Data masking by noise addition and the estimation of nonparametric regression models

Accounting and Finance

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Published

Standard

Data masking by noise addition and the estimation of nonparametric regression models. / Lechner, Sandra; Pohlmeier, Winfried.
Econometrics of Anonymized Micro Data: Sonderheft 5/2005 Jahrbücher für Nationalökonomie und Statistik. De Gruyter, 2016. p. 517-528.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Lechner, S & Pohlmeier, W 2016, Data masking by noise addition and the estimation of nonparametric regression models. in Econometrics of Anonymized Micro Data: Sonderheft 5/2005 Jahrbücher für Nationalökonomie und Statistik. De Gruyter, pp. 517-528.

APA

Lechner, S., & Pohlmeier, W. (2016). Data masking by noise addition and the estimation of nonparametric regression models. In Econometrics of Anonymized Micro Data: Sonderheft 5/2005 Jahrbücher für Nationalökonomie und Statistik (pp. 517-528). De Gruyter.

Vancouver

Lechner S, Pohlmeier W. Data masking by noise addition and the estimation of nonparametric regression models. In Econometrics of Anonymized Micro Data: Sonderheft 5/2005 Jahrbücher für Nationalökonomie und Statistik. De Gruyter. 2016. p. 517-528

Author

Lechner, Sandra ; Pohlmeier, Winfried. / Data masking by noise addition and the estimation of nonparametric regression models. Econometrics of Anonymized Micro Data: Sonderheft 5/2005 Jahrbücher für Nationalökonomie und Statistik. De Gruyter, 2016. pp. 517-528

Bibtex

@inbook{bd31abc7e3404b159f951cadee455083,

title = "Data masking by noise addition and the estimation of nonparametric regression models",

abstract = "Data collecting institutions use a large range of masking procedures in order to protect data against disclosure. Generally, a masking procedure can be regarded as a kind of data filter that transforms the true data generating process. Such a transformation severely affects the quality of the data and limits its use for empirical research. A popular masking procedure is noise addition, which leads to inconsistent estimates if the additional measurement errors are ignored. This paper investigates to what extent appropriate econometric techniques can obtain consistent estimates of the true data generating process for parametric and nonparametric models when data is masked by noise addition. We show how the reduction of the data quality can be minimized using the local polynomial Simulation-Extrapolation (SIMEX) estimator. Evidence is provided by a Monte-Carlo study and by an application to firm-level data, where we analyze the impact of innovative activity on employment.",

author = "Sandra Lechner and Winfried Pohlmeier",

note = "Publisher Copyright: {\textcopyright} Lucius Sc Lucius Verlagsgesellschaft mbH Stuttgart 2005.",

year = "2016",

month = nov,

day = "21",

language = "English",

isbn = "9783828203259",

pages = "517--528",

booktitle = "Econometrics of Anonymized Micro Data",

publisher = "De Gruyter",

}

RIS

TY - CHAP

T1 - Data masking by noise addition and the estimation of nonparametric regression models

AU - Lechner, Sandra

AU - Pohlmeier, Winfried

PY - 2016/11/21

Y1 - 2016/11/21

N2 - Data collecting institutions use a large range of masking procedures in order to protect data against disclosure. Generally, a masking procedure can be regarded as a kind of data filter that transforms the true data generating process. Such a transformation severely affects the quality of the data and limits its use for empirical research. A popular masking procedure is noise addition, which leads to inconsistent estimates if the additional measurement errors are ignored. This paper investigates to what extent appropriate econometric techniques can obtain consistent estimates of the true data generating process for parametric and nonparametric models when data is masked by noise addition. We show how the reduction of the data quality can be minimized using the local polynomial Simulation-Extrapolation (SIMEX) estimator. Evidence is provided by a Monte-Carlo study and by an application to firm-level data, where we analyze the impact of innovative activity on employment.

AB - Data collecting institutions use a large range of masking procedures in order to protect data against disclosure. Generally, a masking procedure can be regarded as a kind of data filter that transforms the true data generating process. Such a transformation severely affects the quality of the data and limits its use for empirical research. A popular masking procedure is noise addition, which leads to inconsistent estimates if the additional measurement errors are ignored. This paper investigates to what extent appropriate econometric techniques can obtain consistent estimates of the true data generating process for parametric and nonparametric models when data is masked by noise addition. We show how the reduction of the data quality can be minimized using the local polynomial Simulation-Extrapolation (SIMEX) estimator. Evidence is provided by a Monte-Carlo study and by an application to firm-level data, where we analyze the impact of innovative activity on employment.

UR - http://www.scopus.com/inward/record.url?scp=85140163428&partnerID=8YFLogxK

M3 - Chapter

AN - SCOPUS:85140163428

SN - 9783828203259

SP - 517

EP - 528

BT - Econometrics of Anonymized Micro Data

PB - De Gruyter

ER -

Research