Practical Regression: Time Series and Autocorrelation Case Solution & Answer

This is the twelfth in a series of lecture notes that, tied together in a manual, you might call “practical regression.” The purpose of these notes is to complement the theoretical content of most statistics texts practices based on nearly three decades of experience of the author, along with over a hundred years experience of colleagues who have offered advice tips. As the title of “practical regression” implies, these notes are a guide to regression in practice. This white paper examines the time series data. The note explains the concept of change over time and how to capture the trend by regressing. Most of the note is devoted to the problem of autocorrelation. The note concludes with a discussion of the use of leads and lags as predictor variables.
by
David Dranove
Source: Kellogg School of Management
7 pages.
Release: June 11, 2012. Prod #: KEL646-PDF-ENG
Practical Regression and Time Series Autocorrelation case solution

Related Case Solutions:

Practical Regression: Building Your Model: What Variables to Include Practical Regression: Noise Heteroskedasticity and Grouped Data Practical Regression: Time Series and Autocorrelation Practical Regression: Regression Basics Practical Regression: Regression Basics Practical Regression: Log vs. Linear Specification Practical Regression: Log vs. Linear Specification Practical Regression: Discrete Dependent Variables Practical Regression: Causality and Instrumental Variables Practical Regression: Maximum Likelihood Estimation