[Gmm-commits] r42 - in pkg/gmm: . R inst/doc man

noreply at r-forge.r-project.org noreply at r-forge.r-project.org
Tue Nov 29 23:02:27 CET 2011 

Author: chaussep
Date: 2011-11-29 23:02:25 +0100 (Tue, 29 Nov 2011)
New Revision: 42

Modified:
   pkg/gmm/NAMESPACE
   pkg/gmm/NEWS
   pkg/gmm/R/Methods.gmm.R
   pkg/gmm/R/gel.R
   pkg/gmm/R/getModel.R
   pkg/gmm/R/gmm.R
   pkg/gmm/R/momentEstim.R
   pkg/gmm/R/specTest.R
   pkg/gmm/inst/doc/gmm_with_R.pdf
   pkg/gmm/inst/doc/gmm_with_R.rnw
   pkg/gmm/man/summary.Rd
Log:
Several changes, See last NEWS

Modified: pkg/gmm/NAMESPACE
===================================================================
--- pkg/gmm/NAMESPACE	2011-11-01 17:58:07 UTC (rev 41)
+++ pkg/gmm/NAMESPACE	2011-11-29 22:02:25 UTC (rev 42)
@@ -7,9 +7,11 @@
 	residuals.gmm, fitted.gel, residuals.gel, plot.gmm, plot.gel,formula.gmm, formula.gel, charStable, specTest, 
 	specTest.gmm, specTest.gel, print.specTest, momentEstim.baseGmm.twoStep, momentEstim.baseGmm.twoStep.formula,
         momentEstim.baseGmm.iterative.formula, momentEstim.baseGmm.iterative, momentEstim.baseGmm.cue.formula, 
-	momentEstim.baseGmm.cue, getModel.baseGmm, getModel.baseGel, FinRes.baseGmm.res, momentEstim.baseGel.mod, momentEstim.baseGel.modFormula)
+	momentEstim.baseGmm.cue, getModel.baseGmm, getModel.baseGel, FinRes.baseGmm.res, momentEstim.baseGel.mod, 
+	momentEstim.baseGel.modFormula,tsls,summary.tsls, print.summary.tsls)
  
 S3method(summary, gmm)
+S3method(summary, tsls)
 S3method(summary, gel)
 S3method(print, gmm)
 S3method(print, summary.gmm)
@@ -22,6 +24,7 @@
 S3method(formula, gmm)
 S3method(print, gel)
 S3method(print, summary.gel)
+S3method(print, summary.tsls)
 S3method(coef, gel)
 S3method(vcov, gel)
 S3method(confint, gel)

Modified: pkg/gmm/NEWS
===================================================================
--- pkg/gmm/NEWS	2011-11-01 17:58:07 UTC (rev 41)
+++ pkg/gmm/NEWS	2011-11-29 22:02:25 UTC (rev 42)
@@ -5,7 +5,12 @@
   values provided by the user (for linear cases) or the first step GMM. 
 o You can now trace the convergence of the iterative GMM with the option traceIter=T
 o The weights for the kernel are also fixed for iter-GMM. It is faster and I don't think it should change at each iteration. I am open to comments on that.
-s
+o A function tsls() has been added to facilitate the estimation by 2SLS. The user does not have to worry about selecting the right gmm options
+o summary and print Methods have been added for tsls objects. The summary prints the f-statistics of the first stage LS
+o SmoothG for GEL uses now the kernapply() function which is more efficient.
+o Something the GMM converge to weird values which is sometimes caused by bad first step estimates used to compute the weighting matrix. 
+  Summary() prints the initial values to have more infortmation when convergence fails.
+o There was a bug in specTest() for GEL.  The degrees of freedom for the J-test were wrong. It is fixed.
 
