2 edition of **Estimation and confidence regions for parameter sets in econometric models** found in the catalog.

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- 29 Currently reading

Published
**2006**
by Massachusetts Institute of Technology, Dept. of Economics in Cambridge, MA
.

Written in English

**Edition Notes**

Other titles | Inference on parameter sets in economic modes |

Statement | Victor Chernozhukov, Han Hong [and] Elie Tamer |

Series | Working paper series / Massachusetts Institute of Technology, Dept. of Economics -- working paper 06-18B, Working paper (Massachusetts Institute of Technology. Dept. of Economics) -- no. 06-18B. |

Contributions | Hong, Han, Tamer, Elie, Massachusetts Institute of Technology. Dept. of Economics |

The Physical Object | |
---|---|

Pagination | 47 p. ; |

Number of Pages | 47 |

ID Numbers | |

Open Library | OL24643505M |

OCLC/WorldCa | 122271482 |

In econometrics, the regression model is a common starting point of an analysis. As you define your regression model, you need to consider several elements: Economic theory, intuition, and common sense should all motivate your regression model. The most common regression estimation technique, ordinary least squares (OLS), obtains the best estimates of your model if [ ]. 5 Hypothesis Tests and Confidence Intervals in the Simple Linear Regression Model. This chapter, continues our treatment of the simple linear regression model. The following subsections discuss how we may use our knowledge about the sampling distribution of the OLS estimator in order to make statements regarding its uncertainty.

Uniformly Valid Post-Regularization Confidence Regions for Many Functional Parameters in Z-Estimation Framework, ArXiv , with D. Chetverikov, A. Belloni, and Y. Wei; Big Data: Prediction Methods. "High-Dimensional Sparse Econometric Models, an Introduction,"Springer Lecture Notes , with A. Belloni. The response variable is linear with the parameters. Y = B 1 X 1 +B 2 X 2 + + B k X k = Σ B j X j. Objective. The objective of the method is to estimate the parameters of the model, based on the observed n sets of values and by applying a certain criterium function (the observed sets of values are constituted by selected values of the auxiliary variable and by the corresponding observed.

Typical Problems Estimating Econometric Models. Recognizing Usual Variables: Normal Distribution. is the development of techniques to address such problems or other complications with the data that make standard model estimation difficult or unreliable. About the Book Author. Roberto Pedace. in models that do not point identify a parameter. Therefore, new methods for inference are developed. These methods construct con-fidence sets for partially identified parameters, and confidence regions for sets of parameters, or identifiable sets. Review in Advance first posted online on Febru (C hanges may.

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Specifically, we provide estimators and confidence regions for the set of minimizers Θ I of an econometric criterion function Q (θ). In applications, the criterion function embodies testable restrictions on economic models.

A parameter value θthat describes an economic model satisfies these restrictions if Q (θ) attains its minimum at this Cited by: Downloadable (with restrictions). This paper develops a framework for performing estimation and inference in econometric models with partial identification, focusing particularly on models characterized by moment inequalities and equalities.

Applications of this framework include the analysis of game-theoretic models, revealed preference restrictions, regressions with missing and corrupted. Request PDF | Estimation and Confidence Regions for Parameter Sets in Econometric Models1 | This paper develops a framework for performing estimation and inference in econometric models.

Specifically, this paper provides estimators and confidence regions for minima of an econometric criterion function Q([Theta]). In applications, Q([Theta]) embodies testable restrictions on economic models. A parameter [Theta] that describes an economic model passes these restrictions if Q([Theta]) attains the minimum value normalized to be : Supplementary Material for?Estimation and Confidence Regions for Parameter Sets in Econometric Models.

In the main text the true probability measure, P, is the nuisance parameter. In this supplementary material we examine which contiguous perturbations of the original fixed P preserve or do not preserve the estimation and coverage properties.

DOI: /jx Corpus ID: Estimation and confidence regions for parameter sets in econometric models @inproceedings{ChernozhukovEstimationAC, title={Estimation and confidence regions for parameter sets in econometric models}, author={Victor Chernozhukov and Han Hong and Elie Tamer}, year={} }.

Supplementary Material for?Estimation and Confidence Regions for Parameter Sets in Econometric Models. In the main text the true probability measure, P, is the nuisance parameter.

In this supplementary material we examine which contiguous perturbations of the original fixed P preserve or do not preserve the estimation and coverage properties. Parameter Sets in Econometric Models EXAMPLE 1 (cont.) † Compare the result with Beresteanu and Molinari ().

