285 lines
6.5 KiB
Plaintext
285 lines
6.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# R\n",
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"\n",
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"using the [RCall.jl](https://juliainterop.github.io/RCall.jl/stable/) package."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"CovNWFn"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"using Printf, DelimitedFiles, LinearAlgebra, Statistics\n",
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"\n",
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"include(\"jlFiles/printmat.jl\")\n",
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"include(\"jlFiles/OlsNW.jl\") #functions for OLS"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Sample size: (388,)\n"
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]
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}
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],
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"source": [
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"x = readdlm(\"Data/FFmFactorsPs.csv\",',',skipstart=1)\n",
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"\n",
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" #yearmonth, market, small minus big, high minus low\n",
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"(ym,Rme,RSMB,RHML) = (x[:,1],x[:,2]/100,x[:,3]/100,x[:,4]/100) \n",
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"x = nothing\n",
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"\n",
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"printlnPs(\"Sample size:\",size(Rme))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Do OLS (in Julia)\n",
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"\n",
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"use the function sin the file OlsNW.jl to do OLS. Report point estimates and standard errors."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[34m\u001b[1mOLS Results (assuming iid residuals):\u001b[22m\u001b[39m\n",
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"\n",
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" b std_iid\n",
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"c 0.007 0.002\n",
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"SMB 0.217 0.073\n",
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"HML -0.429 0.074\n",
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"\n"
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]
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}
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],
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"source": [
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"Y = Rme\n",
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"T = size(Y,1)\n",
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"X = [ones(T) RSMB RHML]\n",
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"\n",
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"(b,u,Yhat,V,R2) = OlsGMFn(Y,X)\n",
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"std_iid = sqrt.(diag(V))\n",
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"\n",
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"printblue(\"OLS Results (assuming iid residuals):\\n\")\n",
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"xNames = [\"c\",\"SMB\",\"HML\"]\n",
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"printmat([b std_iid],colNames=[\"b\",\"std_iid\"],rowNames=xNames)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Getting Started with RCall"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"using RCall"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"RObject{VecSxp}\n",
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"\n",
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"Call:\n",
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"lm(formula = Y ~ X + 0)\n",
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"\n",
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"Residuals:\n",
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" Min 1Q Median 3Q Max \n",
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"-0.20224 -0.02477 0.00335 0.02663 0.11840 \n",
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"\n",
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"Coefficients:\n",
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" Estimate Std. Error t value Pr(>|t|) \n",
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"X1 0.006983 0.002205 3.167 0.00166 ** \n",
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"X2 0.216968 0.073565 2.949 0.00338 ** \n",
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"X3 -0.429088 0.073710 -5.821 1.23e-08 ***\n",
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"---\n",
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"Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
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"\n",
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"Residual standard error: 0.04295 on 385 degrees of freedom\n",
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"Multiple R-squared: 0.1488,\tAdjusted R-squared: 0.1422 \n",
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"F-statistic: 22.44 on 3 and 385 DF, p-value: 2.07e-13\n",
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"\n",
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"\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"RObject{VecSxp}\n",
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"\n",
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"Call:\n",
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"lm(formula = Y ~ X + 0)\n",
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"\n",
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"Coefficients:\n",
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" X1 X2 X3 \n",
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" 0.006983 0.216968 -0.429088 \n",
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"\n"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"@rput X Y\n",
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"\n",
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"ResultsR = reval(\"summary(mod <- lm(Y ~ X+0))\") #print summary of regression\n",
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"println(ResultsR)\n",
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"\n",
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"resultsR = reval(\"mod <- lm(Y ~ X+0)\") #get all output"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[:coefficients, :residuals, :effects, :rank, Symbol(\"fitted.values\"), :assign, :qr, Symbol(\"df.residual\"), :xlevels, :call, :terms, :model]\n"
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]
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}
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],
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"source": [
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"println(names(resultsR)) #print all keys (field names)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Task 1\n",
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"\n",
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"Print the Julia and Python estimates (of the coefficients) in a table so we can compare directly."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Task 2\n",
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"\n",
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"Print the smallest and largest values of the difference between the residuals according to Julia and those according to Python."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# OLS (in Julia) with Robust Standard Errors\n",
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"\n",
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"Use standard errors that are robust to heteroskedastcity and autocorrelation (2 lags)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[34m\u001b[1mOLS Results (robust std):\u001b[22m\u001b[39m\n",
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"\n",
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" b std_nw\n",
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"c 0.007 0.002\n",
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"SMB 0.217 0.129\n",
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"HML -0.429 0.118\n",
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"\n"
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]
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}
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],
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"source": [
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"(b,u,Yhat,V,R2) = OlsNWFn(Y,X,2)\n",
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"std_nw = sqrt.(diag(V))\n",
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"\n",
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"printblue(\"OLS Results (robust std):\\n\")\n",
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"xNames = [\"c\",\"SMB\",\"HML\"]\n",
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"printmat([b std_nw],colNames=[\"b\",\"std_nw\"],rowNames=xNames)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Task 3 \n",
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"\n",
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"Now redo the R estimation with the same sort of robust standard errors. Hint: the `NeweyWest` in the `sandwich`package."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"@webio": {
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"lastCommId": null,
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"lastKernelId": null
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},
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"kernelspec": {
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"display_name": "Julia 1.6.3",
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"language": "julia",
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"name": "julia-1.6"
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},
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"language_info": {
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"file_extension": ".jl",
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"mimetype": "application/julia",
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"name": "julia",
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"version": "1.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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