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A Barro-type economic growth model is estimated for the 50 states in the U.S. using data for three decades beginning in 1960. Frontier estimation techniques are used to test for the presence of state-specific shocks to economic growth that are independent of the usual, normally-distributed random errors. We find that large, positive shocks to growth occurred during the period 1960-90. Our results indicate that the error term structure assumed under OLS may not be appropriate for modeling economic growth.

We consider the high order moments estimator of the frontier of a random pair, introduced by [S. Girard, A. Guillou and G. Stupfler, J. Multivariate Anal. 116 (2013) 172–189]. In the present paper, we show that this estimator is strongly uniformly consistent on compact sets and its rate of convergence is given when the conditional cumulative distribution function belongs to the Hall class of distribution functions.

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Modeling Economic Growth with Unpredictable Shocks: A State-Level Application for 1960-90

Uniform strong consistency of a frontier estimatorusing kernel regression on high order moments

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Modeling Economic Growth with Unpredictable Shocks: A State-Level Application for 1960-90

Uniform strong consistency of a frontier estimatorusing kernel regression on high order moments