Abstract
In this paper, nonparametric estimators of a multivariate density, its conditional mean (regression function) and its conditional variances (heteroskedasticity) are presented. Among other results, we establish central limit theorems for the estimators and build up confidence intervals based on these estimators. These techniques are applied to obtain new results in two areas of econometrics: Monte Carlo investigations of the exact distributions of test statistics, and the treatment of heteroskedasticity in linear regression.
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© 1987 D. Reidel Publishing Company
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Singh, R.S., Ullah, A., Carter, R.A.L. (1987). Nonparametric Inference In Econometrics: New Applications. In: MacNeill, I.B., Umphrey, G.J., Carter, R.A.L., McLeod, A.I., Ullah, A. (eds) Time Series and Econometric Modelling. The University of Western Ontario Series in Philosophy of Science, vol 36. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-4790-0_18
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DOI: https://doi.org/10.1007/978-94-009-4790-0_18
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