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21 Cards in this Set
- Front
- Back
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Sampling error
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Arises by chance, esp. in small samples
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Omitted variable bias
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Bias is systematically correlated with error
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Latent variables
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Affect the model but are not directly observable
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Endogenous variable
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How aquired value provides info that tells us more about dependent variable
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Exogenous variable
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Process would not provide any additional information
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Two conditions for instrumental variables
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1) Correlation between z and x
2) z is uncorrelated with u |
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Problem with 2SLS
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Produces larger error terms
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Structural equation
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The original equation, including the endogenous variable(s)
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Reduced form equation
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Endogenous variable in written in terms of exogenous variables
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First stage of 2SLS
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Regress endogenous variable(s) on exogenous variables, including z
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Exclusion restrictions
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Need as many excluded instruments as there are endogenous variables
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Finite sample bias
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As n approaches infinity, endogeneity approaches 0
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What happens when you do the first stage incorrectly?
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The influence of the variables that were lefft out goes into error term.
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Why 2SLS errors are bigger than OLS errors
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1) Only use some info
2) Variation in the error term 3) The more corr there is in the orig. model, the harder it is to detect indiv. influences |
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Test for endogeneity
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Hausman Test
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Hausman Test
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2 estimators:
-consistent & efficient under Ho -consistent under Ho and Ha |
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Consistenct estimator
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As n --> infinity, coefficients converge toward true values
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Inconsistent estimator
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As n --> infinity, coefficients converge on wrong value
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Efficient estimator
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Results in smaller standard errors than any other estimator of its type (ie, linear)
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Hausman Ho
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Independent variables are exogenous
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Hausman Ha
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At least one independent variable is endogenous
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