Python Guide for Introductory Econometrics for Finance by Brooks Chris

Author:Brooks, Chris [Brooks, Chris]
Language: eng
Format: epub
Publisher: Cambridge University Press
Published: 2019-03-27T16:00:00+00:00

11

Constructing ARMA models

Reading: Brooks ( 2019 , sections 6.4 – 6.7)

Getting started

This example uses the monthly UK house price series which was already incorporated in Python as an example in section 2. In this section, we re-load the data into the NoteBook. Recall the procedure of importing data from an Excel workfile and generating a new variable ’dhp’, we type the command as follows (In [1]). To facilitate calculations, we save the data by pickle module for future usage (In [2]).

There are a total of 326 monthly observations running from February 1991 (recall that the January observation was ’lost’ in constructing the lagged value) to March 2018 for the percentage change in house price series. The objective of this exercise is to build an ARMA model for the house price changes. Recall that there are three stages involved: identification, estimation and diagnostic checking. The first stage is carried out by looking at the autocorrelation and partial autocorrelation coefficients to identify any structure in the data.

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