Abstract
Observations in a dataset are rarely missing at random. One can control for this non-random selection of the data by introducing fixed effects or other nuisance parameters. This chapter deals with consistent estimation the presence of many nuisance parameters. It derives a new orthogonality concept that gives sufficient conditions for consistent estimation of the parameters of interest. It also shows how this orthogonality concept can be used to derive and compare estimators. The chapter then shows how to use the orthogonality concept to derive estimators for unbalanced panels and incomplete data sets (missing data).
| Original language | English (US) |
|---|---|
| Title of host publication | Advances in Econometrics |
| Pages | 155-178 |
| Number of pages | 24 |
| Volume | 27 A |
| DOIs | |
| State | Published - 2011 |
| Externally published | Yes |
Publication series
| Name | Advances in Econometrics |
|---|---|
| Volume | 27 A |
| ISSN (Print) | 07319053 |
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Keywords
- Causal inference
- Information orthogonality
- Missing data
- Panel data
ASJC Scopus subject areas
- Economics and Econometrics
Cite this
Consistent estimation and orthogonality. / Woutersen, Tiemen M.
Advances in Econometrics. Vol. 27 A 2011. p. 155-178 17004567 (Advances in Econometrics; Vol. 27 A).Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Consistent estimation and orthogonality
AU - Woutersen, Tiemen M
PY - 2011
Y1 - 2011
N2 - Observations in a dataset are rarely missing at random. One can control for this non-random selection of the data by introducing fixed effects or other nuisance parameters. This chapter deals with consistent estimation the presence of many nuisance parameters. It derives a new orthogonality concept that gives sufficient conditions for consistent estimation of the parameters of interest. It also shows how this orthogonality concept can be used to derive and compare estimators. The chapter then shows how to use the orthogonality concept to derive estimators for unbalanced panels and incomplete data sets (missing data).
AB - Observations in a dataset are rarely missing at random. One can control for this non-random selection of the data by introducing fixed effects or other nuisance parameters. This chapter deals with consistent estimation the presence of many nuisance parameters. It derives a new orthogonality concept that gives sufficient conditions for consistent estimation of the parameters of interest. It also shows how this orthogonality concept can be used to derive and compare estimators. The chapter then shows how to use the orthogonality concept to derive estimators for unbalanced panels and incomplete data sets (missing data).
KW - Causal inference
KW - Information orthogonality
KW - Missing data
KW - Panel data
UR - http://www.scopus.com/inward/record.url?scp=84884661850&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84884661850&partnerID=8YFLogxK
U2 - 10.1108/S0731-9053(2011)000027A009
DO - 10.1108/S0731-9053(2011)000027A009
M3 - Chapter
SN - 9781780525242
VL - 27 A
T3 - Advances in Econometrics
SP - 155
EP - 178
BT - Advances in Econometrics
ER -