BM estimate ΘI by Θb I = h Y1;Y2 i which is the same with CHT. † For conﬁdence region, BM used p nmax n ﬂ ﬂ ﬂY1 ¡E[Y1] ﬂ ﬂ ﬂ; ﬂ ﬂ ﬂY2 ¡E[Y2] ﬂ. In order to conduct inference in partially identified econometric models defined by moment (in)equalities, the literature has proposed three methods: bootstrap, subsampling, and asymptotic approximation.

H., & Tamer, E. () Estimation and confidence regions for parameter sets in econometric models. Set Estimation and Inference with. Parameters of interest in econometric models can be deﬂned as those parameter vectors that minimize a population objective or criterion function.

If this criterion function is minimized uniquely at a particular parameter vector, then one can obtain valid conﬂdence regions (or intervals) for this parameter using a sample analog of this function.

Galichon, A. & Henry, M. () Dilation bootstrap: A methodology for constructing confidence regions with partially identified models. Journal of Econometrics– Horowitz, J.L. & Manski, C.F. () Identification and robustness with contaminated and corrupted data.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In the main text the true probability measure, P, is the nuisance parameter. In this supplementary material we examine which contiguous perturbations of the original fixed P preserve or do not preserve the estimation and coverage properties of the regions constructed in the main text.

ESTIMATION AND CONFIDENCE REGIONS FOR PARAMETER SETS IN ECONOMETRIC MODELS1 BY VICTOR CHERNOZHUKOV, HAN HONG, AND ELIE TAMER This paper develops a framework for performing estimation and inference in econo-metric models with partial identification, focusing particularly on models character-ized by moment inequalities and equalities.

Econometric relations are often simultaneous in the sense that some of their variables are connected by a system of such equations. These variables are called endogenous in the system and the others, the values of which are supposed to be determined outside the system, the statistical analysis of such relations is based on time series, a distinction is also made between lagged and.

in a very general class of partially identiﬁed econometric models. Let P denote the distribution of the observed data. The class of models we consider is deﬁned by a pop-ulation objective function Q(θP) for θ∈Θ. The point of departure from the classical extremum estimation framework is that it is not assumed that Q(θP) has a unique.

Chernozhukov, Hong and Tamer () “Estimation and confidence regions for parameter sets in econometric models,” Econometr 3. Asymptotic Normality – smooth case.

Whitney Newey and Daniel McFadden, () “Large sample estimation and hypothesis testing,” in Handbook of Econometrics, IV, ch 4. HBM 15S D^WEY MassachusettsInstituteofTechnology DepartmentofEconomics WorkingPaperSeries ESTIMATIONANDCONFIDENCEREGIONSFORPARAMETER SETSINECONOMETRICMODELS.

When the restrictions are parameterized, the test can be inverted to yield confidence regions for partially identified parameters, thereby complementing other proposals, primarily Chernozhukov et.

"See recent revision called "Estimation & confidence regions for parameter sets in economic modes," [SSRN] id# " Includes bibliographical references (p. ) This paper provides confidence regions for minima of an econometric criterion function Q([mu]). The minima form a set of parameters, [Theta]I, called the identified set.

Chernozhukov, V, H. Hong and E. Tamer,"Estimation and Confidence Regions for Parameter Sets in Econometric Models," Econometrica, 75(5), Lecture 4: Asymptotic Normality of Extremum Estimator.

Readings: Newey, W. K., and D. McFadden "Large Sample Estimation and Hypothesis Testing." In Handbook of Econometrics. Ch 36. An econometric model then is a set of joint probability distributions to which the true joint probability distribution of the variables under study is supposed to belong.

In the case in which the elements of this set can be indexed by a finite number of real-valued parameters, the model is called a parametric model ; otherwise it is a."Estimation and Confidence Regions for Parameter Sets in Econometric Models," Econometrica, Econometric Society, vol.

75(5), pagesSeptember. Charles F. Manski & Elie Tamer, " Inference on Regressions with Interval Data on a Regressor or Outcome," Econometrica, Econometric Society, vol. 70(2), pagesMarch. is thus a first-order confidence region for which the nominal α level is only reached asymptotically with respect to N and addition to the three approximations above, strongly depends on a covering probability, which itself depends on both intrinsic and parametric components of the model nonlinearity.

For the sake of improvement, we could propose a region depending only on intrinsic.