272 lines
8.6 KiB
Plaintext
272 lines
8.6 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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"# Python\n",
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"\n",
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"using the [PyCall.jl](https://github.com/JuliaPy/PyCall.jl) 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 PyCall"
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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 PyCall\n",
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"sm = pyimport(\"statsmodels.api\"); #activate this 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": 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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"PyObject <class 'statsmodels.iolib.summary.Summary'>\n",
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"\"\"\"\n",
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" OLS Regression Results \n",
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"==============================================================================\n",
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"Dep. Variable: y R-squared: 0.134\n",
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"Model: OLS Adj. R-squared: 0.130\n",
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"Method: Least Squares F-statistic: 29.85\n",
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"Date: Mon, 08 Nov 2021 Prob (F-statistic): 8.88e-13\n",
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"Time: 16:17:13 Log-Likelihood: 672.28\n",
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"No. Observations: 388 AIC: -1339.\n",
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"Df Residuals: 385 BIC: -1327.\n",
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"Df Model: 2 \n",
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"Covariance Type: nonrobust \n",
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"==============================================================================\n",
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" coef std err t P>|t| [0.025 0.975]\n",
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"------------------------------------------------------------------------------\n",
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"const 0.0070 0.002 3.167 0.002 0.003 0.011\n",
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"x1 0.2170 0.074 2.949 0.003 0.072 0.362\n",
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"x2 -0.4291 0.074 -5.821 0.000 -0.574 -0.284\n",
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"==============================================================================\n",
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"Omnibus: 58.863 Durbin-Watson: 1.849\n",
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"Prob(Omnibus): 0.000 Jarque-Bera (JB): 146.539\n",
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"Skew: -0.749 Prob(JB): 1.51e-32\n",
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"Kurtosis: 5.612 Cond. No. 38.8\n",
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"==============================================================================\n",
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"\n",
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"Notes:\n",
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"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\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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"resultsP = sm.OLS(Y, X).fit() #can use Python functions directly\n",
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"\n",
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"println(resultsP.summary())"
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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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"[:HC0_se, :HC1_se, :HC2_se, :HC3_se, :_HCCM, :__class__, :__delattr__, :__dict__, :__dir__, :__doc__, :__eq__, :__format__, :__ge__, :__getattribute__, :__gt__, :__hash__, :__init__, :__init_subclass__, :__le__, :__lt__, :__module__, :__ne__, :__new__, :__reduce__, :__reduce_ex__, :__repr__, :__setattr__, :__sizeof__, :__str__, :__subclasshook__, :__weakref__, :_abat_diagonal, :_cache, :_data_attr, :_data_in_cache, :_get_robustcov_results, :_is_nested, :_use_t, :_wexog_singular_values, :aic, :bic, :bse, :centered_tss, :compare_f_test, :compare_lm_test, :compare_lr_test, :condition_number, :conf_int, :conf_int_el, :cov_HC0, :cov_HC1, :cov_HC2, :cov_HC3, :cov_kwds, :cov_params, :cov_type, :df_model, :df_resid, :diagn, :eigenvals, :el_test, :ess, :f_pvalue, :f_test, :fittedvalues, :fvalue, :get_influence, :get_prediction, :get_robustcov_results, :info_criteria, :initialize, :k_constant, :llf, :load, :model, :mse_model, :mse_resid, :mse_total, :nobs, :normalized_cov_params, :outlier_test, :params, :predict, :pvalues, :remove_data, :resid, :resid_pearson, :rsquared, :rsquared_adj, :save, :scale, :ssr, :summary, :summary2, :t_test, :t_test_pairwise, :tvalues, :uncentered_tss, :use_t, :wald_test, :wald_test_terms, :wresid]\n"
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]
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}
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],
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"source": [
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"println(keys(resultsP)) #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 Python estimation with the same sort of robust standard errors. Hint: `resultsP.get_robustcov_results()`"
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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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