Theory and practice of inference in regression discontinuity: a fixed-bandwidth asymptotics approach
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The Department of Economic Science was founded in 1898 to teach economic theory as a truth of industrial life, and was very much concerned with applying economics to business and industry, particularly agriculture. Between 1910 and 1967 it showed the growing influence of other social studies, such as sociology, history, and political science. Today it encompasses the majors of Agricultural Business (preparing for agricultural finance and management), Business Economics, and Economics (for advanced studies in business or economics or for careers in financing, management, insurance, etc).
History
The Department of Economic Science was founded in 1898 under the Division of Industrial Science (later College of Liberal Arts and Sciences); it became co-directed by the Division of Agriculture in 1919. In 1910 it became the Department of Economics and Political Science. In 1913 it became the Department of Applied Economics and Social Science; in 1924 it became the Department of Economics, History, and Sociology; in 1931 it became the Department of Economics and Sociology. In 1967 it became the Department of Economics, and in 2007 it became co-directed by the Colleges of Agriculture and Life Sciences, Liberal Arts and Sciences, and Business.
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1898–present
Historical Names
- Department of Economic Science (1898–1910)
- Department of Economics and Political Science (1910-1913)
- Department of Applied Economics and Social Science (1913–1924)
- Department of Economics, History and Sociology (1924–1931)
- Department of Economics and Sociology (1931–1967)
Related Units
- College of Agricultural and Life Sciences (parent college)
- College of Liberal Arts and Sciences (parent college)
- College of Business (parent college)
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Abstract
In regression discontinuity design (RD), researchers use bandwidths around the discontinuity. For agiven bandwidth, one can estimate asymptotic variance based on the assumption that the bandwidthshrinks to zero as sample size increases (the traditional approach) or, alternatively, that the bandwidthis …xed. The main theoretical results for RD rely on the former, while most applications in the literaturetreat the estimates as parametric. This paper develops the “…xed-bandwidth” alternative asymptotictheory for RD designs, bridging the gap between theorists and practitioners while shedding light onimplicit assumptions in both approaches. The …xed-bandwidth approach provides alternative formulas(approximations) for the bias and variance of RD estimators. Simulations indicate that …xed-bandwidthapproximations are usually better than traditional approximations, and improvements are nontrivialwhen there is heteroskedasticity. When there is no heteroskedasticity, both approximations are shown tobe equivalent, under some additional mild conditions. Feasible estimators of …xed-bandwidth standarderrors are easy to implement and improve coverage of con…dence intervals compared to the traditionalapproach, especially in the presence of heteroskedasticity. Fixed-bandwidth approximations are akin totreating RD estimators as locally parametric, providing theoretical justi…cation for the common empiricalpractice of using heteroskedasticity-robust standard errors in RD settings.