Non-standard Confidence Sets for Ratios and Tipping Points with Applications to Dynamic Panel Data

Jean-Thomas Bernard (), Ba Chu, Lynda Khalaf and Marcel Voia

No 17-05, Carleton Economic Papers from Carleton University, Department of Economics

Abstract: We study estimation uncertainty when the object of interest contains one or more ratios of parameters. The ratio of parameters is a discontinuous parameter transformation; it has been shown that traditional confidence intervals often fail to cover this true ratio with very high probability. Constructing confidence sets for ratios using Fieller’s method is a viable solution as the method can avoid the discontinuity problem. This paper proposes an extension of the multivariate Fieller method beyond standard estimators, focusing on asymptotically mixed normal estimators that commonly arise in dynamic panel polynomial regression with persistent covariates. We discuss the cases where the underlying estimators converge to various distri- butions, depending on the persistence level of the covariates. We show that the asymptotic distribution of the pivotal statistic used for constructing a Fieller’s confidence set remains a standard Chi-squared distribution regardless of rates of convergence, thus the rates are being ‘self-normalized’ and can be unknown. A simulation study illustrates the finite sample properties of the proposed method in a dynamic polynomial panel. Our method is demonstrated to work well in small samples, even when the persistence coefficient is unity.

Pages: 31 pages
Date: 2017-01-18
New Economics Papers: this item is included in nep-ecm
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Published: Carleton Economic Papers

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Related works:
Journal Article: Non-Standard Confidence Sets for Ratios and Tipping Points with Applications to Dynamic Panel Data (2019)
Working Paper: Non-Standard Confidence Sets for Ratios and Tipping Points with Applications to Dynamic Panel Data (2019)
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