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You searched for: Journal Journal of econometrics Remove constraint Journal: Journal of econometrics Source 2017 v.200 no.2 Remove constraint Source: 2017 v.200 no.2 Subject economic analysis Remove constraint Subject: economic analysis Subject economic theory Remove constraint Subject: economic theory
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1. Bayesian moment-based inference in a regression model with misclassification error

Author:
Bollinger, Christopher R.; van Hasselt, Martijn
Source:
Journal of econometrics 2017 v.200 no.2 pp. 282-294
ISSN:
0304-4076
Subject:
Bayesian theory; algorithms; data collection; econometric models; economic analysis; economic theory; pain; probability; regression analysis
Abstract:
... We present a Bayesian analysis of a regression model with a binary covariate that may have classification (measurement) error. Prior research demonstrates that the regression coefficient is only partially identified. We take a Bayesian approach which adds assumptions in the form of priors on the unknown misclassification probabilities. The approach is intermediate between the frequentist bounds of ...
DOI:
10.1016/j.jeconom.2017.06.011

2. Consistent estimation of linear panel data models with measurement error

Author:
Meijer, Erik; Spierdijk, Laura; Wansbeek, Tom
Source:
Journal of econometrics 2017 v.200 no.2 pp. 169-180
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; equations; heteroskedasticity; regression analysis; variance covariance matrix
Abstract:
... Measurement error causes a bias towards zero when estimating a panel data linear regression model. The panel data context offers various opportunities to derive instrumental variables allowing for consistent estimation. We consider three sources of moment conditions: (i) restrictions on the covariance matrix of the errors in the equations, (ii) nonzero third moments of the regressors, and (iii) he ...
DOI:
10.1016/j.jeconom.2017.06.003

3. Counting rotten apples: Student achievement and score manipulation in Italian elementary Schools

Author:
Battistin, Erich; De Nadai, Michele; Vuri, Daniela
Source:
Journal of econometrics 2017 v.200 no.2 pp. 344-362
ISSN:
0304-4076
Subject:
academic achievement; apples; econometric models; economic analysis; economic theory; elementary schools; elementary students; statistical models; Italy
Abstract:
... We derive bounds on the distribution of math and language scores of elementary school students in Italy correcting for pervasive manipulation. A natural experiment that randomly assigns external monitors to schools is used to deal with endogeneity of manipulation, as well as its mismeasurement in the data. Bounds are obtained from properties of the statistical model used to detect classes with man ...
DOI:
10.1016/j.jeconom.2017.06.015

4. Identification of additive and polynomial models of mismeasured regressors without instruments

Author:
Ben-Moshe, Dan; D’Haultfœuille, Xavier; Lewbel, Arthur
Source:
Journal of econometrics 2017 v.200 no.2 pp. 207-222
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; mathematical models
Abstract:
... We show nonparametric point identification of a measurement error model with covariates that can be interpreted as invalid instruments. Our main contribution is to replace standard exclusion restrictions with the weaker assumption of additivity in the covariates. Measurement errors are ubiquitous and additive models are popular, so our results combining the two should have widespread potential app ...
DOI:
10.1016/j.jeconom.2017.06.006

5. Identification of first-price auctions with non-equilibrium beliefs: A measurement error approach

Author:
An, Yonghong
Source:
Journal of econometrics 2017 v.200 no.2 pp. 326-343
ISSN:
0304-4076
Subject:
USDA Forest Service; auctions; econometric models; economic analysis; economic theory; forest trees; United States
Abstract:
... This paper studies identification and estimation of two models for first-price auctions: (1) bidders’ beliefs about their opponents’ bidding behavior are not in equilibrium but follow “level-k” thinking, and (2) bidders’ values are asymmetrically distributed. Exploiting the nonparametric methodology developed for measurement error models (e.g., Hu, 2008), we show that both models can be identified ...
DOI:
10.1016/j.jeconom.2017.06.014

6. Instrumental variable estimation of nonlinear models with nonclassical measurement error using control variables

Author:
Hahn, Jinyong; Ridder, Geert
Source:
Journal of econometrics 2017 v.200 no.2 pp. 238-250
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; nonlinear models
Abstract:
... We consider nonlinear parametric models with an independent variable that is measured with error. The measurement error can be correlated with the true value, i.e., the measurement error is allowed to be nonclassical. We propose a control variable estimator for the parameters of interest. The estimator is consistent even if the latent true value is endogenous. We derive the influence function of t ...
DOI:
10.1016/j.jeconom.2017.06.008

7. Many IVs estimation of dynamic panel regression models with measurement error

Author:
Lee, Nayoung; Moon, Hyungsik Roger; Zhou, Qiankun
Source:
Journal of econometrics 2017 v.200 no.2 pp. 251-259
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; regression analysis
Abstract:
... In this paper, we investigate a dynamic linear panel regression model with measurement error. We consider the panel data estimation whose time dimension (T) is not small and comparable to the cross sectional dimension (N). First, we show that the 2SLS estimator suffers from the bias problem due to many instrumental variables. Using the alternative asymptotics where T3N goes to a constant as N,T→∞, ...
DOI:
10.1016/j.jeconom.2017.06.009

