Multiple comparisons with the best, with economic applications

By W. Horrace, P. Schmidt. 2000

134 citations recorded by the source corpus.

In this paper we discuss a statistical method called multiple comparisons with the best, or MCB. This paper is meant to introduce MCB to economists. We discuss possible uses of MCB in economics. The application that we treat in most detail is the construction of confidence intervals for inefficiency measures from stochastic frontier models with panel data. We also consider an application to the analysis of labour market wage gaps. Copyright © 2000 John Wiley & Sons, Ltd.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/be5f0b660b8d270288e0d5837abe8456358ece6b

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In Economics and Econometrics

In Management Science and Operations Research

Citations in the corpus

Bayesian measurement of productivity and efficiency in the presence of undesirable outputs: crediting electric utilities for reducing air pollution (2005, unknown): “An alternative classical approach to comparing efficiency among a large number of firms would be the multiple comparisons with the best approach of Horrace and Schmidt (2000). While we did not choose to compare a single firm against groups of firms, we could generalize the above approach to compute ”
Nonparametric Kernel Regression with Multiple Predictors and Multiple Shape Constraints (2012, methodology): “We consider a production dataset that has been studied by Horrace and Schmidt (2000) who analyzed technical efficiency for Indonesian rice farms.”
On ranking and selection from independent truncated normal distributions (2005, methodology): “Horrace and Schmidt (2000) calculate confidence intervals using a fixed-effect specification and ‘‘multiple comparisons with the best’’ techniques, based on differences of normal (non-truncated) variates.”
Earnings Functions When Wages and Prices Vary by Location (2009, methodology): “Using test procedures described in Horrace and Schmidt (2000), we conclude that in 1990 the lowest returns to college are in San Francisco or Seattle, and the highest returns are in the subset: Houston, Pittsburgh, Dallas, Phoenix, Atlanta, and Tampa (based on a one-sided test, with a 0.95 critical…”
Forecasting: theory and practice (2020, methodology): “Kelly and Pruitt (2013) and Huang et al. (2015b) use partial least squares (Wold, 1966) to construct target-relevant factors from a cross section of valuation ratios and a variety of sentiment measures, respectively, to improve market return forecasts.”
Using Copulas to Model Time Dependence in Stochastic Frontier Models (2014, background): “In some versions, u∗it is assumed to be time invariant and zit includes a time trend (see, e.g., Battese and Coelli, 1992; Kumbhakar, 1990).”
The Econometric Approach to Efficiency Analysis (2008, background): “[See Jondrow et al. (1982) and Battese and Coelli (1992) for discussion and analysis of the distinction between these two measures.”
A Review and Empirical Comparison of Bayesian and Classical Approaches to Inference on Efficiency Levels in Stochastic Frontier Models with Panel Data (2000, background): “MCB was developed by Hsu (1981, 1984) and Edwards and Hsu (1983). A general exposition can be found in Hochberg and Tamhane (1987), Hsu (1996) and Horrace and Schmidt (1999).”
Economic Efficiency and Frontier Techniques (2004, background): “Finally, Sengupta (2000a) and Huang and Li (2001) have developed more refined stochastic DEA models.”
Fixed and Random Effects in Stochastic Frontier Models (2005, background): “[For interval estimation, see Horrace and Schmidt (2000) and Kim and Schmidt (2000).]”