 Changes in version 1.3-8
 

Modified: pkg/gmm/R/Methods.gmm.R
===================================================================
--- pkg/gmm/R/Methods.gmm.R	2011-11-01 17:58:07 UTC (rev 41)
+++ pkg/gmm/R/Methods.gmm.R	2011-11-29 22:02:25 UTC (rev 42)
@@ -29,10 +29,45 @@
         ans$algoInfo <- z$algoInfo
 	if(z$met=="cue")
 		ans$cue <- object$cue
+	if (!is.null(object$initTheta))
+		ans$initTheta <- object$initTheta
 	class(ans) <- "summary.gmm"
 	ans
 	}
 
+summary.tsls <- function(object, ...)
+	{
+	ans <- summary.gmm(object)
+	k <- object$dat$k
+	fstat <- vector()
+	fstat[1] <- object$fsRes[[1]]$fstatistic[1]
+	df1 <- object$fsRes[[1]]$fstatistic[2]
+	df2 <- object$fsRes[[1]]$fstatistic[3]
+	for (i in 2:k)	
+		fstat[i] <- object$fsRes[[i]]$fstatistic[1]
+	pvfstat <- 1-pf(fstat,df1, df2)
+	names(fstat) <- colnames(object$dat$x)[(object$dat$ny+1):(object$dat$ny+k)]
+	ans$fstatistic <- list(fstat = fstat, pvfstat = pvfstat, df1 = df1, df2 = df2)
+	class(ans) <- "summary.tsls"
+	return(ans)
+	}
+
+print.summary.tsls <- function(x, digits = 5, ...)
+	{
+	print.summary.gmm(x,digits)
+	cat("\n First stage F-statistics: \n")
+	if(names(x$fstatistic$fstat)[1]=="(Intercept)")
+		start=2
+	else
+		start=1
+	for (i in start:length(x$fstatistic$fstat))
+		cat(names(x$fstatistic$fstat)[i], 
+		": F(",x$fstatistic$df1,", ",x$fstatistic$df2,") = ",x$fstatistic$fstat[i], 
+		" (P-Vavue = ",x$fstatistic$pvfstat[i],")\n")
+	
+	}
+
+
 print.summary.gmm <- function(x, digits = 5, ...)
 	{
 	cat("\nCall:\n")
@@ -51,6 +86,12 @@
 	print.default(format(x$stest$test, digits=digits),
                       print.gap = 2, quote = FALSE)
 	cat("\n")
+	if(!is.null(x$initTheta))
+		{
+		cat("Initial values of the coefficients\n")
+		print(x$initTheta)
+		cat("\n")
+		}
 	if(!is.null(x$algoInfo))
 		{	
 		cat("#############\n")

Modified: pkg/gmm/R/gel.R
===================================================================
--- pkg/gmm/R/gel.R	2011-11-01 17:58:07 UTC (rev 41)
+++ pkg/gmm/R/gel.R	2011-11-29 22:02:25 UTC (rev 42)
@@ -161,16 +161,22 @@
 	rt <- length(w)
 	if (rt >= 2)
 		{
-		w <- c(w[rt:2], w)
-		w <- w / sum(w)
-		rt <- rt - 1
-		sgt <- function(t) crossprod(x[(t-rt):(t+rt),], w)
-		x[(rt+1):(n-rt),] <- t(sapply((rt + 1):(n - rt), sgt))
+		rt <- length(w)
+		if (rt>1)
+			{
+			w <- c(w[rt:2], w)
+			w <- w / sum(w)
+			w <- kernel(w[rt:length(w)])
+			}
+		else
+			w <- kernel(1)
+
+		x <- kernapply(x,w)		
 		sx <- list("smoothx" = x, "kern_weights" = w)
 		return(sx)		
 		}
 	else
-		sx <- list("smoothx" = x,"kern_weights" = 1)
+		sx <- list("smoothx" = x,"kern_weights" = kernel(1))
 		return(sx)		
 	}
 
@@ -237,7 +243,7 @@
         return(rhof)
         }
       if (ncol(gt) > 1)
-        rlamb <- optim(rep(0, ncol(gt)), rhofct, control = list(maxit = 1000))
+        rlamb <- optim(rep(0, ncol(gt)), rhofct, control = list(maxit = 1000,parscale=rep(.01,ncol(gt))),method="B")
       else
         {
         rlamb <- optimize(rhofct, c(-1,1))

