Tyler Ransom tyleransom

## normalMLE.jl
function normalMLE(b::Vector,Y::Array,X::Array,w::Array=ones(size(Y)),d::Array=ones(size(Y)),J::Int64=1)
    @assert size(Y,1)==size(X,2) "X and Y must be the same length"

    T = promote_type(eltype(b), eltype(X))
    like = zero(T)
    @inbounds for i=1:size(Y,1)
        xb = zero(T)
        for j in 1:size(X,1)
            xb += X[j, i] * b[j]
        end

## build-gcc-4.7.sh
#!/bin/bash

#Download source
wget http://mirrors.concertpass.com/gcc/releases/gcc-4.7.2/gcc-4.7.2.tar.bz2
tar xvfj gcc-4.7.2.tar.bz2

cd gcc-4.7.2
./contrib/download_prerequisites

#Download and apply fix to be able to build GCC 4.7 with GCC 5.x

## jump_mwe.jl
using DataFrames
using HTTP
using uCSV
using JuMP
using GLPK
using Cbc

# read in the data
fname = "https://gist.githubusercontent.com/tyleransom/3738fa6cd1b3b5602e0621b08d068380/raw/5c4b139639d7b3c16716289b0232b8528d1bf494/mwe.csv"
data = DataFrame(uCSV.read(IOBuffer(HTTP.get(fname).body), quotes='"', header=1))

## autochoice.csv

          
            id
            male
            income
            size
            choicecat
            dealer1
            dealer2
            dealer3

            
              1
              1
              46.7
              2
              3
              18
              8
              5

            
              2
              1
              26.1
              3
              1
              17
              6
              2

            
              3
              1
              32.7
              4
              1
              12
              6
              2

            
              4
              0
              49.2
              2
              2
              18
              7
              4

            
              5
              1
              24.3
              4
              1
              10
              7
              3

            
              6
              0
              39
              4
              1
              13
              4
              1

            
              7
              1
              33
              2
              1
              24
              7
              4

            
              8
              1
              20.3
              4
              1
              9
              3
              3

            
              9
              1
              38
              3
              2
              14
              10
              2

## SMM_OLS.jl
using Random
using LinearAlgebra
using Statistics
using Distributions
using DataFrames
using OrderedCollections
using SMM

Random.seed!(1234)

## lfetest.R
library(tidyverse)
library(magrittr)
devtools::load_all('~/lfe') # dev branch of lfe from @grant_mcdermott; see https://twitter.com/grant_mcdermott/status/1313900416342474752?s=20
df <- read_csv("https://raw.githubusercontent.com/OU-PhD-Econometrics/fall-2020/master/ProblemSets/PS4-mixture/nlsw88t.csv")
df %<>% mutate(occ_code = as.factor(occ_code))
felm(elnwage5 ~ occ_code | year + idcode, data=df)
# stata equivalent:
# insheet using "https://raw.githubusercontent.com/OU-PhD-Econometrics/fall-2020/master/ProblemSets/PS4-mixture/nlsw88t.csv", comma case
# areg elnwage5 i.occ_code i.year, absorb(idcode)

## analysis.R
library(tidyverse)

letters <- read_csv("https://gist.githubusercontent.com/tyleransom/c197d17b745870a464926435b95d7d9c/raw/3fd2cf7f50ecd9c04bd5adfbe4892f490730a560/beads.csv")

ggplot(data=letters, aes(x=`letter/character`)) +
    geom_bar() +
    geom_text(stat='count', aes(label=..count..), vjust=-1) +
    theme_minimal()

## NHANES.R
library(NHANES)
library(tidyverse)
library(magrittr)
library(modelsummary)

df <- as_tibble(NHANES)

datasummary_skim(df, type="categorical")
datasummary_skim(df, type="numeric")

## JHRchecklist.md

      
              1 file
            
          
              2 forks
            
          
              0 comments
            
          
              10 stars
            
          
                tyleransom
                / JHRchecklist.md
            
            
              Last active
              July 2, 2023 12:30
            
              
                Checklist for converting LaTeX PDF to Microsoft Word for *Journal of Human Resources* publication process
              
          
    Make following formatting changes in LaTeX before importing the resulting PDF into Word


 Sections and subsections numbered like "II.A.1" (add following to end of preamble)

