Journal articles: 'IV estimation'

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Kitamura, Yuichi, and Peter C. B. Phillips. "Efficient IV Estimation in Nonstationary Regression." Econometric Theory 11, no. 5 (October 1995): 1095–130. http://dx.doi.org/10.1017/s026646660000997x.

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A limit theory for instrumental variables (IV) estimation that allows for possibly nonstationary processes was developed in Kitamura and Phillips (1992, Fully Modified IV, GIVE, and GMM Estimation with Possibly Non-stationary Regressors and Instruments, mimeo, Yale University). This theory covers a case that is important for practitioners, where the nonstationarity of the regressors may not be of full rank, and shows that the fully modified (FM) regression procedure of Phillips and Hansen (1990) is still applicable. FM. versions of the generalized method of moments (GMM) estimator and the generalized instrumental variables estimator (GIVE) were also developed, and these estimators (FM-GMM and FM-GIVE) were designed specifically to take advantage of potential stationarity in the regressors (or unknown linear combinations of them). These estimators were shown to deliver efficiency gains over FM-IV in the estimation of the stationary components of a model.This paper provides an overview of the FM-IV, FM-GMM, and FM-GIVE procedures and investigates the small sample properties of these estimation procedures by simulations. We compare the following five estimation methods: ordinary least squares, crude (conventional) IV, FM-IV, FM-GMM, and FM-GIVE. Our findings are as follows, (i) In terms of overall performance in both stationary and nonstationary cases, FM-IV is more concentrated and better centered than OLS and crude IV, though it has a higher root mean square error than crude IV due to occasional outliers, (ii) Among FM-IV, FM-GMM, and FM-GIVE, (a) when applied to the stationary coefficients, FM-GIVE generally outperforms FM-IV and FM-GMM by a wide margin, whereas the difference between the latter two is quite small when the AR roots of the stationary processes are rather large; and (b) when applied to the nonstationary coefficients, the three estimators are numerically very close. The performance of the FM-GIVE estimator is generally very encouraging.

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Skeels, Christopher L., and Larry W. Taylor. "Prediction after IV estimation." Economics Letters 122, no. 3 (March 2014): 420–22. http://dx.doi.org/10.1016/j.econlet.2014.01.003.

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Benson, David, Matthew A. Masten, and Alexander Torgovitsky. "ivcrc: An instrumental-variables estimator for the correlated random-coefficients model." Stata Journal: Promoting communications on statistics and Stata 22, no. 3 (September 2022): 469–95. http://dx.doi.org/10.1177/1536867x221124449.

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We discuss the ivcrc command, which implements an instrumental-variables (IV) estimator for the linear correlated random-coefficients model. The correlated random-coefficients model is a natural generalization of the standard linear IV model that allows for endogenous, multivalued treatments and unobserved heterogeneity in treatment effects. The estimator implemented by ivcrc uses recent semiparametric identification results that allow for flexible functional forms and permit instruments that may be binary, discrete, or continuous. The ivcrc command also allows for the estimation of varying-coefficient regressions, which are closely related in structure to the proposed IV estimator. We illustrate the use of ivcrc by estimating the returns to education in the National Longitudinal Survey of Young Men.

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Benson, David, Matthew A. Masten, and Alexander Torgovitsky. "ivcrc: An Instrumental Variables Estimator for the Correlated Random Coefficients Model." Finance and Economics Discussion Series 2020, no. 046r1 (April 4, 2022): 1–29. http://dx.doi.org/10.17016/feds.2020.046r1.

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We discuss the ivcrc module, which implements an instrumental variables (IV) estimator for the linear correlated random coefficients (CRC) model. The CRC model is a natural generalization of the standard linear IV model that allows for endogenous, multivalued treatments and unobserved heterogeneity in treatment effects. The estimator implemented by ivcrc uses recent semiparametric identification results that allow for flexible functional forms and permit instruments that may be binary, discrete, or continuous. The ivcrc module also allows for the estimation of varying coefficients regressions, which are closely related in structure to the proposed IV estimator. We illustrate use of ivcrc by estimating the returns to education in the National Longitudinal Survey of Young Men.

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Lange, Theis, and Aksel K. G. Jensen. "IV estimation without distributional assumptions." Biometrical Journal 62, no. 3 (February 5, 2020): 688–96. http://dx.doi.org/10.1002/bimj.201800277.