Modified: pkg/gmm/R/getModel.R
===================================================================
--- pkg/gmm/R/getModel.R	2011-11-01 17:58:07 UTC (rev 41)
+++ pkg/gmm/R/getModel.R	2011-11-29 22:02:25 UTC (rev 42)
@@ -28,7 +28,7 @@
     
     if(is.null(object$weightsMatrix))
       {
-      if (object$vcov == "iid")
+      if (object$vcov == "iid" & object$wmatrix != "ident")
       	{
           clname <- "baseGmm.twoStep.formula"
           object$type <- "Linear model with iid errors: Regular IV or 2SLS"
@@ -182,15 +182,14 @@
     rgmm <- gmm(P$g, x, P$tet0, wmatrix = "ident")
 
 
-    P$bwVal <- P$bw(lm(P$g(rgmm$coefficients, x)~1), kernel = P$wkernel, prewhite = P$prewhite, 
+    P$bwVal <- P$bw(centeredGt <- lm(P$g(rgmm$coefficients, x)~1), kernel = P$wkernel, prewhite = P$prewhite, 
                ar.method = P$ar.method, approx = P$approx)
-    P$w <- P$weights(lm(P$g(rgmm$coefficients, x)~1), kernel = P$kernel, bw = P$bwVal, prewhite = P$prewhite, 
-               ar.method = P$ar.method, approx = P$approx, tol = P$tol_weights)
+    P$w <- smoothG(residuals(centeredGt), bw = P$bwVal)$kern_weights
 
-    P$g <- function(thet, x, g1 = P$g1, w = P$w)
+    P$g <- function(thet, x, g1 = P$g1, bw = P$bwVal)
       {
       gf <- g1(thet, x)
-      gt <- smoothG(gf, weights = w)$smoothx
+      gt <- smoothG(gf, bw = bw)$smoothx
       return(gt)
       }
     }
@@ -198,7 +197,7 @@
    {
    P$k1 <- 1
    P$k2 <- 1
-   P$w <- 1
+   P$w <- kernel(1)
    P$bwVal <- 1
    }	
   class(P) <- clname

Modified: pkg/gmm/R/gmm.R
===================================================================
--- pkg/gmm/R/gmm.R	2011-11-01 17:58:07 UTC (rev 41)
+++ pkg/gmm/R/gmm.R	2011-11-29 22:02:25 UTC (rev 42)
@@ -42,6 +42,15 @@
 z
 }
 
+tsls <- function(g,x,data)
+{
+if(class(g) != "formula")
+	stop("2SLS is for linear models expressed as formula only")
+ans <- gmm(g,x,data=data,vcov="iid")
+class(ans) <- c("tsls","gmm")
+return(ans)
+}
+
 getDat <- function (formula, h, data) 
 {
 	cl <- match.call()
@@ -126,8 +135,9 @@
   	{
   	if(type=="2sls")
 	  	{
-  		fsls <- lm(xm~hm-1)$fitted
-  	     par <- lm(ym~fsls-1)$coef
+		restsls <- lm(xm~hm-1)
+  		fsls <- restsls$fitted
+  	     	par <- lm(ym~fsls-1)$coef
 		if (ny == 1)
 		{
   	     	e2sls <- ym-xm%*%par
@@ -182,6 +192,11 @@
 	  value <- crossprod(gb, w%*%gb) 
 
   res <- list(par = par, value = value)
+  if (!is.null(type))
+     {    
+     if (type == "2sls")
+     res$fsRes <- summary(restsls)
+     }
   return(res)
   }
 
@@ -215,6 +230,13 @@
 .objCue <- function(thet, x, P)
   {
   gt <- P$g(thet,x)
+  if (is.null(P$fixedKernW))
+	w <-  weightsAndrews(lm(gt~1), prewhite=P$prewhite,
+ 			   bw = P$bw, kernel = P$kernel, approx = P$approx, 
+ 			   ar.method = P$ar.method, tol = P$tol) 
+  else
+	w <- P$fixedKernW
+
   gbar <- as.vector(colMeans(gt))
   if (P$vcov == "iid")
     w2 <- P$iid(thet, x, P$g, P$centeredVcov)
@@ -227,7 +249,7 @@
         gmat <- P$g(thet,x)
         class(gmat) <- "gmmFct"
       }
-    w2 <- vcovHAC(gmat, weights=P$fixedKernW, sandwich = FALSE)
+    w2 <- vcovHAC(gmat, weights=w, sandwich = FALSE)
    }
   obj <- crossprod(gbar,solve(w2,gbar))
   return(obj)