 \renewcommand{\thesection}{\Roman{section}}
 \renewcommand{\thesubsection}{\thesection.\Alph{subsection}}
 \renewcommand{\thesubsubsection}{\thesubsection.\arabic{subsubsection}}


 roman numeraled endnotes instead of footnotes (add following to preamble)

 \usepackage{endnotes}
 \let\footnote=\endnote
 \renewcommand{\theendnote}{\roman{endnote}}


 put "\theendnotes" just before the online appendix


## chutesAndLadders.jl
wait_for_key(prompt) = (print(stdout, prompt); read(stdin, 1); nothing)

user_input(prompt) = (print(prompt); p = readline(); p)

function spin()
    wait_for_key("Press enter to spin\n")
    spun = rand(1:6)
    println("You spun a ",spun)
    return spun
end
	function normalMLE(b::Vector,Y::Array,X::Array,w::Array=ones(size(Y)),d::Array=ones(size(Y)),J::Int64=1)
	@assert size(Y,1)==size(X,2) "X and Y must be the same length"

	T = promote_type(eltype(b), eltype(X))
	like = zero(T)
	@inbounds for i=1:size(Y,1)
	xb = zero(T)
	for j in 1:size(X,1)
	xb += X[j, i] * b[j]
	end
	#!/bin/bash

	#Download source
	wget http://mirrors.concertpass.com/gcc/releases/gcc-4.7.2/gcc-4.7.2.tar.bz2
	tar xvfj gcc-4.7.2.tar.bz2

	cd gcc-4.7.2
	./contrib/download_prerequisites

	#Download and apply fix to be able to build GCC 4.7 with GCC 5.x
	using DataFrames
	using HTTP
	using uCSV
	using JuMP
	using GLPK
	using Cbc

	# read in the data
	fname = "https://gist.githubusercontent.com/tyleransom/3738fa6cd1b3b5602e0621b08d068380/raw/5c4b139639d7b3c16716289b0232b8528d1bf494/mwe.csv"
	data = DataFrame(uCSV.read(IOBuffer(HTTP.get(fname).body), quotes='"', header=1))
id	male	income	size	choicecat	dealer1	dealer2	dealer3
1	1	46.7	2	3	18	8	5
2	1	26.1	3	1	17	6	2
3	1	32.7	4	1	12	6	2
4	0	49.2	2	2	18	7	4
5	1	24.3	4	1	10	7	3
6	0	39	4	1	13	4	1
7	1	33	2	1	24	7	4
8	1	20.3	4	1	9	3	3
9	1	38	3	2	14	10	2
	using Random
	using LinearAlgebra
	using Statistics
	using Distributions
	using DataFrames
	using OrderedCollections
	using SMM

	Random.seed!(1234)
	library(tidyverse)
	library(magrittr)
	devtools::load_all('~/lfe') # dev branch of lfe from @grant_mcdermott; see https://twitter.com/grant_mcdermott/status/1313900416342474752?s=20
	df <- read_csv("https://raw.githubusercontent.com/OU-PhD-Econometrics/fall-2020/master/ProblemSets/PS4-mixture/nlsw88t.csv")
	df %<>% mutate(occ_code = as.factor(occ_code))
	felm(elnwage5 ~ occ_code \| year + idcode, data=df)
	# stata equivalent:
	# insheet using "https://raw.githubusercontent.com/OU-PhD-Econometrics/fall-2020/master/ProblemSets/PS4-mixture/nlsw88t.csv", comma case
	# areg elnwage5 i.occ_code i.year, absorb(idcode)
	library(tidyverse)

	letters <- read_csv("https://gist.githubusercontent.com/tyleransom/c197d17b745870a464926435b95d7d9c/raw/3fd2cf7f50ecd9c04bd5adfbe4892f490730a560/beads.csv")

	ggplot(data=letters, aes(x=`letter/character`)) +
	geom_bar() +
	geom_text(stat='count', aes(label=..count..), vjust=-1) +
	theme_minimal()
	library(NHANES)
	library(tidyverse)
	library(magrittr)
	library(modelsummary)

	df <- as_tibble(NHANES)

	datasummary_skim(df, type="categorical")
	datasummary_skim(df, type="numeric")
	wait_for_key(prompt) = (print(stdout, prompt); read(stdin, 1); nothing)

	user_input(prompt) = (print(prompt); p = readline(); p)

	function spin()
	wait_for_key("Press enter to spin\n")
	spun = rand(1:6)
	println("You spun a ",spun)
	return spun
	end