An Exact Recursive 2SLS Version of the Instrumental Variable Estimator. Stability Analysis

Larminat, De

doi:10.23919/acc.1984.4788538

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2009

2019

Publication Types

Select...

Article3

Other1

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

What Explains the Quantity and Quality of Local Inventive Activity?

Carlino

Hunt

2009

SSRN Journal

View full text Add to dashboard Cite

We geocode a data set of patents and their citation counts, including citations from abroad. This allows us to examine both the quantity and quality of local inventions. We also refine our data on local academic R&D to explore effects from different fields of science and sources of R&D funding. Finally, we incorporate data on congressional earmarks of funds for academic R&D. We discuss the implications of these results for policy and future research.

show abstract

What Explains the Quantity and Quality of Local Inventive Activity?

Carlino

Hunt

2009

SSRN Journal

View full text Add to dashboard Cite

show abstract

On the Recursive Algorithm of Bias Compensated Weighted Least Squares Method

Ikenoue

Kanae

Wada

2019

2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)

View full text Add to dashboard Cite

Identification of Errors-In-Variables Models Based on Bias Compensation of Weighted Least Squares Estimator

Ikenoue

Kanae

Wada

2018

Stochastic Systems Theory and its Applications (SSS)

View full text Add to dashboard Cite

This paper investigates the problem of identifying errors-in-variables (EIV) models, where the both input and output measurements are corrupted by white noise, and addresses a new method to solve the problem. The identification problem of EIV models with unknown noise variances has been extensively studied and several methods have been proposed. To be further developed in terms of estimation accuracy, a generalized eigenvector method with no requirement of a priori knowledge about the noise variances is proposed by using the biased weighted least squares estimator. The proposed generalized eigenvalue problem can be derived by removing only the bias induced by the output noise, and thus the system parameter can be obtained as the generalized eigenvector without requiring the use of iterative identification procedure. Moreover, the bias compensation principle based algorithm, which is suitable for on-line implementation, is derived to solve the proposed generalized eigenvalue problem. The results of simulated examples indicate that the proposed approach provides good parameter estimates.

show abstract

An Exact Recursive 2SLS Version of the Instrumental Variable Estimator. Stability Analysis

Cited by 4 publications

References 6 publications

What Explains the Quantity and Quality of Local Inventive Activity?

What Explains the Quantity and Quality of Local Inventive Activity?

On the Recursive Algorithm of Bias Compensated Weighted Least Squares Method

Identification of Errors-In-Variables Models Based on Bias Compensation of Weighted Least Squares Estimator

Contact Info

Product

Resources

About