scholarly journals Some Recent Developments in Efficiency Measurement in Stochastic Frontier Models

2011 ◽  
Vol 2011 ◽  
pp. 1-25 ◽  
Author(s):  
Subal C. Kumbhakar ◽  
Efthymios G. Tsionas
Keyword(s):  
Latent Class ◽  
Stochastic Frontier ◽  
Local Maximum ◽  
Latent Class Models ◽  
Economic Behavior ◽  
Efficiency Measurement ◽  
Likelihood Method ◽  
Stochastic Frontier Models ◽  
Recent Developments ◽  
Local Maximum Likelihood

This paper addresses some of the recent developments in efficiency measurement using stochastic frontier (SF) models in some selected areas. The following three issues are discussed in details. First, estimation of SF models with input-oriented technical efficiency. Second, estimation of latent class models to address technological heterogeneity as well as heterogeneity in economic behavior. Finally, estimation of SF models using local maximum likelihood method. Estimation of some of these models in the past was considered to be too difficult. We focus on the advances that have been made in recent years to estimate some of these so-called difficult models. We complement these with some developments in other areas as well.

A Simultaneous Approach to Market Segmentation and Market Structuring

1987 ◽  
Vol 24 (2) ◽  
pp. 139-153 ◽  
Author(s):  
Rajiv Grover ◽  
V. Srinivasan
Keyword(s):  
Market Segmentation ◽  
Latent Class ◽  
Brand Choice ◽  
Latent Class Models ◽  
Brand Names ◽  
Likelihood Method ◽  
The Matrix ◽  
Cross Classification ◽  
Choice Probabilities

The authors define a market segment to be a group of consumers homogeneous in terms of the probabilities of choosing the different brands in a product class. Because the vector of choice probabilities is homogeneous within segments and heterogeneous across segments, each segment is characterized by its corresponding group of brands with “large” choice probabilities. The competitive market structure is determined as the possibly overlapping groups of brands corresponding to the different segments. The use of brand choice probabilities as the basis for segmentation leads to market structuring and market segmentation becoming reverse sides of the same analysis. Using panel data, the authors obtain the matrix of cross-classification of brands chosen on two purchase occasions and extract segments by using the maximum likelihood method for estimating latent class models. An application to the instant coffee market indicates that the proposed approach has substantial validity and suggests the presence of submarkets related to product attributes as well as to brand names.

scholarly journals Estimação Local de Valor em Risco Baseado em Cópulas

2009 ◽  
Vol 7 (1) ◽  
pp. 29
Author(s):  
Eduardo F. L. De Melo ◽  
Beatriz Vaz de Melo Mendes
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Estimation ◽  
Asymptotic Variance ◽  
Local Maximum ◽  
Real Data ◽  
Likelihood Method ◽  
Out Of Sample ◽  
Local Maximum Likelihood ◽  
Individual Asset

In this paper we propose the local maximum likelihood method for dynamically estimate copula parameters. We study the estimates statistical properties and derive the expression for their asymptotic variance in the case of Gaussian copulas. The local estimates are able to detect temporal changes in the strength of dependence among assets. These dynamics are combined with a GARCH type modeling of each individual asset to estimate the Value- at-Risk. The performance of the proposed estimates is investigated through Monte Carlo simulation experiments. In an application using real data, an out-of-sample test indicated that the new methodology may outperform the constant copula model when it comes to Value-at-Risk estimation.

scholarly journals Examining the Inefficiency of Transit Systems Using Latent Class Stochastic Frontier Models

2012 ◽  
Vol 50 (2) ◽  
Author(s):  
Ryoichi Sakano ◽  
Kofi Obeng
Keyword(s):  
Latent Class ◽  
Latent Class Model ◽  
Stochastic Frontier ◽  
Random Errors ◽  
Class Model ◽  
Single Class ◽  
Transit Systems ◽  
Cost Frontier ◽  
Stochastic Frontier Models ◽  
Cost Inefficiency

This paper estimates a single class stochastic cost frontier model that accounts for heterogeneity by including background variables and a latent class model of the same specification. It is found that both the two-sided random errors and one-sided errors (cost inefficiency) are substantially smaller in the latter model than it is in the former, suggesting possible bias in the estimates of inefficiency from the single class model. Further, 58.9%–68.39% of the calculated inefficiencies are due to differences in technology captured by the latent classes. The paper concludes that using background variables only to capture heterogeneity may exaggerate measured inefficiencies in transit systems and suggests the latent class approach as a solution.

Developmental Measurement of Mental Attention

1994 ◽  
Vol 17 (1) ◽  
pp. 161-200 ◽  
Author(s):  
Juan Pascual-Leone ◽  
Raymond Baillargeon
Keyword(s):  
Latent Class ◽  
Latent Class Models ◽  
Categorical Data Analysis ◽  
Test Items ◽  
Mental Demand ◽  
Mental Attention ◽  
Constructivist Model ◽  
Discrete Manner ◽  
Analysis System ◽  
Data Analysis System

A dialectical constructivist model of mental attention ("effort") and of working memory is briefly presented, and used to explicate subjects' processing in misleading test items. We illustrate with task analyses of the Figural Intersections Test (FIT). We semantically derive a set of 10 Theoretical Structural Predictions (TSP) that stipulate relations between mental attentional resources (mental-power: Mp) and the systematically varied mental demand of items (mental-demand: Md), as they jointly codetermine probable performance (conditional probabilities of passing and failing). These predictions are evaluated on first approximation using a known family of ordered Latent Class models, all probabilistic versions of Guttman's unidimensional scale. Parameters of these models were estimated using the Categorical Data Analysis System of Eliason (1990). Main results are: (1) Data fit Lazarsfeld's latent-distance model, providing initial support for our 10 predictions; (2) The M-power of children (latent Mp-classes) when assessed behaviourally may increase with age in a discrete manner, and have the potential to generate interval scales of measurement; (3) In the light of our results what statisticians often consider "error of measurement" appears (in part) to be signal, not noise: The organismic signal of misleading (Y-) processes that in their dialectical (trade-off) interaction with success-producing (X-) processes generate performance.

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