Wooldridge PPT ch9

Under econometrics prof keunkwan ryu 16 measurement

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under Econometrics Prof. Keunkwan Ryu 16 Measurement Error in an Explanatory Variable (cont) Notice that the multiplicative error is just Var( x 1 *)/Var( x 1 ) Since Var( x 1 *)/Var( x 1 ) < 1, the estimate is biased toward zero – called attenuation bias It’s more complicated with a multiple regression, but can still expect attenuation bias with classical errors in variables
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Fall 2008 under Econometrics Prof. Keunkwan Ryu 17 Missing Data – Is it a Problem? If any observation is missing data on one of the variables in the model, it can’t be used If data is missing at random, using a sample restricted to observations with no missing values will be fine A problem can arise if the data is missing systematically – say high income individuals refuse to provide income data
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Fall 2008 under Econometrics Prof. Keunkwan Ryu 18 Nonrandom Samples If the sample is chosen on the basis of an x variable, then estimates are unbiased If the sample is chosen on the basis of the y variable, then we have sample selection bias Sample selection can be more subtle Say looking at wages for workers – since people choose to work this isn’t the same as wage offers
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Fall 2008 under Econometrics Prof. Keunkwan Ryu 19 Outliers Sometimes an individual observation can be very different from the others, and can have a large effect on the outcome Sometimes this outlier will simply be do to errors in data entry – one reason why looking at summary statistics is important Sometimes the observation will just truly be very different from the others
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Fall 2008 under Econometrics Prof. Keunkwan Ryu 20 Outliers (continued) Not unreasonable to fix observations where it’s clear there was just an extra zero entered or left off, etc. Not unreasonable to drop observations that appear to be extreme outliers, although readers may prefer to see estimates with and without the outliers Can use Stata to investigate outliers
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under Econometrics Prof Keunkwan Ryu 16 Measurement Error...

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