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Park, Soojin, and Gregory J. Palardy. "Sensitivity Evaluation of Methods for Estimating Complier Average Causal Mediation Effects to Assumptions." Journal of Educational and Behavioral Statistics 45, no. 4 (March 9, 2020): 475–506. http://dx.doi.org/10.3102/1076998620908599.

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Estimating the effects of randomized experiments and, by extension, their mediating mechanisms, is often complicated by treatment noncompliance. Two estimation methods for causal mediation in the presence of noncompliance have recently been proposed, the instrumental variable method (IV-mediate) and maximum likelihood method (ML-mediate). However, little research has examined their performance when certain assumptions are violated and under varying data conditions. This article addresses that gap in the research and compares the performance of the two methods. The results show that the distributional assumption of the compliance behavior plays an important role in estimation. That is, regardless of the estimation method or whether the other assumptions hold, results are biased if the distributional assumption is not met. We also found that the IV-mediate method is more sensitive to exclusion restriction violations, while the ML-mediate method is more sensitive to monotonicity violations. Moreover, estimates depend in part on compliance rate, sample size, and the availability and impact of control covariates. These findings are used to provide guidance on estimator selection.

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Albuquerque, Pedro Henrique Melo, and Mariana Rosa Montenegro. "PROMETHEE IV through kernel density estimation." Communications in Statistics - Theory and Methods 45, no. 18 (December 17, 2015): 5355–62. http://dx.doi.org/10.1080/03610926.2014.942432.

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Carrara, Nicholas, and Jesse Ernst. "On the Estimation of Mutual Information." Proceedings 33, no. 1 (January 15, 2020): 31. http://dx.doi.org/10.3390/proceedings2019033031.

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In this paper we focus on the estimation of mutual information from finite samples ( X × Y ) . The main concern with estimations of mutual information (MI) is their robustness under the class of transformations for which it remains invariant: i.e., type I (coordinate transformations), III (marginalizations) and special cases of type IV (embeddings, products). Estimators which fail to meet these standards are not robust in their general applicability. Since most machine learning tasks employ transformations which belong to the classes referenced in part I, the mutual information can tell us which transformations are most optimal. There are several classes of estimation methods in the literature, such as non-parametric estimators like the one developed by Kraskov et al., and its improved versions. These estimators are extremely useful, since they rely only on the geometry of the underlying sample, and circumvent estimating the probability distribution itself. We explore the robustness of this family of estimators in the context of our design criteria.

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Mitnik, Pablo A. "Intergenerational Income Elasticities, Instrumental Variable Estimation, and Bracketing Strategies." Sociological Methodology 50, no. 1 (January 7, 2020): 1–46. http://dx.doi.org/10.1177/0081175019887992.

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The fact that the intergenerational income elasticity (IGE)—the workhorse measure of economic mobility—is defined in terms of the geometric mean of children’s income generates serious methodological problems. This has led to a call to replace it with the IGE of the expectation, which requires developing the methodological knowledge necessary to estimate the latter with short-run measures of income. This article contributes to this aim. The author advances a “bracketing strategy” for the set estimation of the IGE of the expectation that is equivalent to that used to set estimate (rather than point estimate) the conventional IGE with estimates obtained with the ordinary least squares and instrumental variable (IV) estimators. The proposed bracketing strategy couples estimates generated with the Poisson pseudo–maximum likelihood estimator and a generalized method of moments IV estimator of the Poisson or exponential regression model. The author develops a generalized error-in-variables model for the IV estimation of the IGE of the expectation and compares it with the corresponding model underlying the IV estimation of the conventional IGE. By considering both bracketing strategies from the perspective of the partial-identification approach to inference, the author specifies how to construct confidence intervals for the IGEs, in particular when the upper bound is estimated more than once with different sets of instruments. Finally, using data from the Panel Study of Income Dynamics, the author shows that the bracketing strategies work as expected and assesses the information they generate and how this information varies across instruments and short-run measures of parental income. Three computer programs made available as companions to the article make the set estimation of IGEs, and statistical inference, very simple endeavors.

