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econpy / IPython Notebook: Google Domestic Trends OLS & Autocorrelation
Last active December 14, 2015 15:18
Google Domestic Trends: Using automotive buyer index to predict automotive financing index.
@econpy
econpy / Logistic_Regression in Python
Created March 13, 2013 23:10
Logistic Regression in Python
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"metadata": {
"name": "Logistic Regression"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
@econpy
econpy / Search Engine HHI
Last active December 16, 2015 11:29
Analysis of search engine market shares across countries and time.
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"name": "hhi"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
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"cells": [
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@econpy
econpy / Preferences.sublime-settings
Created November 16, 2013 07:50
Sublime 3 User Settings
{
"always_show_minimap_viewport": true,
"bold_folder_labels": true,
"caret_style": "solid",
"color_scheme": "Packages/Theme - Flatland/Flatland Dark.tmTheme",
"draw_minimap_border": true,
"enable_telemetry": false,
"ensure_newline_at_eof_on_save": true,
"flatland_sidebar_tree_xsmall": true,
"flatland_square_tabs": true,
@econpy
econpy / MetaParser.py
Created April 24, 2014 08:38
MetaParser.py file for Presidio that correctly calculates the date used by the Bookworm GUI (number of days since 0000-01-01, including leap years).
from datetime import date, datetime
import json
fields_to_derive = []
def DaysSinceZero(dateobj):
numdays = 0
for yr in range(dateobj.year):