scholarly journals Estimasi Nonlinear Least Trimmed Squares (NLTS) pada Model Regresi Nonlinier yang Dikenai Outlier

CAUCHY ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 22
Author(s):  
Nur Laili Arofah ◽  
Sri Harini
Keyword(s):  
Production Function ◽  
Nonlinear Regression ◽  
Nonlinear Least Squares ◽  
Least Trimmed Squares ◽  
Constant Elasticity ◽  
Constant Elasticity Of Substitution ◽  
First Order ◽  
Nonlinear Regression Models ◽  
Nonlinear Statistical Model ◽  
Better Than

<p>Constant Elasticity of Substitution (CES) production function is the intrinsic nonlinear regression models that are often used to estimate the data in an industry. Intrinsic nonlinear regression model is a kind<br />of nonlinear regression that can not be linearized, so as to estimate the beta parameters nonlinear statistical model used was Nonlinear Least Squares (NLS) using a first order taylor series approach used in the Gauss<br />Newton iteration. One of the problems often encountered in the analysis of data is an outlier, the presence of outliers in the data analysis greatly influence the results of the analysis so it becomes less valid and the estimation<br />become biased. One method that is resistant to outliers regression is a method of Nonlinear Least Trimmed Squares. This research aims to determine the characteristics of parameter CES production function which<br />contains outlier. The result shows that parameter of the production function CES which contains outliers are bias, inconsistent. So the CES production function which does not contain outliers better than the are contains<br />outliers.</p>

U.S. ELASTICITIES OF SUBSTITUTION AND FACTOR AUGMENTATION AT THE INDUSTRY LEVEL

2012 ◽  
Vol 17 (4) ◽  
pp. 861-897 ◽  
Author(s):  
Andrew T. Young
Keyword(s):  
Manufacturing Sector ◽  
Weighted Average ◽  
Value Added ◽  
The United States ◽  
Large Majority ◽  
Elasticity Of Substitution ◽  
Constant Elasticity ◽  
Constant Elasticity Of Substitution ◽  
First Order ◽  
Industry Level

We provide industry-level estimates of the elasticity of substitution (σ) between capital and labor in the United States. We also estimate rates of factor augmentation. Aggregate estimates are produced. Our empirical model comes from the first-order conditions associated with a constant–elasticity of substitution production function. Our data represent 35 industries at roughly the 2-digit SIC level, 1960–2005. We find that aggregate U.S. σ is likely less than 0.620. σ is likely less than unity for a large majority of individual industries. Evidence also suggests that aggregate σ is less than the value-added share-weighted average of industry σ's. Aggregate technical change appears to be net labor–augmenting. This also appears to be true for the large majority of individual industries, but several industries may be characterized by net capital augmentation. When industry-level elasticity estimates are mapped to model sectors, the manufacturing sector σ is lower than that of services; the investment sector σ is lower than that of consumption.

scholarly journals A Monograph on Nonlinear Regression Models

2018 ◽  
Vol 7 (4.10) ◽  
pp. 543
Author(s):  
B. Mahaboob ◽  
B. Venkateswarlu ◽  
C. Narayana ◽  
J. Ravi sankar ◽  
P. Balasiddamuni
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Nonlinear Regression ◽  
Nonlinear Models ◽  
Nonlinear Least Squares ◽  
Error Variance ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Nonlinear Regression Model ◽  
Nonlinear Regression Models

This research article uses Matrix Calculus techniques to study least squares application of nonlinear regression model, sampling distributions of nonlinear least squares estimators of regression parametric vector and error variance and testing of general nonlinear hypothesis on parameters of nonlinear regression model. Arthipova Irina et.al [1], in this paper, discussed some examples of different nonlinear models and the application of OLS (Ordinary Least Squares). MA Tabati et.al (2), proposed a robust alternative technique to OLS nonlinear regression method which provide accurate parameter estimates when outliers and/or influential observations are present. Xu Zheng et.al [3] presented new parametric tests for heteroscedasticity in nonlinear and nonparametric models.  

