Sensitivity analysis of an ols multiple regression inference with respect to possible linear endogeneity in the explanatory variables, for both modest and for extremely large samples
Year of publication: |
2020
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Authors: | Ashley, Richard A. ; Parmeter, Christopher F. |
Published in: |
Econometrics : open access journal. - Basel : MDPI, ISSN 2225-1146, ZDB-ID 2717594-7. - Vol. 8.2020, 1/11, p. 1-24
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Subject: | big data | exogeneity | inference | instrumental variables | large samples | multiple regression | robustness | Schätztheorie | Estimation theory | IV-Schätzung | Instrumental variables | Stichprobenerhebung | Sampling | Sensitivitätsanalyse | Sensitivity analysis | Induktive Statistik | Statistical inference | Multiple Regression | Multiple regression | Regressionsanalyse | Regression analysis | Big Data | Big data | Robustes Verfahren | Robust statistics |
Type of publication: | Article |
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Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
Language: | English |
Other identifiers: | 10.3390/econometrics8010011 [DOI] hdl:10419/247559 [Handle] |
Classification: | C2 - Econometric Methods: Single Equation Models ; C15 - Statistical Simulation Methods; Monte Carlo Methods |
Source: | ECONIS - Online Catalogue of the ZBW |
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