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Tadano, Kotaro, Kenji Kawashima, Kazuyuki Kojima, and Naofumi Tanaka. "Development of a Pneumatic Surgical Manipulator IBIS IV." Journal of Robotics and Mechatronics 22, no. 2 (April 20, 2010): 179–88. http://dx.doi.org/10.20965/jrm.2010.p0179.

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In teleoperated, minimally invasive surgery systems, the measurement and conveyance of a sense of force to the operator is problematic. In order to carry out safer and more precise operations using robotic manipulators, force measurement and operator feedback are very important factors. We previously proposed a pneumatic surgical manipulator that is capable of estimating external force without the use of force sensors. However, the force estimation had a sensitivity of only 3 N because of inertia and friction effects. In this paper, we develop a new and improved model of the pneumatic surgical manipulator, IBIS IV. We evaluate its performance in terms of force estimation. The experimental results indicate that IBIS IV estimates external forces with a sensitivity of 1.0 N. We also conduct an in-vivo experiment and confirm the effectiveness and improvement of the manipulator.

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Kaji, Tetsuya. "Theory of Weak Identification in Semiparametric Models." Econometrica 89, no. 2 (2021): 733–63. http://dx.doi.org/10.3982/ecta16413.

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We provide general formulation of weak identification in semiparametric models and an efficiency concept. Weak identification occurs when a parameter is weakly regular, that is, when it is locally homogeneous of degree zero. When this happens, consistent or equivariant estimation is shown to be impossible. We then show that there exists an underlying regular parameter that fully characterizes the weakly regular parameter. While this parameter is not unique, concepts of sufficiency and minimality help pin down a desirable one. If estimation of minimal sufficient underlying parameters is inefficient, it introduces noise in the corresponding estimation of weakly regular parameters, whence we can improve the estimators by local asymptotic Rao–Blackwellization. We call an estimator weakly efficient if it does not admit such improvement. New weakly efficient estimators are presented in linear IV and nonlinear regression models. Simulation of a linear IV model demonstrates how 2SLS and optimal IV estimators are improved.

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12

Baum, Christopher F., Mark E. Schaffer, and Steven Stillman. "Instrumental Variables and GMM: Estimation and Testing." Stata Journal: Promoting communications on statistics and Stata 3, no. 1 (March 2003): 1–31. http://dx.doi.org/10.1177/1536867x0300300101.

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We discuss instrumental variables (IV) estimation in the broader context of the generalized method of moments (GMM), and describe an extended IV estimation routine that provides GMM estimates as well as additional diagnostic tests. Stand-alone test procedures for heteroskedasticity, overidentification, and endogeneity in the IV context are also described.

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13

Marine Carrasco and Mohamed Doukali. "Efficient Estimation Using Regularized Jackknife IV Estimator." Annals of Economics and Statistics, no. 128 (2017): 109. http://dx.doi.org/10.15609/annaeconstat2009.128.0109.

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14

Rilstone, Paul. "Semiparametric IV Estimation with Parameter Dependent Instruments." Econometric Theory 8, no. 3 (September 1992): 403–6. http://dx.doi.org/10.1017/s0266466600013001.

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A well-known result in the method of moments literature is that the efficient instruments for the estimation of a model are functions of the conditional expectation of its gradient. Some recent studies have suggested the nonparametric estimation of these instruments when they are of unknown functional form. When these instruments in turn depend on the unknown parameters it has been suggested that these be replaced by preliminary consistent estimates. It is shown here that solving the sample moment equations simultaneously over the instruments and the residuals of the model will generally produce the same asymptotic efficiency and avoid the disadvantages inherent with the use of preliminary estimates.

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15

Robertson, Donald, and Vasilis Sarafidis. "IV estimation of panels with factor residuals." Journal of Econometrics 185, no. 2 (April 2015): 526–41. http://dx.doi.org/10.1016/j.jeconom.2014.12.001.

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Andresen, Martin E., and Martin Huber. "Instrument-based estimation with binarised treatments: issues and tests for the exclusion restriction." Econometrics Journal 24, no. 3 (February 2, 2021): 536–58. http://dx.doi.org/10.1093/ectj/utab002.

