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Book part
Publication date: 25 July 1997

Ehsan S. Soofi

Abstract

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Applying Maximum Entropy to Econometric Problems
Type: Book
ISBN: 978-0-76230-187-4

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Article
Publication date: 9 November 2012

R. Farnoosh, P. Nabati and A. Hajirajabi

The main purpose of this paper is to estimate the resistance and inductor in the RL electrical circuit when these are unavailable or missing data that it is a concern in…

Abstract

Purpose

The main purpose of this paper is to estimate the resistance and inductor in the RL electrical circuit when these are unavailable or missing data that it is a concern in electrical engineering. The input voltage is assumed to be corrupted by the noise and the current is observed at discrete time points.

Design/methodology/approach

The authors propose a computationally efficient framework for parameters estimation using least square estimator and Bayesian Monte Carlo scheme.

Findings

The explicit formulas for least square estimator are derived and the strong consistency of resistance estimator is verified when inductor is a known parameter, then Bayesian estimation of parameters governed by using Markov chain Monte Carlo methods. The applicability of the results is demonstrated by using numerical examples. Several numerical results and figures are presented via Matlab and R programming to illustrate the performance of the estimators.

Practical implications

The paper can be used in various types of electrical engineering real time projects. The projects include electrical circuits, electrical machines theory and drives, especially when the parameters are uncertain that it is a worry in electrical engineering.

Originality/value

To the author's best knowledge, least square and Bayesian estimation of resistance and inductor have not been studied before. The proposed model is nonlinear with respect to inductor (L); therefore the present work has fundamental difference in comparison with the similar models.

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COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 31 no. 6
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 1 January 1986

ROGER N. CONWAY and RON C. MITTELHAMMER

In the last two decades there has been considerable progress made in the development of alternative estimation techniques to ordinary least squares (OLS) regression. The…

Abstract

In the last two decades there has been considerable progress made in the development of alternative estimation techniques to ordinary least squares (OLS) regression. The search for alternative estimators has no doubt been motivated by the observance of erratic OLS estimator behavior in cases where there are too few observations, multicollinearity problems, or simply “information‐poor” data sets. Imprecise and unreliable OLS coefficient estimates have been the result.

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Studies in Economics and Finance, vol. 10 no. 1
Type: Research Article
ISSN: 1086-7376

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Article
Publication date: 20 May 2020

Houzhe Zhang, Defeng Gu, Xiaojun Duan, Kai Shao and Chunbo Wei

The purpose of this paper is to focus on the performance of three typical nonlinear least-squares estimation algorithms in atmospheric density model calibration.

Abstract

Purpose

The purpose of this paper is to focus on the performance of three typical nonlinear least-squares estimation algorithms in atmospheric density model calibration.

Design/methodology/approach

The error of Jacchia-Roberts atmospheric density model is expressed as an objective function about temperature parameters. The estimation of parameter corrections is a typical nonlinear least-squares problem. Three algorithms for nonlinear least-squares problems, Gauss–Newton (G-N), damped Gauss–Newton (damped G-N) and Levenberg–Marquardt (L-M) algorithms, are adopted to estimate temperature parameter corrections of Jacchia-Roberts for model calibration.

Findings

The results show that G-N algorithm is not convergent at some sampling points. The main reason is the nonlinear relationship between Jacchia-Roberts and its temperature parameters. Damped G-N and L-M algorithms are both convergent at all sampling points. G-N, damped G-N and L-M algorithms reduce the root mean square error of Jacchia-Roberts from 20.4% to 9.3%, 9.4% and 9.4%, respectively. The average iterations of G-N, damped G-N and L-M algorithms are 3.0, 2.8 and 2.9, respectively.

Practical implications

This study is expected to provide a guidance for the selection of nonlinear least-squares estimation methods in atmospheric density model calibration.

Originality/value

The study analyses the performance of three typical nonlinear least-squares estimation methods in the calibration of atmospheric density model. The non-convergent phenomenon of G-N algorithm is discovered and explained. Damped G-N and L-M algorithms are more suitable for the nonlinear least-squares problems in model calibration than G-N algorithm and the first two algorithms have slightly fewer iterations.

Details

Aircraft Engineering and Aerospace Technology, vol. 92 no. 7
Type: Research Article
ISSN: 1748-8842

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Article
Publication date: 20 September 2021

Dang Luo and Decai Sun

With the prosperity of grey extension models, the form and structure of grey forecasting models tend to be complicated. How to select the appropriate model structure…

Abstract

Purpose

With the prosperity of grey extension models, the form and structure of grey forecasting models tend to be complicated. How to select the appropriate model structure according to the data characteristics has become an important topic. The purpose of this paper is to design a structure selection method for the grey multivariate model.

Design/methodology/approach

The linear correction term is introduced into the grey model, then the nonhomogeneous grey multivariable model with convolution integral [NGMC(1,N)] is proposed. Then, by incorporating the least absolute shrinkage and selection operator (LASSO), the model parameters are compressed and estimated based on the least angle regression (LARS) algorithm.

