scholarly journals Specification Testing of Production in a Stochastic Frontier Model

2018 ◽  
Vol 10 (9) ◽  
pp. 3082 ◽  
Author(s):  
Xu Guo ◽  
Gao-Rong Li ◽  
Michael McAleer ◽  
Wing-Keung Wong
Keyword(s):  
Empirical Test ◽  
Stochastic Frontier ◽  
Real Data ◽  
Simulation Studies ◽  
Test Statistics ◽  
Local Smoothing ◽  
Finite Sample ◽  
Production Frontier ◽  
Specification Testing ◽  
Stochastic Frontier Models

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.

scholarly journals Goodness-of-Fit Tests for Bivariate Time Series of Counts

Econometrics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 10
Author(s):  
Šárka Hudecová ◽  
Marie Hušková ◽  
Simos G. Meintanis
Keyword(s):  
Goodness Of Fit ◽  
Probability Generating Function ◽  
Parametric Bootstrap ◽  
Real Data ◽  
Data Sets ◽  
Test Statistics ◽  
Finite Sample ◽  
Generalized Poisson ◽  
Goodness Of Fit Tests ◽  
Monte Carlo Experiments

This article considers goodness-of-fit tests for bivariate INAR and bivariate Poisson autoregression models. The test statistics are based on an L2-type distance between two estimators of the probability generating function of the observations: one being entirely nonparametric and the second one being semiparametric computed under the corresponding null hypothesis. The asymptotic distribution of the proposed tests statistics both under the null hypotheses as well as under alternatives is derived and consistency is proved. The case of testing bivariate generalized Poisson autoregression and extension of the methods to dimension higher than two are also discussed. The finite-sample performance of a parametric bootstrap version of the tests is illustrated via a series of Monte Carlo experiments. The article concludes with applications on real data sets and discussion.

Topp–Leone Linear Exponential Distribution

2018 ◽  
Vol 33 (1) ◽  
pp. 31-43
Author(s):  
Bol A. M. Atem ◽  
Suleman Nasiru ◽  
Kwara Nantomah
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Exponential Distribution ◽  
Likelihood Estimation ◽  
Real Data ◽  
Simulation Studies ◽  
Finite Sample ◽  
Data Set ◽  
Finite Sample Properties ◽  
Linear Exponential Distribution

Abstract This article studies the properties of the Topp–Leone linear exponential distribution. The parameters of the new model are estimated using maximum likelihood estimation, and simulation studies are performed to examine the finite sample properties of the parameters. An application of the model is demonstrated using a real data set. Finally, a bivariate extension of the model is proposed.

scholarly journals New Equivalence Tests for Hardy–Weinberg Equilibrium and Multiple Alleles

Stats ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 34-39
Author(s):  
Vladimir Ostrovski
Keyword(s):  
Bootstrap Method ◽  
Real Data ◽  
Data Sets ◽  
Test Statistics ◽  
Finite Sample ◽  
Multiple Alleles ◽  
Weinberg Equilibrium ◽  
Equivalence Tests ◽  
Hardy Weinberg Equilibrium ◽  
The Bootstrap Method

We consider testing equivalence to Hardy–Weinberg Equilibrium in case of multiple alleles. Two different test statistics are proposed for this test problem. The asymptotic distribution of the test statistics is derived. The corresponding tests can be carried out using asymptotic approximation. Alternatively, the variance of the test statistics can be estimated by the bootstrap method. The proposed tests are applied to three real data sets. The finite sample performance of the tests is studied by simulations, which are inspired by the real data sets.

scholarly journals A comparison on two discordancy tests to detect outlier in von mises (VM) sample

2020 ◽  
Vol 19 (1) ◽  
pp. 156
Author(s):  
Fatin Najihah Badarisam ◽  
Adzhar Rambli ◽  
Mohammad Illyas Sidik
Keyword(s):  
Real Data ◽  
Circular Data ◽  
Performance Tests ◽  
Simulation Studies ◽  
Test Statistics ◽  
Data Set ◽  
Concentration Parameter ◽  
Von Mises ◽  
Better Than ◽  
Author Knowledge

<span>This paper focuses on comparing two discordancy tests between robust and non-robust statistic to detect a single outlier in univariate circular data. So far, to the best author knowledge that there is no literature make a comparison between both tests of <em>RCDu Statistic</em> and </span><em><span>𝐺</span><sub><span>1</span></sub><span> Statistic</span></em><span>. The test statistics are based on the circular median and spacing theory. In addition, those statistics can detect multiple and patches outliers. The performance tests of <em>RCDu Statistic</em> and </span><em><span>𝐺</span><sub><span>1</span></sub><span> Statistic</span></em><span> are tested in outlier proportion of correct detection, masking and swamping effect. At the beginning stage, we obtained the cut-off points for the <em>RCDu Statistic</em> and </span><em><span>𝐺</span><sub><span>1</span></sub><span> Statistic</span></em><span> by applying Monte Carlo simulation studies. Then, generated sample from von Mises (VM) with the combination of sample size and concentration parameter. The estimating process of cut-off points for both statistics is repeated 3000 times at 10%, 5% and 1% upper percentiles. As a result, the <em>RCDu Statistic</em> perform well in detecting a correct single outlier. Moreover, the <em>RCDu Statistic</em> has a lower masking rate compared to </span><em><span>𝐺</span><sub><span>1</span></sub><span> Statistic</span></em><span>.  However, the </span><em><span>𝐺</span><sub><span>1</span></sub><span> Statistic</span></em><span> is better than <em>RCDu Statistic</em> for swamping effect due to a lower swamping rate. Thus, <em>RCDu Statistic</em> performs better than </span><em><span>𝐺</span><sub><span>1</span></sub><span> Statistic</span></em><span> in detecting a single outlier for von Mises (VM) sample. As an illustration, both statistics were applied to the real data set from a conducted experiments series to investigate the northen cricket frogs homing ability.</span>

