scholarly journals Improving " Jackknife Instrumental Variable Estimation method" using A class of immun algorithm with practical application

2019 ◽  
Vol 25 (113) ◽  
pp. 462-474
Author(s):  
صباح منفي رضا ◽  
علاء حسين صبري
Keyword(s):  
Instrumental Variable ◽  
Clonal Selection ◽  
Random Search ◽  
Estimation Method ◽  
Simultaneous Equation ◽  
Percentage Error ◽  
Clonal Selection Algorithm ◽  
Instrumental Variable Estimation ◽  
Linear Simultaneous Equation ◽  
Simultaneous Equation Models

Abstract Most of the robust methods based on the idea of ​​sacrificing one side versus promotion of another, the artificial intelligence mechanisms try to balance weakness and strength to make the best solutions in a random search technique. In this paper, a new idea is introduced to improve the estimators of parameters of linear simultaneous equation models that resulting from the Jackknife Instrumental Variable Estimation method (JIVE) by using a class of immune algorithm which called Clonal Selection Algorithm (CSA) and better estimates are obtained using one of the robust criterion which is called Mean Absolut Percentage Error (MAPE). The success of intelligence algorithm mechanisms has been proven that used to improve the parameters of linear simultaneous equation models according to user criterion and real data of size n=48.

scholarly journals Characterisation of k-Class estimation in simultaneous equation models

1969 ◽  
Vol 74 (3) ◽  
pp. 307-310
Author(s):  
José F. Burguete ◽  
Esteban Burguete
Keyword(s):  
Estimation Method ◽  
Simultaneous Equation ◽  
Equation Model ◽  
Geometric Characterization ◽  
Simultaneous Equation Model ◽  
Oblique Projector ◽  
Linear Simultaneous Equation ◽  
Whole Space ◽  
Simultaneous Equation Models

This paper presents the direct geometric characterization of the k-Class estimation method in a linear simultaneous equation model. The concept of oblique projector is used. The paper also exhibits several estimators generated by projecting along subspaces of the whole space along which this class is projected.

LOCAL INSTRUMENTAL VARIABLE METHOD FOR THE GENERALIZED ADDITIVE-INTERACTIVE NONLINEAR VOLATILITY MODEL ESTIMATION

2012 ◽  
Vol 28 (3) ◽  
pp. 629-669
Author(s):  
Michael Levine ◽  
Jinguang Li
Keyword(s):  
Instrumental Variable ◽  
Estimation Method ◽  
Estimation Methods ◽  
Financial Assets ◽  
Arch Model ◽  
Instrumental Variable Estimation ◽  
Interaction Terms ◽  
Variance Functions ◽  
Volatility Model ◽  
Instrumental Variable Method

In this article we consider a new separable nonparametric volatility model that includes second-order interaction terms in both mean and conditional variance functions. This is a very flexible nonparametric ARCH model that can potentially explain the behavior of the wide variety of financial assets. The model is estimated using the generalized version of the local instrumental variable estimation method first introduced in Kim and Linton (2004, Econometric Theory 20, 1094–1139). This method is computationally more effective than most other nonparametric estimation methods that can potentially be used to estimate components of such a model. Asymptotic behavior of the resulting estimators is investigated and their asymptotic normality is established. Explicit expressions for asymptotic means and variances of these estimators are also obtained.

Econometrics Illustrated, with Applications from Insurance-Research Awards

2017 ◽  
Vol 11 (2) ◽  
Author(s):  
Richard J Butler
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Instrumental Variable ◽  
Gini Index ◽  
Simultaneous Equation ◽  
Instrumental Variable Estimation ◽  
Simple Index ◽  
Variable Bias ◽  
Asian Pacific

AbstractThe linear regression model is the dominant tool employed in applied risk and insurance research. Based on my 2016 APRIA lecture at Chengdu, China, I illustrate the simple geometry of the linear regression model, as well as some standard results from it: omitted variable bias (OVB), classical measurement error (CME), simultaneous equation models (SEM), and instrumental variable estimation. Instrumental variable estimation solves OVB, CME, and SEM problems by constructing similar triangles to retrieve consistent estimates. I apply these tools by estimating the determinants of the Witt and Mehr awards given annually for Journal of Risk and Insurance articles, as two examples. The Witt vs. Mehr awards also contrasts short-term scholarly recognition (Witt) versus long-term scholarly recognition (Mehr). The comments made here apply to other paper awards, such as those presented by the Asian Pacific Journal of Risk and Insurance. I also present a simple index function based on the classical Gini index (hence, this new index is denoted as the regression gini index, RGI) useful for comparing two regression models, and apply this to explain the empirical difference between the determinants of the Witt and Mehr awards.

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