Modified: pkg/gmm/R/momentEstim.R
===================================================================
--- pkg/gmm/R/momentEstim.R	2011-11-01 17:58:07 UTC (rev 41)
+++ pkg/gmm/R/momentEstim.R	2011-11-29 22:02:25 UTC (rev 42)
@@ -115,17 +115,17 @@
 		return(obj*2)
 		}
 	argDots <- list(...)
-        allArgOptim <- list(par = res$par, fn = .obj1, gr = gr2, x = P$x, w = w, gf = P$g, INV = TRUE)
+        allArgOptim <- list(par = P$t0, fn = .obj1, gr = gr2, x = P$x, w = w, gf = P$g, INV = TRUE)
         allArgOptim <- c(allArgOptim,argDots)
         res2 <- do.call(optim,allArgOptim)
         }
       else
-        res2 <- optim(res$par, .obj1, x = P$x, w = w, gf = P$g, ...)
+        res2 <- optim(P$t0, .obj1, x = P$x, w = w, gf = P$g, ...)
       }
 
     if (P$optfct == "nlminb")
       {
-      res2 <- nlminb(res$par, .obj1, x = P$x, w = w, gf = P$g, ...)
+      res2 <- nlminb(P$t0, .obj1, x = P$x, w = w, gf = P$g, ...)
       res2$value <- res2$objective
       }
 
@@ -136,7 +136,7 @@
       res2$value <- res2$objective
       }	
 
-    z = list(coefficients = res2$par, objective = res2$value, k=k, k2=k2, n=n, q=q, df=df)	
+    z = list(coefficients = res2$par, objective = res2$value, k=k, k2=k2, n=n, q=q, df=df, initTheta = res$par)	
     if (P$optfct == "optim")
 	z$algoInfo <- list(convergence = res2$convergence, counts = res2$counts, message = res2$message)
     else if(P$optfct == "nlminb")
@@ -185,11 +185,12 @@
     if (P$vcov == "iid")
     	{
       res2 <- .tetlin(x, diag(q), dat$ny, dat$nh, dat$k, P$gradv, P$g, type="2sls")
+      initTheta <- NULL
       }
     if (P$vcov == "HAC")
       {
-      w=diag(q)
-      res1 <- .tetlin(x, w, dat$ny, dat$nh, dat$k, P$gradv, P$g)
+      res1 <- .tetlin(x, diag(q), dat$ny, dat$nh, dat$k, P$gradv, P$g, type="2sls")
+      initTheta <- res1$par
       if(P$centeredVcov) 
        	 gmat <- lm(g(res1$par, x)~1)
       else
@@ -202,7 +203,7 @@
 	 res2 <- .tetlin(x, w, dat$ny, dat$nh, dat$k, P$gradv, g)
       }
     
