Identification, weak instruments and statistical inference in econometrics
We discuss statistical inference problems associated with identification and testability in econometrics, and we emphasize the common nature of the two issues. After reviewing the relevant statistical notions, we consider in turn inference in nonparametric models and recent developments on weakly...
Đã lưu trong:
| Tác giả chính: | |
|---|---|
| Định dạng: | Sách |
| Ngôn ngữ: | English |
| Được phát hành: |
de Montreal
2012
|
| Những chủ đề: | |
| Truy cập trực tuyến: | https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/30546 |
| Các nhãn: |
Thêm thẻ
Không có thẻ, Là người đầu tiên thẻ bản ghi này!
|
| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
|---|
| Tóm tắt: | We discuss statistical inference problems associated with identification and testability in econometrics,
and we emphasize the common nature of the two issues. After reviewing the relevant
statistical notions, we consider in turn inference in nonparametric models and recent developments
on weakly identified models (or weak instruments). We point out that many hypotheses, for which
test procedures are commonly proposed, are not testable at all, while some frequently used econometric
methods are fundamentally inappropriate for the models considered. Such situations lead to
ill-defined statistical problems and are often associated with a misguided use of asymptotic distributional
results. Concerning nonparametric hypotheses, we discuss three basic problems for which
such difficulties occur: (1) testing a mean (or a moment) under (too) weak distributional assumptions;
(2) inference under heteroskedasticity of unknown form; (3) inference in dynamic models
with an unlimited number of parameters. Concerning weakly identified models, we stress that valid
inference should be based on proper pivotal functions—a condition not satisfied by standardWaldtype
methods based on standard errors — and we discuss recent developments in this field, mainly
from the viewpoint of building valid tests and confidence sets. The techniques discussed include
alternative proposed statistics, bounds, projection, split-sampling, conditioning, Monte Carlo tests.
The possibility of deriving a finite-sample distributional theory, robustness to the presence of weak
instruments, and robustness to the specification of a model for endogenous explanatory variables
are stressed as important criteria assessing alternative procedures. |
|---|