Findings

By adjusting the values of the parameters, the NGMC(1,N) model can derive various structures of grey models, which shows the structural adaptability of the NGMC(1,N) model. Based on the geometric interpretation of the LASSO method, the structure selection of the grey model can be transformed into sparse parameter estimation, and the structure selection can be realized by LASSO estimation.

Practical implications

This paper not only provides an effective method to identify the key factors of the agricultural drought vulnerability, but also presents a practical model to predict the agricultural drought vulnerability.

Originality/value

Based on the LASSO method, a structure selection algorithm for the NGMC(1,N) model is designed, and the structure selection method is applied to the vulnerability prediction of agricultural drought in Puyang City, Henan Province.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 18 December 2019

Kashif Munir and Mahnoor Bukhari

The purpose of this paper is to examine the impact of three modes of globalization, i.e. trade globalization, financial globalization and technological globalization…

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Abstract

Purpose

The purpose of this paper is to examine the impact of three modes of globalization, i.e. trade globalization, financial globalization and technological globalization, separately on income inequality on the Asian emerging economies.

Design/methodology/approach

The study uses Hecksher–Ohlin and the Stolper–Samuelson theorem as a theoretical model for the relationship between globalization and income inequality. The study uses pooled least square (POLS) and instrumental variable least square (IVLS) estimation technique but prefers the IVLS over POLS due to the problems of omitted variable biased and endogeneity. Due to unavailability of data for all the Asian emerging economies, the study uses the following 11 countries, i.e. Bangladesh, China, India, Indonesia, Malaysia, Pakistan, Philippines, Sri Lanka, Singapore, South Korea and Thailand, from 1980 to 2014 for the trade and technological globalization model and from 1990 to 2014 for the financial globalization model.

Findings

Trade globalization significantly contributes to reduce income inequality in the Asian emerging economies. The impact of financial globalization on income inequality suggests that financial integration causes an increase in income inequality. Therefore, the benefits of financial globalization are not evenly distributed among the rich and the poor. The impact of technological globalization significantly contributes in the reduction of income inequality.

Practical implications

Government has to invest in research and development activities, establish efficient financial system, reduce trade restrictions and provide subsidies that help to increase the volume of trade.

Originality/value

This study contributes in the existing literature by analyzing the impact of trade globalization, financial globalization and technological globalization on income inequality in Asian emerging economies. The study provides useful guidelines to policy makers and governments to make effective policies in relation to globalization and income inequality that lead toward economic growth and reducing income inequality.

Details

International Journal of Sociology and Social Policy, vol. 40 no. 1/2
Type: Research Article
ISSN: 0144-333X

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Article
Publication date: 8 April 2014

Dennis Olson and Taisier A. Zoubi

This study aims to examine the determinants of the allowance for loan losses (ALL) and loan loss provisions (LLP) for banks in the Middle East and North African (MENA…

Abstract

Purpose

This study aims to examine the determinants of the allowance for loan losses (ALL) and loan loss provisions (LLP) for banks in the Middle East and North African (MENA) region using both a two-stage approach and simultaneous equation system to address the potential problem of estimation bias introduced by estimating the ALL and LLP separately. The paper also tests three competing hypotheses: the earnings management hypothesis, the capital management hypothesis, and the signaling hypothesis.

Design/methodology/approach

The authors adopt a simultaneous equation and three-stage approaches to test whether MENA banks jointly determine LLP and ALL and the determinants of the two accounts. The sample consists of all available electronic data for 75 banks (451 bank-year observations) in nine MENA countries over the period 2000-2008.

Findings

Evidence suggests that the two accounts are jointly determined. The results support the earnings management hypothesis – meaning that MENA banks have engaged in year-to-year income smoothing. The authors also find that LLP and ALL provide signals about future earnings.

Research limitations/implications

The authors acknowledge that the LLP account is only one of many accounts on the income statement that could be used for signaling or to manage earnings, and that the ALL is one of several accounts that could be used for signaling, earnings or capital management. Future studies could examine other accruals for their role in managing earnings, signaling and capital.

Practical implications

The results indicate that bank managers use LLP and ALL accounts to manage earnings management, policy makers may want to limit the ability of banks to manipulate earnings.

Originality/value

Prior research on the loan loss accounting practices has been based on single equation models of the determinants of LLP and ALL. An issue that has not been adequately addressed in this literature is that ALL and LLP may be interrelated and jointly determined by banks. If the two accounts are not independent of each other, failure to include one when estimating the other may lead to an omitted variable problem, while including both in the same equation induces a potential simultaneity bias. The study is the first empirical work examining whether ALL and LLP are jointly determined by banks. By jointly estimating LLP and ALL, the study permits an assessment of the magnitude of the potential error from adopting ordinary least squares estimation of a single equation model.