-    z = list(coefficients = res2$par, objective = res2$value, dat=dat, k=k, k2=k2, n=n, q=q, df=df)	
+    z = list(coefficients = res2$par, objective = res2$value, dat=dat, k=k, k2=k2, n=n, q=q, df=df, initTheta = initTheta)	
     }
   z$gt <- g(z$coefficients, x) 
   b <- z$coefficients
@@ -237,6 +238,9 @@
     names(z$coefficients) <- namex
     colnames(z$gt) <- nameh
     }
+  if (P$vcov == "iid" & P$wmatrix != "ident")
+	z$fsRes <- res2$fsRes
+
   class(z) <- paste(P$TypeGmm,".res",sep="")
   return(z)	
   }
@@ -265,15 +269,14 @@
     }
   else
     {
-    w=diag(rep(1, q))
-    res <- .tetlin(x, w, dat$ny, dat$nh, dat$k, P$gradv, g)
+    w <- diag(q)
+    res <- .tetlin(x, w, dat$ny, dat$nh, dat$k, P$gradv, g, type="2sls")
+    initTheta <- res$par
     ch <- 100000
     j <- 1
     while(ch > P$crit)
       {
       tet <- res$par
-      if (P$vcov == "iid")
-        w <- P$iid(tet, x, g, P$centeredVcov)
       if (P$vcov == "HAC")
 	{
         if (P$centeredVcov)
@@ -296,9 +299,11 @@
         cat("No convergence after ", P$itermax, " iterations")
         ch <- P$crit
         }
+	if(P$traceIter)
+		cat("Iter :",j,": value=",res$value,", Coef = ", res$par,"\n") 
         j <- j+1	
       }
-    z = list(coefficients = res$par, objective = res$value, dat=dat, k=k, k2=k2, n=n, q=q, df=df)	
+    z = list(coefficients = res$par, objective = res$value, dat=dat, k=k, k2=k2, n=n, q=q, df=df, initTheta=initTheta)	
    }
   z$gt <- g(z$coefficients, x) 
   b <- z$coefficients
@@ -403,6 +408,7 @@
     }	
   else
     {
+    initTheta = res$par
     ch <- 100000
     j <- 1
     while(ch > P$crit)
@@ -471,10 +477,10 @@
           ch <- P$crit
           }
 	if(P$traceIter)
-		print(c(iter=j,Objective=res$value,res$par))
+		cat("Iter :",j,": value=",res$value,", Coef = ", res$par,"\n") 
         j <- j+1	
       }
-    z = list(coefficients = res$par, objective = res$value,k=k, k2=k2, n=n, q=q, df=df)	
+    z = list(coefficients = res$par, objective = res$value,k=k, k2=k2, n=n, q=q, df=df, initTheta=initTheta)	
     if (P$optfct == "optim")
 	z$algoInfo <- list(convergence = res$convergence, counts = res$counts, message = res$message)
     else if(P$optfct == "nlminb")
@@ -499,6 +505,8 @@
 
 momentEstim.baseGmm.cue.formula <- function(object, ...)
   {
+
+  fixedKernWeights <- TRUE # to be changed or included as an option in gmm() in future version
   P <- object
   g <- P$g
   if (is.null(P$data))
@@ -524,17 +532,30 @@
     {
     if (is.null(P$t0))
 	{
-	P$t0 <- .tetlin(x,diag(q), dat$ny, dat$nh, dat$k, P$gradv, g)$par
-	P$weightMessage <- "Weights for kernel estimate of the covariance are fixed and based on the first step estimate of Theta"	
+	P$t0 <- .tetlin(x,diag(q), dat$ny, dat$nh, dat$k, P$gradv, g, type="2sls")$par
+	initTheta <- P$t0
+	if (fixedKernWeights)
+		P$weightMessage <- "Weights for kernel estimate of the covariance are fixed and based on the first step estimate of Theta"	
+	else
+		P$weightMessage <- "Weights for kernel estimate are variable inside the objective"
 	}
     else
-	P$weightMessage <- "Weights for kernel estimate of the covariance are fixed and based on the initial value of Theta provided by the user"	
+	{
+	initTheta <- P$t0
+	if (fixedKernWeights)
+		P$weightMessage <- "Weights for kernel estimate of the covariance are fixed and based on the initial value of Theta provided by the user"	
+	else
+		P$weightMessage <- "Weights for kernel estimate are variable inside the objective"
+	}
 
-    gt0 <- g(P$t0,x)
-    gt0 <- lm(gt0~1)
-    P$fixedKernW <-  weightsAndrews(gt0, prewhite=P$prewhite,
+    if (fixedKernWeights)
+	    {
+	    gt0 <- g(P$t0,x)
+	    gt0 <- lm(gt0~1)
+            P$fixedKernW <-  weightsAndrews(gt0, prewhite=P$prewhite,
  			   bw = P$bw, kernel = P$kernel, approx = P$approx, 
  			   ar.method = P$ar.method, tol = P$tol) 
+	    }
 
 
     if (P$optfct == "optim")
@@ -550,7 +571,7 @@
       res2$par <- res2$minimum
       res2$value <- res2$objective
       }
-    z = list(coefficients = res2$par, objective = res2$value, dat = dat, k = k, k2 = k2, n = n, q = q, df = df)
+    z = list(coefficients = res2$par, objective = res2$value, dat = dat, k = k, k2 = k2, n = n, q = q, df = df, initTheta=initTheta)
     if (P$optfct == "optim")
 	z$algoInfo <- list(convergence = res2$convergence, counts = res2$counts, message = res2$message)
     else if(P$optfct == "nlminb")
@@ -604,6 +625,7 @@
   if(class(res)=="try-error")
 	stop("Cannot get a first step estimate to compute the weights for the Kernel estimate of the covariance matrix; try different starting values")
 