scholarly journals Measuring Technological Progress of Smart Grid Based on Production Function Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Dong Han ◽  
Zheng Yan
Keyword(s):  
Smart Grid ◽  
Production Function ◽  
Technological Progress ◽  
Data Envelopment ◽  
Constant Elasticity ◽  
Constant Elasticity Of Substitution ◽  
Estimated Parameters ◽  
Production Function Approach ◽  
Ridge Regression Analysis ◽  
Function Models

Production function theory combined with data envelopment analysis (DEA) and ridge regression analysis (RRA) is applied to evaluate the technological progress of the smart grid. The feasible conditions of production function models are determined by the DEA algorithm. RRA is applied to estimate the relevant parameters of the evaluation models under study. One of the significant steps in the design of the assessment algorithm is the structure of production function models. Therefore, the Cobb-Douglas, constant elasticity of substitution, and translog production functions are employed to evaluate the technological progress of the smart grid, respectively. The results of analysis and calculation mainly include the DEA relative efficiency, slacks in inputs and outputs of inefficient units, estimated parameters, and quantitative indices of technological progress.

scholarly journals Improved Inference for Moving Average Disturbances in Nonlinear Regression Models

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Pierre Nguimkeu
Keyword(s):  
Nonlinear Regression ◽  
Regression Models ◽  
Moving Average ◽  
Asymptotic Properties ◽  
Likelihood Estimation ◽  
Small Sample ◽  
First Order ◽  
Wald Tests ◽  
Nonlinear Regression Models ◽  
Mobile Cellular

This paper proposes an improved likelihood-based method to test for first-order moving average in the disturbances of nonlinear regression models. The proposed method has a third-order distributional accuracy which makes it particularly attractive for inference in small sample sizes models. Compared to the commonly used first-order methods such as likelihood ratio and Wald tests which rely on large samples and asymptotic properties of the maximum likelihood estimation, the proposed method has remarkable accuracy. Monte Carlo simulations are provided to show how the proposed method outperforms the existing ones. Two empirical examples including a power regression model of aggregate consumption and a Gompertz growth model of mobile cellular usage in the US are presented to illustrate the implementation and usefulness of the proposed method in practice.

CONSTANT-ELASTICITY-OF-SUBSTITUTION PRODUCTION FUNCTION

2008 ◽  
Vol 12 (5) ◽  
pp. 694-701 ◽  
Author(s):  
Hideki Nakamura ◽  
Masakatsu Nakamura
Keyword(s):  
Production Function ◽  
Capital Accumulation ◽  
Specific Form ◽  
Production Functions ◽  
Elasticity Of Substitution ◽  
Intermediate Goods ◽  
Constant Elasticity ◽  
Constant Elasticity Of Substitution ◽  
Douglas Production Function

We consider endogenous changes of inputs from labor to capital in the production of intermediate goods, i.e., a form of mechanization. We derive complementary relationships between capital accumulation and mechanization by assuming a Cobb–Douglas production function for the production of final goods from intermediate goods. A constant-elasticity-of-substitution production function in which the elasticity of substitution exceeds unity can be endogenously derived as the envelope of Cobb–Douglas production functions when the efficiency of inputs is assumed in a specific form. The difficulty of mechanization represents the elasticity of substitution.

scholarly journals Intermetropolitan Comparisons Of Production Function

2011 ◽  
Vol 3 (2) ◽  
pp. 112
Author(s):  
Martin Williams ◽  
Tuan Ton-That
Keyword(s):  
Production Function ◽  
Production Technology ◽  
Returns To Scale ◽  
Elasticity Of Substitution ◽  
Constant Elasticity ◽  
Constant Elasticity Of Substitution ◽  
Different Levels ◽  
Variable Returns To Scale ◽  
Inputs And Outputs

A nonhomogeneous production is used to study the features of the production technology across U.S. cities. We compute marginal productivities and scale elasticities for different levels of inputs and outputs. The form of the production function allows variable returns to scale. We can also test the Cobb-Douglas and constant elasticity of substitution forms within the nonhomogeneous specification. Conclusions are drawn concerning returns to scale across cities of different sizes.

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