Operations Research
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Operations Research
2016-04-30
The Use of Panel Quantile Regression for Efficiency Measurement: Insights from Monte Carlo Simulations
http://d.repec.org/n?u=RePEc:cch:wpaper:160005&r=ore
In panel stochastic frontier models, the Fixed Effects (FE) approach produces biased technical efficiency scores when time-invariant variables are important in the production process, and the Random Effects (RE) approach imposes distributional assumptions about the inefficiency. Moreover, technical efficiency scores obtained from these models are biased when the sample contains a large number of firms near the efficient frontier. We propose the use of quantile regression (QR) with a Correlated Random Effects (CRE) specification as an alternative to these approaches. Using Monte Carlo simulations, we show that CRE QR can overcome the limitations of FE and RE stochastic frontier models.
Audrey Laporte
Adrian Rohit Dass
technical efficiency, quantile regression, panel data, stochastic frontier analysis
2016-04
Uncertainty, rationality and complexity in a multi sectoral dynamic model: the Dynamic Stochastic Generalized Aggregation approach
http://d.repec.org/n?u=RePEc:een:camaaa:2016-16&r=ore
The paper proposes an innovative approach for the analytical solution of agent-based models. The approach is termed Dynamic Stochastic Generalized Aggregation (DSG-A) and is tested on a macroeconomic model articulated in a job and in a goods markets with a large number of heterogeneous and interacting agents (namely firms and workers). The agents heuristically adapt their expectations by interpreting the signals from the market and give rise to macroeconomic regularities. The model is analytically solved in two different scenarios. In the first, the emergent proper- ties of the system are determined uniquely by the myopic behavior of the agents while, in the second, a social planner quantifies the optimal number of agents adopting a particular strategy. The integration of the DSG-A approach with intertemporal optimal control allows the identification of multiple equilibria and their qualitative classification.
Michele Catalano
Corrado Di Guilmi
aggregation, uncertainty, opinion dynamics, master equation, optimal control
2016-04
Multivariate Variance Ratio Statistics
http://d.repec.org/n?u=RePEc:cam:camdae:1459&r=ore
We propose several multivariate variance ratio statistics. We derive the asymptotic distribution of the statistics and scalar functions thereof under the null hypothesis that returns are unpredictable after a constant mean adjustment (i.e., under the Efficient Market Hypothesis). We do not impose the no leverage assumption of Lo and MacKinlay (1988) but our asymptotic standard errors are relatively simple and in particular do not require the selection of a bandwidth parameter. We extend the framework to allow for a smoothly varying risk premium in calendar time, and show that the limiting distribution is the same as in the constant mean adjustment case. We show the limiting behaviour of the statistic under a multivariate fads model and under a moderately explosive bubble process: these alternative hypotheses give opposite predictions with regards to the long run value of the statistics. We apply the methodology to three weekly size-sorted CRSP portfolio returns from 1962 to 2013 in three subperiods. We find evidence of a reduction of linear predictability in the most recent period, for small and medium cap stocks. We find similar results for the main UK stock indexes. The main findings are not substantially affected by allowing for a slowly varying risk premium.
Seok Young Hong
Oliver Linton
Hui Jun Zhang
Bubbles; Fads; Martingale; Momentum; Predictability
2014-06-24