EconometricsBySimulation

# First let's load some libraries
require('XML'); require('reshape2'); require('ggplot2')
require('magrittr') # See 
# http://goo.gl/Wj5F87

# I have borrowed Andrie's code from stackoverflow
# http://goo.gl/noYVo7
source("http://goo.gl/w64gfp")

# Load in the google spreadsheet

EconometricsBySimulation

# This set of functions comes entirely form Andrie on stackoverflow
# http://stackoverflow.com/questions/22873602/importing-data-into-r-from-google-spreadsheet

readGoogleSheet <- function(url, na.string="", header=TRUE){
  stopifnot(require(XML))
  # Suppress warnings because Google docs seems to have incomplete final line
  suppressWarnings({
    doc <- paste(readLines(url), collapse=" ")
  })
  if(nchar(doc) == 0) stop("No content found")

EconometricsBySimulation

I recently found myself in need of a function to sample randomly from an arbitrarily defined probability density function. An excellent post by Quantitations shows how to accomplish this using some of Rs fairly sophisticated functional approximation tools such as integrate and uniroot. The only problem with this excellent post was that the machine cost was enormous with samples of 1000 draws taking 10 seconds on my machine and repeated samples of 100,000+ draws (which I was after) clearly being unworkable.

Thus I decided to take my own crack at it. First let us review the basics of drawing random variables from non-uniform distributions. The standard method I think most algorithms use works as follows:
  
  Assumptions
1. You can draw pseudo-random uniform variable u
2. You can integrate the pdf to construct a cdf
$$p = F(x) = \int_{-\infty}^\infty f(x) dx$$
  3. You can invert the cdf in order to solve for p
$$G(F(x))=F^{-1}(F(x))=F^{-1}(p)=x$$

EconometricsBySimulation

require(plyr)
require(ggplot2)
require(scales)

# Download data from:
# http://www.ssa.gov/oact/babynames/names.zip
setwd("C:/Data/SS-names/")
files<-list.files()
files<-files[grepl(".txt",files)]

EconometricsBySimulation

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            <Keywords name="Delimiters">&quot;00&quot;00</Keywords>
            <Keywords name="Folder+">begin function type for while if</Keywords>

EconometricsBySimulation

_grmedf,_gstd01,actest,addest,addtxt,adjust,adodev,adoedit,adolist,adoupdate,aipe,akdensity,allpossible,alt,amcmc,an16_1,anovacontrast,anovalator,apc,apcd,apch,areastack,arimafit,array,art,ashell,atkplot,atsinst,attrib,avplot3,backrasch,backup,bayesmlogit,bcii,bcoeff,betafit,betaprior,bic,bicdrop1,binary_mediation,bincoverage,biplotvlab,birthsim,blist,blogit2,bmjcip,bpmedian,bronch,bs4rw,bspline,bstut,bsweights,bugsdat,bxmodel,bygap,bynote,bys,bystore,byvar,calibest,canred,catdev,catenate,cb2html,ccinter,cctable,cellgr,cem,cf2,chardef,charlist,charlson,checkrob,checkvar,chidemo,china_map,china_spatdwm,ci_marg_mu,cidemo,cidemo2,cij,circular,cistat,ciw,ckvar,clarify,cleanchars,cleanlog,clrbound,clstop_lbt,clt,clustpop,clustsens,cmp,cns,cnsrsig,codci,codebook2,coefplot,cohend,collapse2,collapseunique,collin,colortrans,compado,conddens,condivreg,confirmdir,contrast,copypkg,coranal,cordemo,corrsq,corrtex,cpr,cprplots,cqiv,crib10,crib11,crib12,crib13,crib9,csgof,csinter,csjl,cstable,csvconvert,cureregr,datelist,de2

EconometricsBySimulation

####### Excel summary exporter #######
## Modified from Smart, Francis ##
## Jean P. Gibert, 2014 ##

lmOut <- function(res, file="test.csv", ndigit=3, writecsv=T) {
  # If summary has not been run on the model then run summary
  if (length(grep("summary", class(res)))==0) res <- summary(res)
  co <- res$coefficients
  nvar <- nrow(co)
  ncoll <- ncol(co)

EconometricsBySimulation

# R - Doodling in R

# I am working on creating some functions that will be capable of creating shapes and plots that look hand drawn.

# I have made some progress in this goal.

# In that process I have also discovered that I can make some doodles that look hand drawn as well.

# In order to accomplish the goal of simulating hand drawing I want to simulate the momentum of hand writing.

EconometricsBySimulation

lmOut <- function(res, file="test.csv", ndigit=3, writecsv=T) {
  # If summary has not been run on the model then run summary
  if (length(grep("summary", class(res)))==0) res <- summary(res)
  
  co <- res$coefficients
  nvar <- nrow(co)
  ncol <- ncol(co)
  f <- res$fstatistic
  formatter <- function(x) format(round(x,ndigit),nsmall=ndigit)
  

EconometricsBySimulation

clear

set obs 4000

gen id = _n

gen eta1 = rnormal()
gen eta2 = rnormal()

* Generate 5 irrelevant factors that might affect each of the