Querying and Analyzing Google Domestic Trends Data

Similar to Google Trends, Google Domestic Trends is a set of indices created by aggregating search volumes for groups of queries that are related to a specific sector.

In this IPython Notebook, I go through some statistical tests in Python with Google Domestic Trends data using searches by automotive buyers (queries such as "cars, kelly blue book, auto, used cars, toyota, autotrader") to try and predict the volume of search queries related to automotive financing (queries such as "lease, mileage, loan calculator, auto loan, car payment").

I also do some basic tests of periodicity in the data, as well as provide a Python wrapper for querying Google Domestic Trends to return a pandas DataFrame.

Estimate an Econometric Model from Scraped Data in Real-Time using Python

Here's a link to a fairly lengthy tutorial I put together titled "Estimate an Econometric Model from Scraped Data in Real-Time using Python" which is posted at another one of my websites:  live.economics.io

The application that's analyzed in the tutorial involves determining the factors that influence the price of a desktop CPU, using the prices and features of processors that are currently listed at newegg.com as the data set for the model.

Lastly, if you haven't been to econpy.org in a while, check out the recently updated interface which now includes an IPython terminal right in the browser -- special thanks to PythonAnywhere on that one!

Using Python for Econometrics and Linear Algebra

The following links contain Python code for various tasks in econometrics and linear algebra. They come from the owner of the blog, Digital Explorations. As a PhD student in economics, and a Python enthusiast myself (see: econpy.org), I am more than happy to see Python code like this being created.

• Cholesky decomposition and inverse of positive definite matrices
• Solving an AR(p) time series model using least squares
• Solving Ax = b where A is symmetric positive definite
• White's test for heteroscedasticity
• Breusch-Godfrey test for serial correlation up to order p
• Nonlinear regression, fitting of linearizable nonlinear equations to data
• Performing one iteration of Cochrane-Orcutt procedure
• Iterated Cochrane-Orcutt procedure
• Ljung-Box test for autocorrelation
• Statistics: Computing quantiles
• Gramm Schmidt orthogonalization algorithm in Python
• QR decomposition with Gramm-Schmidt orthogonalization Python
• Gauss elimination in Python
• Computing determinants via diagonalization