Specification Testing of Production in a Stochastic Frontier Model

Abstract

Parametric production frontier functions are frequently used in stochastic frontier models, but there do not seem to be any empirical test statistics for the plausibility of this application. In this paper, we develop procedures to test whether or not the parametric production frontier functions are suitable. Toward this aim, we developed two test statistics based on local smoothing and an empirical process, respectively. Residual-based wild bootstrap versions of these two test statistics are also suggested. The distributions of technical inefficiency and the noise term are not specified, which allows specification testing of the production frontier function even under heteroscedasticity. Simulation studies and a real data example are presented to examine the finite sample sizes and powers of the test statistics. The theory developed in this paper is useful for production managers in their decisions on production.

Review on Efficiency and Anomalies in Stock Markets

“…We have also used robust estimation to conduct many applications (see, for example, Wong and Bian (2000); Phang et al (1996); Phang and Wong (1997); Wong et al (2001); Fong and Wong (2006); Qiao et al (2008c); Bian et al (2011); Raza et al (2016); Xu et al (2017); Chan et al (2018); Guo et al (2018b); Tsendsuren et al (2018); Gupta et al (2019a); Pham et al (2020); and many others). Fong and Wong (2007) applied the volatility-volume regressions to the daily realized volatility of common stocks to study sources of volatility predictability.…”

Could Mergers Become More Sustainable? A Study of the Stock Exchange Mergers of NASDAQ and OMX

“…This paper investigates the stock exchange merger of NASDAQ with OMX and examines the sustainability of co-movement between the stock markets of OMX and NASDAQ, that could affect investors' profit and decision making in their investment, changing their trading strategies, and could affect market efficiency and create arbitrage opportunity, anomaly, and additional risk. Thus, extension of our paper could include studying co-movement of other series [55,[58][59][60][61][62][63][64][65][66][67][68][69][70][71], co-movement of using different trading strategies [47,[72][73][74][75], co-movement of making use of different anomalies [76][77][78], co-movement of investing in different markets [60,64,[79][80][81][82], sustainability of making use of different market conditions [66,83], and co-movement in different types of risk [84][85][86][87][88][89][90][91][92][93][94][95]…”

Robust Bayesian Inference in Stochastic Frontier Models

“…In terms of future research, it would be interesting to examine the performance of robust posteriors in more complicated SFM and reconcile differences that arise from different approaches to modeling inefficiency. Moreover, an interesting avenue for future research would be based on recent work by Guo et al (2018) who examined whether a parametric production frontier function is suitable in the analysis. Guo et al (2018) developed two test statistics based on local smoothing and an empirical process, respectively and suggested also residual-based wild bootstrap versions of these two test statistics.…”

“…Moreover, an interesting avenue for future research would be based on recent work by Guo et al (2018) who examined whether a parametric production frontier function is suitable in the analysis. Guo et al (2018) developed two test statistics based on local smoothing and an empirical process, respectively and suggested also residual-based wild bootstrap versions of these two test statistics. As coarsening provides more robust results it is likely that the procedures in Guo et al (2018) would tend to be in favor of the parametric specification although one has to resolve the issue of applying the Guo et al (2018) procedures in a Bayesian context.…”