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Summary When estimating local average and marginal treatment effects using instrumental variables (IVs), multivalued endogenous treatments are frequently converted to binary measures, supposedly to improve interpretability or policy relevance. Such binarisation introduces a violation of the IV exclusion if (a) the IV affects the multivalued treatment within support areas below and/or above the threshold and (b) such IV-induced changes in the multivalued treatment affect the outcome. We discuss assumptions that satisfy the IV exclusion restriction with a binarised treatment and permit identifying the average effect of (a) the binarised treatment and (b) unit-level increases in the original multivalued treatment among specific compliers. We derive testable implications of these assumptions and propose tests which we apply to the estimation of the returns to college graduation instrumented by college proximity.

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Dai, Xiaowen, and Libin Jin. "Minimum distance quantile regression for spatial autoregressive panel data models with fixed effects." PLOS ONE 16, no. 12 (December 14, 2021): e0261144. http://dx.doi.org/10.1371/journal.pone.0261144.

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This paper considers the quantile regression model with individual fixed effects for spatial panel data. Efficient minimum distance quantile regression estimators based on instrumental variable (IV) method are proposed for parameter estimation. The proposed estimator is computational fast compared with the IV-FEQR estimator proposed by Dai et al. (2020). Asymptotic properties of the proposed estimators are also established. Simulations are conducted to study the performance of the proposed method. Finally, we illustrate our methodologies using a cigarettes demand data set.

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Kripfganz, Sebastian, and Vasilis Sarafidis. "Instrumental-variable estimation of large-T panel-data models with common factors." Stata Journal: Promoting communications on statistics and Stata 21, no. 3 (September 2021): 659–86. http://dx.doi.org/10.1177/1536867x211045558.

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In this article, we introduce the xtivdfreg command, which implements a general instrumental-variables (IV) approach for fitting panel-data models with many time-series observations, T, and unobserved common factors or interactive effects, as developed by Norkute et al. (2021, Journal of Econometrics 220: 416–446) and Cui et al. (2020a , ISER Discussion Paper 1101). The underlying idea of this approach is to project out the common factors from exogenous covariates using principal-components analysis and to run IV regression in both of two stages, using defactored covariates as instruments. The resulting two-stage IV estimator is valid for models with homogeneous or heterogeneous slope coefficients and has several advantages relative to existing popular approaches. In addition, the xtivdfreg command extends the two-stage IV approach in two major ways. First, the algorithm accommodates estimation of unbalanced panels. Second, the algorithm permits a flexible specification of instruments. We show that when one imposes zero factors, the xtivdfreg command can replicate the results of the popular Stata ivregress command. Notably, unlike ivregress, xtivdfreg permits estimation of the two-way error-components paneldata model with heterogeneous slope coefficients.

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Kripfganz, Sebastian, and Vasilis Sarafidis. "Instrumental-variable estimation of large-T panel-data models with common factors." Stata Journal: Promoting communications on statistics and Stata 21, no. 3 (September 2021): 659–86. http://dx.doi.org/10.1177/1536867x211045558.

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In this article, we introduce the xtivdfreg command, which implements a general instrumental-variables (IV) approach for fitting panel-data models with many time-series observations, T, and unobserved common factors or interactive effects, as developed by Norkute et al. (2021, Journal of Econometrics 220: 416–446) and Cui et al. (2020a , ISER Discussion Paper 1101). The underlying idea of this approach is to project out the common factors from exogenous covariates using principal-components analysis and to run IV regression in both of two stages, using defactored covariates as instruments. The resulting two-stage IV estimator is valid for models with homogeneous or heterogeneous slope coefficients and has several advantages relative to existing popular approaches. In addition, the xtivdfreg command extends the two-stage IV approach in two major ways. First, the algorithm accommodates estimation of unbalanced panels. Second, the algorithm permits a flexible specification of instruments. We show that when one imposes zero factors, the xtivdfreg command can replicate the results of the popular Stata ivregress command. Notably, unlike ivregress, xtivdfreg permits estimation of the two-way error-components paneldata model with heterogeneous slope coefficients.

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Grossmann, Volker, and Aderonke Osikominu. "Let the Data Speak? On the Importance of Theory-Based Instrumental Variable Estimations." German Economic Review 20, no. 4 (December 1, 2019): e831-e851. http://dx.doi.org/10.1111/geer.12192.

