Estimating Dynamic Panel Data Models with Measurement Errors with an Application to School Evaluation based on Student Test Scores
نویسندگان
چکیده
A key difficulty in drawing inference of school effect from student test score gains is the fact that test scores are noisy measurements of students’ academic achievements. In this paper we examine competing inferential methods of dynamic panel data models using noisy data. In particular, we consider a score-level model where the score of the current period depends on the score of the previous period. We compare Monte Carlo simulation results of estimators of this model and estimates using the student test score data of Missouri.
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