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Understanding Dummy Variables in Regression Analysis, Study notes of Economic Analysis

Regression AnalysisStatistical InferenceEconometrics

The concept of dummy variables in regression analysis, their interpretation, and how to use them as regressors or the dependent variable. It also covers the chow test and its significance in testing the null hypothesis of no difference between groups. The document also discusses the difference between numerical and ordinal variables and how to transform them into dummy variables for regression analysis.

What you will learn

  • What is the role of dummy variables in regression analysis?
  • What is the Chow Test and how is it used in regression analysis?
  • How to interpret the coefficients of dummy variables in a regression model?

Typology: Study notes

2021/2022

Uploaded on 09/12/2022

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Download Understanding Dummy Variables in Regression Analysis and more Study notes Economic Analysis in PDF only on Docsity! 1 Chapter 7, Dummy Variable Dummy variable can only take values 1 and 0. It is categorical, that means the numbers 1 and 0 have no numerical meanings (we cannot say 1 is greater than 0). In this chapter we use dummy as regressor. Chapter 17 (covered in eco411) shows how to use dummy as the dependent variable. First letโ€™s use wage data and consider a simple regression ๐‘ค๐‘Ž๐‘”๐‘’ = ๐›ฝ0 + ๐›ฝ1๐ท + ๐‘ข (1) where ๐ท = 0 for male, and ๐ท = 1 for female. For dummy variable, you have to be clear ๐ท = 0 is for which group (called base group). Later all comparisons are made relative to the base group. You can report the frequency of ๐ท using tab D. The key to understand the dummy-variable-model is to discuss: when = 0, ๐‘ค๐‘Ž๐‘”๐‘’ = _____________________. If we take expectation we get ___________________ when = 1, ๐‘ค๐‘Ž๐‘”๐‘’ = _____________________. If we take expectation we get ___________________ So ๐›ฝ0 can be interpreted as ________________________; and ๐›ฝ1 can be interpreted as____________________ This result suggests that we can conduct the two-sample t test (the comparison of means test, stata command: ttest wage, by(D)) using the simple regression (1) that involves dummy. Now consider a multiple regression ๐‘ค๐‘Ž๐‘”๐‘’ = ๐›ฝ0 + ๐›ฝ1๐ท + ๐›ฝ2๐‘ฅ + ๐›ฝ3(๐ท โˆ— ๐‘ฅ) + ๐‘ข (2) For example x can be exper, and ๐ท โˆ— ๐‘ฅ is the interaction term (product of) x and dummy. Letโ€™s discuss again: when ๐ท = 0, _________________________________________________________________________________________ when ๐ท = 1, _________________________________________________________________________________________ ๐›ฝ0 can be interpreted as _________________________________________________________; ๐›ฝ1 can be interpreted as _________________________________________________________; ๐›ฝ2 can be interpreted as _________________________________________________________; ๐›ฝ3 can be interpreted as _________________________________________________________; How to show ๐›ฝ1 and ๐›ฝ3 in graph?
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