayuda con un codigo que escribi
para estimar regresiones. La funcion que me interesa
es la ultima (hacvar). El problema es que es muy
lento, usando solo 49 observaciones se demora como 10
minutos asi que para poderlo implementar realmente
necesito que sea mas rapido. Les agradezco cualquier
sugerencia. Muchas gracias,
Nancy Lozano


class getdata:
    	"""This class gets data and defines y, x and inst
to be used in regressions
    	also asks to  define coordinates"""
   	def
__init__(self,filename,y=[],x=[],inst=[],xcoord=[],ycoord=[]):
		self.db=dbfreader113005.dbf(filename)
		self.ynames=y
		self.xnames=x
		self.instnames=inst
		self.Y=self.db.arrayfields(self.ynames)[1]
		self.X=self.db.arrayfields(self.xnames)[1]
		self.H=self.db.arrayfields(self.instnames)[1]#if
inst is empty then this gives a matrix of all numeric
vaiables
		self.xcoord=self.db.listfields(xcoord)
		self.ycoord=self.db.listfields(ycoord)

#estimating OLS
class estimators:
	def __init__(self,Y,X,H,xcoord,ycoord):
		self.var = X
		self.Y = Y
		self.H = H
		self.xcoord=xcoord
		self.ycoord=ycoord

   	""" The class estimator must receive as input a
getdata object
    	It can also receive dbf objects
    	X,Y,H: must be in array format
    	X: array of exogenous variables
    	Y: array of dependent variable
   	H: array of instruments to be used in IV
regressions
    	xcoord: x coordinate values, must be as a list
    	ycoord: y coordinate values, must come as a
list"""
    
	def ols(self,constant=1):
        	"""The function ols gives as result a
dictionary with keys:values
        	olsb: array of coefficients from ols
        	resid: array of residuals
        	varcov: variance-covariance matrix as array
        	yhat: estimated value of dependent variable
        	seb: standard errors for ols coefficients"""
		n=len(self.Y[0])
		if constant==1:
			c=nm.ones((1,n),type='Float64')
			self.X=nm.concatenate((c,self.var))
		else:
			self.X=self.var
		self.k=len(self.X)
		XpX=nm.innerproduct(self.X,self.X)
		iXpX=la.inverse(XpX)
		XY=nm.innerproduct(self.X,self.Y)
		olsb=mm(iXpX,XY)
		Xt=nm.transpose(self.X)
		yhat=mm(Xt,olsb)
		resid=self.Y[0,:]-yhat[:,0]
		residt=nm.transpose(resid)
	
sigmasqd=(mm(residt,resid))*(1.0/(float(n)-float(self.k)))
		#print sigmasqd
		varcov=sigmasqd*iXpX
		res2=mm(residt,resid)
		sumy=sum(self.Y[0,:])/float(n)
		R2 = 1.0-(res2/sum([(i-sumy)**2.0 for i in
self.Y[0,:]]))
	
R2_adj=1.0-((float(n)-1.0)*((1.0-R2)/(float(n)-float(self.k))))
        
		self.olsresults = {}
		self.olsresults['olsb']=olsb.tolist()
		self.olsresults['resid']=resid.tolist()
		self.olsresults['varcov']=varcov
		self.olsresults['yhat']=yhat.tolist()
		self.olsresults['seb']=[math.sqrt(i) for i in
varcov.diagonal()]
		self.olsresults['t-values']=[i[0]/j for (i,j) in
zip(olsb.tolist(),self.olsresults['seb'])]
		self.olsresults['sigmasqd']=sigmasqd
		self.olsresults['R2']=R2
		self.olsresults['R2_adj']=R2_adj
		return self.olsresults

	def hacvar(self,dm,kmethod):
        	"""This function gives the HAC estimate for
the variance-covariance matrix
        	from Kelejian and Prucha (2005).
        	OLS must be run before this function is used
so that the residuals from the OLS
        	regression can be used to estimate the
variance covariance matrix
        	dm: this is the cutoff used to determine the
neighbors, it must be in the same units
        	as the x,y coordinates kmethod: defines the
kernel that must be used, ex. Parzen"""
		self.hacvar=[]
		v=self.X.tolist()
		h=len(v)
		n=float(len(v[0]))
		for k in range(h):
			row=[]
			for l in range(h):
				sumij=0.0
				testij=0.0
				for i, p in enumerate(v[l]):
				
tempkern=Kernels.kernel(self.xcoord[0][i],self.ycoord[0][i],self.xcoord[0],self.ycoord[0],dm)
					tempk=getattr(tempkern,kmethod)()
					ri=self.ols()['resid'][i]
				
temp1=[a*ri*self.ols()['resid'][j]*v[k][i]*tempk[j]
for j, a in enumerate(v[l])]
					sumj=sum(temp1)
					sumij+=sumj
					if tempkern.nneigh>=n**(1.0/3.0):
						t=1.0
					else:
						t=0.0
					testij+=t
				rowkl=sumij/n
				row.append(rowkl)
			self.hacvar.append(row)
		if testij>0:
			print "The number of neighbors is more than n^(1/3)
for %s observations" % testij
		det=la.determinant(self.hacvar)
		if det<0.0:
			print "The determinant of the estimated HAC var-cov
matrix is not p.s.d"
		else:
			print "The determinant of the estimated HAC var-cov
matrix is %s " % det
		return self.hacvar



__________________________________________________
Do You Yahoo!?
Tired of spam?  Yahoo! Mail has the best spam protection around 
http://mail.yahoo.com