Nonparametric estimation and inference for polytomous discrimination index

2017 ◽  
Vol 27 (10) ◽  
pp. 3092-3103 ◽  
Author(s):  
Jialiang Li ◽  
Qunqiang Feng ◽  
Jason P Fine ◽  
Michael J Pencina ◽  
Ben Van Calster
Keyword(s):  
Diagnostic Accuracy ◽  
Nonparametric Estimation ◽  
Asymptotic Properties ◽  
Real Data ◽  
Discrimination Index ◽  
Simulation Studies ◽  
Finite Sample ◽  
Nonparametric Estimators ◽  
Nonparametric Approach ◽  
Accuracy Measure

Polytomous discrimination index is a novel and important diagnostic accuracy measure for multi-category classification. After reconstructing its probabilistic definition, we propose a nonparametric approach to the estimation of polytomous discrimination index based on an empirical sample of biomarker values. In this paper, we provide the finite-sample and asymptotic properties of the proposed estimators and such analytic results may facilitate the statistical inference. Simulation studies are performed to examine the performance of the nonparametric estimators. Two real data examples are analysed to illustrate our methodology.

scholarly journals SPECIFICATION TESTING IN NONPARAMETRIC INSTRUMENTAL QUANTILE REGRESSION

2020 ◽  
Vol 36 (4) ◽  
pp. 583-625 ◽  
Author(s):  
Christoph Breunig
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Monte Carlo Study ◽  
Test Statistics ◽  
Finite Sample ◽  
Test Statistic ◽  
Specification Testing ◽  
Quantile Regression Model ◽  
Endogenous Regressors ◽  
Finite Sample Properties

There are many environments in econometrics which require nonseparable modeling of a structural disturbance. In a nonseparable model with endogenous regressors, key conditions are validity of instrumental variables and monotonicity of the model in a scalar unobservable variable. Under these conditions the nonseparable model is equivalent to an instrumental quantile regression model. A failure of the key conditions, however, makes instrumental quantile regression potentially inconsistent. This article develops a methodology for testing the hypothesis whether the instrumental quantile regression model is correctly specified. Our test statistic is asymptotically normally distributed under correct specification and consistent against any alternative model. In addition, test statistics to justify the model simplification are established. Finite sample properties are examined in a Monte Carlo study and an empirical illustration is provided.

scholarly journals Transport infrastructure and technical efficiency in a panel of countries: Accounting for endogeneity in a stochastic frontier model

2021 ◽  
Author(s):  
Ligia Alba Melo-Becerra ◽  
María Teresa Ramírez-Giraldo
Keyword(s):  
Economic Performance ◽  
Stochastic Frontier ◽  
Transport Infrastructure ◽  
Production Frontier ◽  
Middle Income ◽  
Middle Income Countries ◽  
Economic Output ◽  
Efficiency Measures ◽  
Stochastic Frontier Models

In this paper, a global production frontier is estimated using stochastic frontier models to assess the contribution of transport infrastructure to countries’ performance. We find that the role of infrastructure is underestimated under the exogeneity assumption indicating that handling endogeneity is crucial in the estimation. Results suggest that a better endowment of infrastructure contributes to economic growth, highlighting its importance in explaining differences in the economic performance of countries. Efficiency measures indicate that high-income countries are more efficient than low- and middle-income countries, suggesting that there is room for improving economic performance in countries with a lower income level. Better institutions also are essential to foster countries’ economic output.

scholarly journals Integral Least-Squares Inferences for Semiparametric Models with Functional Data

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Limian Zhao ◽  
Peixin Zhao
Keyword(s):  
Data Analysis ◽  
Asymptotic Normality ◽  
Least Squares ◽  
Confidence Intervals ◽  
Functional Data ◽  
Estimation Method ◽  
Real Data ◽  
Semiparametric Models ◽  
Simulation Studies ◽  
Finite Sample

The inferences for semiparametric models with functional data are investigated. We propose an integral least-squares technique for estimating the parametric components, and the asymptotic normality of the resulting integral least-squares estimator is studied. For the nonparametric components, a local integral least-squares estimation method is proposed, and the asymptotic normality of the resulting estimator is also established. Based on these results, the confidence intervals for the parametric component and the nonparametric component are constructed. At last, some simulation studies and a real data analysis are undertaken to assess the finite sample performance of the proposed estimation method.

scholarly journals Two-sample Tests for Functional Data Using Characteristic Functions

2021 ◽  
Vol 50 (4) ◽  
pp. 53-64
Author(s):  
Mirosław Krzyśko ◽  
Łukasz Smaga
Keyword(s):  
Functional Data ◽  
Real Data ◽  
Characteristic Functions ◽  
Weight Functions ◽  
Test Statistics ◽  
Finite Sample ◽  
Testing Procedures ◽  
Two Sample Problem ◽  
Finite Sample Properties ◽  
Multivariate Functional Data

In this paper, we consider the two-sample problem for univariate and multivariate functional data. To solve this problem, we use tool of characteristic function and a basis function representation of functional data. We construct test statistics for conformity of distributions based on a weighted distance between characteristic functions of random vectors obtained in basis representation. Different weight functions result in different test statistics, whose distributions are approximated by permutation method. Testing procedures are implemented in the R program and the code is available. Simulation study shows good finite sample properties of proposed methods, while real data example illustrates the application of them.

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