8. Misclassification in binary choice models

Author:
Meyer, Bruce D.; Mittag, Nikolas
Source:
Journal of econometrics 2017 v.200 no.2 pp. 295-311
ISSN:
0304-4076
Subject:
covariance; econometric models; economic analysis; economic theory
Abstract:
... Bias from misclassification of binary dependent variables can be pronounced. We examine what can be learned from such contaminated data. First, we derive the asymptotic bias in parametric models allowing misclassification to be correlated with observables and unobservables. Simulations and validation data show that the bias formulas are accurate in finite samples and in most situations imply atten ...
DOI:
10.1016/j.jeconom.2017.06.012

9. Modeling heaped duration data: An application to neonatal mortality

Author:
Arulampalam, Wiji; Corradi, Valentina; Gutknecht, Daniel
Source:
Journal of econometrics 2017 v.200 no.2 pp. 363-377
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; household surveys; neonatal mortality; statistical analysis
Abstract:
... In 2005, the Indian Government launched a conditional cash-incentive program to encourage institutional delivery. This paper studies the effects of the program on neonatal mortality using district-level household survey data. We model mortality using survival analysis, paying special attention to substantial heaping, a form of measurement error, present in the data. The main objective of this pape ...
DOI:
10.1016/j.jeconom.2017.06.016

10. Regression discontinuity design with continuous measurement error in the running variable

Author:
Davezies, Laurent; Le Barbanchon, Thomas
Source:
Journal of econometrics 2017 v.200 no.2 pp. 260-281
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; unemployment
Abstract:
... Since the late 90s, Regression Discontinuity (RD) designs have been widely used to estimate Local Average Treatment Effects (LATE). When the running variable is observed with continuous measurement error, identification fails. Assuming non-differential measurement error, we propose a consistent nonparametric estimator of the LATE when the discrepancy between the true running variable and its noisy ...
DOI:
10.1016/j.jeconom.2017.06.010

11. Semiparametric identification of the bid–ask spread in extended Roll models

Author:
Chen, Xiaohong; Linton, Oliver; Yi, Yanping
Source:
Journal of econometrics 2017 v.200 no.2 pp. 312-325
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; prices; trade
Abstract:
... This paper provides new identification results for the bid–ask spread and the nonparametric distribution of the latent fundamental price increments (εt) from the observed transaction prices alone. The results are established via the characteristic function approach, and hence allow for discrete or continuous εt and the observed price increments do not need to have any finite moments. Constructive ...
DOI:
10.1016/j.jeconom.2017.06.013

12. Simulated minimum distance estimation of dynamic models with errors-in-variables

Author:
Gospodinov, Nikolay; Komunjer, Ivana; Ng, Serena
Source:
Journal of econometrics 2017 v.200 no.2 pp. 181-193
ISSN:
0304-4076
Subject:
algorithms; dynamic models; econometric models; economic analysis; economic theory; empirical research; least squares; risk
Abstract:
... Empirical analysis often involves using inexact measures of the predictors suggested by economic theory. The bias created by the correlation between the mismeasured regressors and the error term motivates the need for instrumental variable estimation. This paper considers a class of estimators that can be used in dynamic models with measurement errors when external instruments may not be available ...
DOI:
10.1016/j.jeconom.2017.06.004

13. Simultaneous treatment of unspecified heteroskedastic model error distribution and mismeasured covariates for restricted moment models

Author:
Garcia, Tanya P.; Ma, Yanyuan
Source:
Journal of econometrics 2017 v.200 no.2 pp. 194-206
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; heteroskedasticity; regression analysis
Abstract:
... We develop consistent and efficient estimation of parameters in general regression models with mismeasured covariates. We assume the model error and covariate distributions are unspecified, and the measurement error distribution is a general parametric distribution with unknown variance–covariance. We construct root-n consistent, asymptotically normal and locally efficient estimators using the sem ...
DOI:
10.1016/j.jeconom.2017.06.005
PubMed:
29200600
PubMed Central:
PMC5708600

14. The econometrics of unobservables: Applications of measurement error models in empirical industrial organization and labor economics

Author:
Hu, Yingyao
Source:
Journal of econometrics 2017 v.200 no.2 pp. 154-168
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; labor economics; microeconomics
Abstract:
... This paper reviews the recent developments in nonparametric identification of measurement error models and their applications in applied microeconomics, in particular, in empirical industrial organization and labor economics. Measurement error models describe mappings from a latent distribution to an observed distribution. The identification and estimation of measurement error models focus on how ...
DOI:
10.1016/j.jeconom.2017.06.002

15. The precision of subjective data and the explanatory power of economic models

Author:
Drerup, Tilman; Enke, Benjamin; von Gaudecker, Hans-Martin
Source:
Journal of econometrics 2017 v.200 no.2 pp. 378-389
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; risk; stock exchange
Abstract:
... Subjective expectations are important primitives in many economic models, yet their direct measurement often yields imprecise and inconsistent data. This has previously been treated as a pure measurement error problem. In contrast, this paper argues that the individual-level precision of such data may reflect the structure of the underlying decision process. We estimate a semiparametric double ind ...
DOI:
10.1016/j.jeconom.2017.06.017

16. Understanding the effect of measurement error on quantile regressions

Author:
Chesher, Andrew
Source:
Journal of econometrics 2017 v.200 no.2 pp. 223-237
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; regression analysis; variance
Abstract:
... The impact of measurement error in explanatory variables on quantile regression functions is investigated using a small variance approximation. The approximation shows how the error contaminated and error free quantile regression functions are related. A key factor is the distribution of the error free explanatory variable. Exact calculations probe the accuracy of the approximation. The order of t ...
DOI:
10.1016/j.jeconom.2017.06.007