Details

Journal of Islamic Accounting and Business Research, vol. 5 no. 1
Type: Research Article
ISSN: 1759-0817

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Article
Publication date: 21 July 2020

Guanghui Liu, Qiang Li, Lijin Fang, Bing Han and Hualiang Zhang

The purpose of this paper is to propose a new joint friction model, which can accurately model the real friction, especially in cases with sudden changes in the motion…

Abstract

Purpose

The purpose of this paper is to propose a new joint friction model, which can accurately model the real friction, especially in cases with sudden changes in the motion direction. The identification and sensor-less control algorithm are investigated to verify the validity of this model.

Design/methodology/approach

The proposed friction model is nonlinear and it considers the angular displacement and angular velocity of the joint as a secondary compensation for identification. In the present study, the authors design a pipeline – including a manually designed excitation trajectory, a weighted least squares algorithm for identifying the dynamic parameters and a hand guiding controller for the arm’s direct teaching.

Findings

Compared with the conventional joint friction model, the proposed method can effectively predict friction factors during the dynamic motion of the arm. Then friction parameters are quantitatively obtained and compared with the proposed friction model and the conventional friction model indirectly. It is found that the average root mean square error of predicted six joints in the proposed method decreases by more than 54%. The arm’s force control with the full torque using the estimated dynamic parameters is qualitatively studied. It is concluded that a light-weight industrial robot can be dragged smoothly by the hand guiding.

Practical implications

In the present study, a systematic pipeline is proposed for identifying and controlling an industrial arm. The whole procedure has been verified in a commercial six DOF industrial arm. Based on the conducted experiment, it is found that the proposed approach is more accurate in comparison with conventional methods. A hand-guiding demo also illustrates that the proposed approach can provide the industrial arm with the full torque compensation. This essential functionality is widely required in many industrial arms such as kinaesthetic teaching.

Originality/value

First, a new friction model is proposed. Based on this model, identifying the dynamic parameter is carried out to obtain a set of model parameters of an industrial arm. Finally, a smooth hand guiding control is demonstrated based on the proposed dynamic model.

Details

Industrial Robot: the international journal of robotics research and application, vol. 47 no. 6
Type: Research Article
ISSN: 0143-991X

Keywords

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Article
Publication date: 1 March 1991

David Blake

The different types of estimators of rational expectations modelsare surveyed. A key feature is that the model′s solution has to be takeninto account when it is estimated…

Abstract

The different types of estimators of rational expectations models are surveyed. A key feature is that the model′s solution has to be taken into account when it is estimated. The two ways of doing this, the substitution and errors‐in‐variables methods, give rise to different estimators. In the former case, a generalised leastsquares or maximum‐likelihood type estimator generally gives consistent and efficient estimates. In the latter case, a generalised instrumental variable (GIV) type estimator is needed. Because the substitution method involves more complicated restrictions and because it resolves the solution indeterminacy in a more arbitary fashion, when there are forward‐looking expectations, the errors‐in‐variables solution with the GIV estimator is the recommended combination.

Details

Journal of Economic Studies, vol. 18 no. 3
Type: Research Article
ISSN: 0144-3585

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Article
Publication date: 22 May 2020

Mariusz Doszyń

The purpose of this paper is to present an algorithm of real estate mass appraisal in which the impact of attributes (real estate features) is estimated by inequality…

Abstract

Purpose

The purpose of this paper is to present an algorithm of real estate mass appraisal in which the impact of attributes (real estate features) is estimated by inequality restricted least squares (IRLS) model.

Design/methodology/approach

This paper presents the algorithm of real estate mass appraisal, which was also presented in the form of an econometric model. Vital problem related to econometric models of mass appraisal is multicollinearity. In this paper, a priori knowledge about parameters is used by imposing restrictions in the form of inequalities. IRLS model is therefore used to limit negative consequences of multicollinearity. In ordinary least squares (OLS) models, estimator variances might be inflated by multicollinearity, which could lead to wrong signs of estimates. In IRLS models, estimators efficiency is higher (estimator variances are lower), which could result in better appraisals.

Findings

The final effect of the analysis is a vector of the impact of real estate attributes on their value in the mass appraisal algorithm. After making expert corrections, the algorithm was used to evaluate 318 properties from the test set. Valuation errors were also discussed.

Originality/value

Restrictions in the form of inequalities were imposed on the parameters of the econometric model, ensuring the non-negativity and monotonicity of real estate attribute impact. In case of real estate, variables are usually correlated. OLS estimators are then inflated and inefficient. Imposing restrictions in form of inequalities could improve results because IRLS estimators are more efficient. In the case of results inconsistent with theoretical assumptions, the real estate mass appraisal algorithm enables having the obtained results adjusted by an expert. This can be important for low quality databases, which is often the case in underdeveloped real estate markets. Another reason for expert correction may be the low efficiency of a given real estate market.

Details

Journal of European Real Estate Research , vol. 13 no. 2
Type: Research Article
ISSN: 1753-9269

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