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Overview of Truncated and Censored Regression Models with Application to Restaurant Expenditures

This document outlines key topics in truncated and censored regression models, beginning with a discussion on the truncated regression model that assumes normal error terms. It reviews the sample likelihood function in the context of a Canadian restaurant expenditure example, including a description of the relevant data. The document also provides an overview of Gauss code and results, delving into censored regression models, specifically Tobit regression. Key readings from influential authors such as McDonald and Moffitt (1980) are recommended for further understanding.

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Overview of Truncated and Censored Regression Models with Application to Restaurant Expenditures

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  1. AAE637 Topics for May 3, 2007 • The Truncated Regression Model Assuming Normal Error Terms • The Sample Likelihood Function • Canadian Restaurant Expend. Example • Description of Data • Overview of Gauss Code/Results • Introduction to Censored Regression Models • Moments of a Censored RV • Implementation of the Censored (Tobit) Regression Model • Readings: McDonald and Moffitt (1980), Norris and Batie (1987), Adesina and Zinnah (1993), Greene (1999)

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