+  initTheta <- res$coef
   n <- nrow(res$gt)
   q <- ncol(res$gt)
 
@@ -622,7 +644,7 @@
     }	
   else
     {
-    gt0 <- P$g(res$coef,x)
+    gt0 <- P$g(res$coef,x) #Should we compute the weigths with the initial value provided?
     gt0 <- lm(gt0~1)
     P$fixedKernW <-  weightsAndrews(gt0, prewhite=P$prewhite,
  			   bw = P$bw, kernel = P$kernel, approx = P$approx, 
@@ -631,11 +653,11 @@
 
     if (P$optfct == "optim")
       {
-      res2 <- optim(res$coef, .objCue, x = x, P = P, ...)
+      res2 <- optim(P$t0, .objCue, x = x, P = P, ...)
       }
     if (P$optfct == "nlminb")
       {
-      res2 <- nlminb(res$coef, .objCue, x = x, P = P, ...)
+      res2 <- nlminb(P$t0, .objCue, x = x, P = P, ...)
       res2$value <- res2$objective
       }
     if (P$optfct == "optimize")
@@ -644,7 +666,7 @@
       res2$par <- res2$minimum
       res2$value <- res2$objective
       }
-    z = list(coefficients=res2$par,objective=res2$value, k=k, k2=k2, n=n, q=q, df=df)	
+    z = list(coefficients=res2$par,objective=res2$value, k=k, k2=k2, n=n, q=q, df=df, initTheta=initTheta)	
     if (P$optfct == "optim")
 	z$algoInfo <- list(convergence = res2$convergence, counts = res2$counts, message = res2$message)
     else if(P$optfct == "nlminb")
@@ -711,7 +733,7 @@
       rhof <- -sum(rho(gt, lamb, type = P$typel, k = P$k1/P$k2)$rhomat)
       return(rhof)
       }
-    rlamb <- optim(rep(0, ncol(gt)), rhofct, control = list(maxit = 1000))
+    rlamb <- optim(rep(0, ncol(gt)), rhofct, control = list(maxit = 1000,parscale=rep(.01,ncol(gt))), method="B")
     z <- list(coefficients = res$par, conv_par = res$convergence, lambda = rlamb$par)
     z$conv_lambda = paste("Lambda by optim. Conv. code = ", rlamb$convergence, sep = "")
     rho1 <- as.numeric(rho(gt, z$lambda, derive = 1, type = P$typel, k = P$k1/P$k2)$rhomat)

Modified: pkg/gmm/R/specTest.R
===================================================================
--- pkg/gmm/R/specTest.R	2011-11-01 17:58:07 UTC (rev 41)
+++ pkg/gmm/R/specTest.R	2011-11-29 22:02:25 UTC (rev 42)
@@ -44,7 +44,7 @@
 	LM_test <- n*crossprod(x$lambda, crossprod(khat, x$lambda))/(x$bwVal^2)
 	J_test <- n*crossprod(gbar, solve(khat, gbar))/(x$k1^2)
 	test <- c(LR_test, LM_test, J_test)
-	df <- (ncol(x$gt) - length(x$par))
+	df <- (ncol(x$gt) - length(x$coef))
 	ntest <- noquote(paste("Over-identifying restrictions tests: degrees of freedom is ", df, sep = ""))
 	vptest <- pchisq(test,df,lower.tail = FALSE)
 	test <- cbind(test,vptest)

Modified: pkg/gmm/inst/doc/gmm_with_R.pdf
===================================================================
--- pkg/gmm/inst/doc/gmm_with_R.pdf	2011-11-01 17:58:07 UTC (rev 41)
+++ pkg/gmm/inst/doc/gmm_with_R.pdf	2011-11-29 22:02:25 UTC (rev 42)
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[TRUNCATED]

To get the complete diff run:
    svnlook diff /svnroot/gmm -r 42

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