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Abstract In absence of randomized-controlled experiments, identification is often aimed via instrumental variable (IV) strategies, typically two-stage least squares estimations. According to Bayes’ rule, however, under a low ex ante probability that a hypothesis is true (e.g. that an excluded instrument is partially correlated with an endogenous regressor), the interpretation of the estimation results may be fundamentally flawed. This paper argues that rigorous theoretical reasoning is key to design credible identification strategies, the foremost, finding candidates for valid instruments. We discuss prominent IV analyses from the macro-development literature to illustrate the potential benefit of structurally derived IV approaches.

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PHILLIPS, PETER C. B. "DYNAMIC PANEL ANDERSON-HSIAO ESTIMATION WITH ROOTS NEAR UNITY." Econometric Theory 34, no. 2 (September 22, 2015): 253–76. http://dx.doi.org/10.1017/s0266466615000298.

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Limit theory is developed for the dynamic panel IV estimator in the presence of an autoregressive root near unity. In the unit root case, Anderson–Hsiao lagged variable instruments satisfy orthogonality conditions but are well known to be irrelevant. For a fixed time series sample size (T) IV is inconsistent and approaches a shifted Cauchy-distributed random variate as the cross-section sample sizen→ ∞. But whenT→ ∞, either for fixednor asn→ ∞, IV is$\sqrt T$consistent and its limit distribution is a ratio of random variables that converges to twice a standard Cauchy asn→ ∞. In this case, the usual instruments are uncorrelated with the regressor but irrelevance does not prevent consistent estimation. The same Cauchy limit theory holds sequentially and jointly as (n,T) → ∞ with no restriction on the divergence rates ofnandT.When the common autoregressive root$\rho = 1 + c/\sqrt T$the panel comprises a collection of mildly integrated time series. In this case, the IV estimator is$\sqrt n$consistent for fixedTand$\sqrt {nT}$consistent with limit distributionN(0, 4) when (n,T) → ∞ sequentially or jointly. These results are robust for common roots of the formρ= 1+c/Tγfor allγ∈ (0, 1) and joint convergence holds. Limit normality holds but the variance changes whenγ= 1. Whenγ> 1 joint convergence fails and sequential limits differ with different rates of convergence. These findings reveal the fragility of conventional Gaussian IV asymptotics to persistence in dynamic panel regressions.

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Aronow, Peter M., and Allison Carnegie. "Beyond LATE: Estimation of the Average Treatment Effect with an Instrumental Variable." Political Analysis 21, no. 4 (2013): 492–506. http://dx.doi.org/10.1093/pan/mpt013.

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Political scientists frequently use instrumental variables (IV) estimation to estimate the causal effect of an endogenous treatment variable. However, when the treatment effect is heterogeneous, this estimation strategy only recovers the local average treatment effect (LATE). The LATE is an average treatment effect (ATE) for a subset of the population: units that receive treatment if and only if they are induced by an exogenous IV. However, researchers may instead be interested in the ATE for the entire population of interest. In this article, we develop a simple reweighting method for estimating the ATE, shedding light on the identification challenge posed in moving from the LATE to the ATE. We apply our method to two published experiments in political science in which we demonstrate that the LATE has the potential to substantively differ from the ATE.

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Wahyuni, Sry, Mardiana Ahmad, Syafruddin Syarif, Nasrudin A. Mappaware, Prihantono Prihantono, and Burhanuddin Bahar. "Digital Scales of Web-Based Partograph in Detecting the Early Postpartum Bleeding." Global Journal of Health Science 11, no. 6 (May 5, 2019): 86. http://dx.doi.org/10.5539/gjhs.v11n6p86.

