Initial Postings: Read and reflect on the assigned readings for the week. Then post what you thought was the most important concept(s), method(s), term(s), and/or any other thing that you felt was worthy of your understanding in each assigned textbook chapter.
Your initial post should be based upon the assigned reading for the week, so the textbook should be a source listed in your reference section and cited within the body of the text. Other sources are not required but feel free to use them if they aid in your discussion.
Also, provide a graduate-level response to each of the following questions:

In Chapter 10 the focus of the material is identifying and assessing data. One of the chief concerns of identifying and assessing data is extrapolation and interpolation. Please explain both of these concepts and give a reason why either of these scenarios would occur.

[Your post must be substantive and demonstrate insight gained from the course material. Postings must be in the student’s own words – do not provide quotes!] [Your initial post should be at least 150 words and in APA format (including Times New Roman with font size 12 and double spaced). 

Chapter10.IdentificationandDataAssessment.pptx

Identification and Data Assessment
Chapter 10
© 2019 McGraw-Hill Education. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or distribution without the prior written consent of McGraw-Hill Education

Learning Objectives
Explain what it means for a variable’s effect to be identified in a model
Explain extrapolation and interpolation and how each inherently suffers from an identification problem
Distinguish between functional form assumptions and enhanced data coverage as remedies for identification problems stemming from exploration and interpolation
Differentiate between endogeneity and types of multicollinearity as identification problems due to variable co-movement
Articulate remedies for identification problems and inference challenges due to variable co-movement
Solve for the direction of bias in cases of variable co-movement

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