Statistical Confluence Analysis by Means of Complete Regression Systems (University Institute of Economics, Oslo, 1934, pp. 5–8)

Frisch, Ragnar

doi:10.1017/cbo9781139170116.026

Cited by 126 publications

(179 citation statements)

References 0 publications

Supporting

Mentioning

155

Contrasting

Unclassified

Order By: Relevance

“…Since the seminal work of Frisch (1934), treatment of specification errors, particularly endogeneity, is regarded as a challenging problem in empirical economics. Endogeneity, measurement errors, or more broadly, specification errors may lead to an inconsistent ordinary least squares (OLS) estimator and yield unreliable results.…”

Section: Introductionmentioning

confidence: 99%

The Pástor-Stambaugh empirical model revisited: Evidence from robust instruments

Racicot

Rentz

2015

J Asset Manag

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

The Pástor-Stambaugh empirical model revisited: Evidence from robust instruments

Racicot

Rentz

2015

J Asset Manag

View full text Add to dashboard Cite

“…In the fundamental work of Frisch, 21 the analysis of measurement errors in the independent variables 22 of a regression, also called EIV, played a central role in the early days of econometrics. Although the economic literature acknowledges that EIV leads to inconsistent linear OLS estimators, this issue has only resurfaced with the work of Cragg 15 and Dagenais and Dagenais.…”

Section: Estimation Methodsmentioning

confidence: 99%

Hedge fund return specification with errors-in-variables

Coën

Hübner²,

Desfleurs³

2010

J Deriv Hedge Funds

View full text Add to dashboard Cite

In linear models for hedge fund returns, errors-in-variables may significantly alter the measurement of factor loadings and the estimation of abnormal performance. The higher moment estimator (HME) introduced by Dagenais and Dagenais (1997) effectively deals with these issues. Results on individual funds show that the HME specification does not uncover systematic performance biases, but can modify estimated alphas in most cases and identifies relative persistence for directional funds in bearish market conditions. www.palgrave-journals.com/jdhf/ Overall, the risk premia calculated with HME remain relatively stable when compared to ordinary least squares specifications.

show abstract

“…Important extensions to the ARMA(X) case have been proposed by Anderson (1971) Hannan (1970) later on. The problem of errors-in-variables modeling (when there are disturbances on both input and output measurements) also has its origins in econometrics, (Frisch, 1934).…”

Section: Econometrics and Time Series Analysismentioning

confidence: 99%

Perspectives on System Identification

Ljung

2008

IFAC Proceedings Volumes

178

View full text Add to dashboard Cite

Abstract:System identification is the art and science of building mathematical models of dynamic systems from observed input-output data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous necessity for successful applications. System identification is a very large topic, with different techniques that depend on the character of the models to be estimated: linear, nonlinear, hybrid, nonparametric etc. At the same time, the area can be characterized by a small number of leading principles, e.g. to look for sustainable descriptions by proper decisions in the triangle of model complexity, information contents in the data, and effective validation. The area has many facets and there are many approaches and methods. A tutorial or a survey in a few pages is not quite possible. Instead, this presentation aims at giving an overview of the "science" side, i.e. basic principles and results and at pointing to open problem areas in the practical, "art", side of how to approach and solve a real problem.

show abstract

Statistical Confluence Analysis by Means of Complete Regression Systems (University Institute of Economics, Oslo, 1934, pp. 5–8)

Cited by 126 publications

References 0 publications

The Pástor-Stambaugh empirical model revisited: Evidence from robust instruments

The Pástor-Stambaugh empirical model revisited: Evidence from robust instruments

Hedge fund return specification with errors-in-variables

Perspectives on System Identification

Contact Info

Product

Resources

About