This lecture covers the concept of Ordinary Least Squares Regression (OLS) analysis, focusing on the relationship between variables and the calculation of square errors. It also discusses the first stage restriction criterion and exclusion restrictions in regression models.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
Deserunt nulla eiusmod sit aliqua nostrud officia dolore magna amet cupidatat laboris consequat nulla duis. Quis commodo culpa est excepteur ex dolore tempor. Laborum nulla tempor esse adipisicing Lorem incididunt sint sint. Non laboris minim adipisicing pariatur velit proident irure.
Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
Explores heteroskedasticity and autocorrelation in econometrics, covering implications, applications, testing methods, and hypothesis testing consequences.