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# APPLIED ECONOMETRICS I AAEC 5307

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This 2 page Study Guide was uploaded by Dwight Marquardt IV on Thursday October 22, 2015. The Study Guide belongs to AAEC 5307 at Texas Tech University taught by Eric Belasco in Fall. Since its upload, it has received 37 views. For similar materials see /class/226376/aaec-5307-texas-tech-university in Agriculture Education at Texas Tech University.

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Date Created: 10/22/15

AAEC 5307 KEY CONCEPTS TO KNOW l Differentiatedescribe the following terms crosssection data timeseries data pooled cross sections panel data Chapter 1 2 Math StatMatrix Algebra Review a Explain the following concepts unbiasedness consistency efficiency Math Stat Review b DefineDescribeExplain Transpose Trace Inverse Idempotent Matrix Algebra Review c How to addsubtractmultiply matrices Matrix Algebra Review 3 Simple Linear Regression Topics Chapter 2 a Know how to derive 30 and 51 either through Method of Moments or Minimizing Least Squares b Conceptually differentiate between the process of Method of Moments and Minimizing Least Squares c What is Rsquared Describe Define AdvantageDiadvantage Be able to describe in words d Expected Value of estimators Unbiasedness i Assumptions SLR lSLR4 Be able to explain each ii Concept of being unbiased in repeated samples Not necessarily for a particular sample iii Proof of unbiasedness for 51 in simple regression iii Interpretation of intercept and slope parameters Variance of estimators i Explain the concept of Homoskedasticity D ii Deriving the variance of 51 for the simple regression case iii Implications of larger sample sizes and larger error variances f Other issues i Effects of changes in unit of measurements ii Functional forms 7 especially Log forms Be able to interpret slope parameters given the log specification Differences between loglog loglin linlog 4 Multiple Linear Regression Estimation Chapter 3 a Derving the vector of parameters B matrix algebra approach b Interpretation of slope parameters Just know how to interpret c Concept of Rsquared as a goodness of fit measure explain implications of adding variables when can you compare Rsquareds d Expected Value Unbiasedness i Assumptions needed for unbiasedness Be able to explain each ii Proof of unbiasedness Matrix algebra approach iii Implications of including irrelevant variables and omitting an included variable Proof of bias in omitting variables Matrix Algebra How to guess the direction of the bias e Variance of estimators 5 6 7 00 i What is the additional assumption needed Homoskedasticity 7 be able to explain ii Deriving the variance of B Matrix Algebra Approach iii Unbiasedness of the error variance estimator Matrix Algebra Approach iv Implications of misspeci cation to the variance of the estimator especially including an irrelevant variable v Conceptually explaining the GaussMarkov theorem Know the proof and be able to explain well in words Multiple Linear Regression Inference Chapter 4 a Normality Assumption Why is it important b How do you test hypothesis about a single parameter in the model Constructing null and alternative hypothesis Computing interpreting tstatistic and pvalues Constructing con dence intervals c Testing linear combinations how to restate regression equation to be able to test d Testing multiple restrictions Ftest i How to conduct test Interpretation ii Relationship between F and t tests Multiple Regression Analysis Asymptotic Properties Chapter 5 a De nition of Consistency Assumptions that assure consistency b Deriving direction of inconsistency c LM test d De nition of asymptotic ef ciency Multiple Regression Analysis Further Issues Chapter 6 a What are effects of scaling on y and x variables b De ne a beta coef cient c Functional Forms i InterpretDescribe different log models ii When and why use log models iii InterpretDescribe quadratic models d Adjusted Rsquared Define Describe e How to make Con dence intervals for predictions f How to predict y in a log model g How to compare Rsquared in log and level models Multiple Regression Analysis Dummy Variables Chapter 7 a Interpreting coef cients of dummy independent variables b How to incorporate dummy variables to represent multiple categories c Interpretation of dummy interactions d Chow test be able to describe conduct and interpret e Linear Probability model De neDescribeDisadvantages f Explain selfselection problem

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