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INTRODUCTION: A simple graph partograph serves to record the information of the inpartu mothers during their 1st active phase of labor, to detect complications, to make an appropriate action in decision making, to prevent prolonged labor, postpartum hemorrhage, and sepsis. The aimof this research was to compare the use of digital scales of web-based partograph and the conventional partograph in detecting the early estimation of postpartum bleeding at the stage III and IV. MATERIALS & METHODS: The study used the Quasi-Experiment method.Thirty women in labor were chosen by using the purposive sampling technique. The web digital scale and the conventional digital scale were used to weigh the underpad of the postpartum at the stage III and IV. The Independent T-test and Mann-Whitney test were employed to analyze the data. RESULTS: The results of the study indicated that the digital scale of the WEB-based partograph was more rapid in estimating the postpartum hemorrhage at the stage III and IV comparing to the digital scale of conventional partograph. The statistical test of Mann-Whitney revealed the p-value > 0.05, which means that there was a difference in the speed but in case of the accuracy aspect of p-value > 0.05, it means that there was no difference. In case of the estimation of the blood amount at stage III and IV, the test value of the Independent T-test revealed the p-value of > 0.05, which means that there was no difference in the estimation of the postpartum hemorrhage. CONCLUSION: It was concluded that the digital scale of web-based partograph was faster in estimating postpartum hemorrhage at period III and IV comparing to the digital scales of conventional partograph and there were no differences in its accurateness and number of postpartum hemorrhage at the stage III and IV.

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Marsac, Rémi, Florent Réal, Nidhu Lal Banik, Mathieu Pédrot, Olivier Pourret, and Valérie Vallet. "Aqueous chemistry of Ce(iv): estimations using actinide analogues." Dalton Trans. 46, no. 39 (2017): 13553–61. http://dx.doi.org/10.1039/c7dt02251d.

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Bhattacharyya, Malay, Abhishek Chaudhary, and Gaurav Yadav. "Conditional VaR estimation using Pearson’s type IV distribution." European Journal of Operational Research 191, no. 2 (December 2008): 386–97. http://dx.doi.org/10.1016/j.ejor.2007.07.021.

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Gabriel, R., S. Keller, J. Matthes, P. Waibel, H. B. Keller, and S. Hinz. "INFRARED MEASUREMENTS AND ESTIMATION OF TEMPERATURE IN THE RESTRICTIVE SCOPE OF AN INDUSTRIAL CEMENT PLANT." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-1 (September 26, 2018): 53–60. http://dx.doi.org/10.5194/isprs-annals-iv-1-53-2018.

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Abstract. In this paper, we describe and evaluate the process of estimating reflectance corrected temperatures based on infrared measurements in the scope of an industrial cement production plant. We overview the underlying cement production phases, as well as the resulting challenges for infrared-based monitoring in such an industrial environment. Our studies are focused particularly on the use of infrared sensors in the clinker cooling process. Using a highly specialized infrared camera (10.6&mu;m), a dataset is obtained capturing the radiation emissions of cement clinker during the clinker cooling process at a cement plant. We briefly turn on the necessity of image preprocessing and then focus on calculating reflectance corrected thermal images for temperature estimation without the use of reference markers or additional instrumentation. This study represents the first usage of infrared camera-based measurements in the clinker cooling process. The main contributions, a recorded dataset and two proposed estimation models including a linear model and a machine learning model with their respective temperature estimations, will provide the basis for the extraction of further process characteristics. Therefore, our contributions will enable scientists as well as process operators to gain new insights about the cement clinker cooling process and to optimize the cement cooling and production process automatically.

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Skeels, Christopher L. "Some Exact Results for Estimators of the Coefficients on the Exogenous Variables in a Single Equation." Econometric Theory 11, no. 3 (June 1995): 484–97. http://dx.doi.org/10.1017/s0266466600009373.

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This paper is devoted to a detailed examination of the exact sampling properties of the instrumental variables (IV) estimator of the vector of coefficients on the exogenous variables in a single structural equation. The first two moments of a linear combination of the elements of this estimator and the joint distribution of these elements are considered. Estimable bounds for the first moment that can readily be incorporated into any IV estimation package are provided. The results obtained are in terms of the same special functions as those that characterize other results for this model, allowing a unified treatment of the model.

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Choi, Byeong Yeob. "Instrumental variable estimation of truncated local average treatment effects." PLOS ONE 16, no. 4 (April 5, 2021): e0249642. http://dx.doi.org/10.1371/journal.pone.0249642.

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Instrumental variable (IV) analysis is used to address unmeasured confounding when comparing two nonrandomized treatment groups. The local average treatment effect (LATE) is a causal estimand that can be identified by an IV. The LATE approach is appealing because its identification relies on weaker assumptions than those in other IV approaches requiring a homogeneous treatment effect assumption. If the instrument is confounded by some covariates, then one can use a weighting estimator, for which the outcome and treatment are weighted by instrumental propensity scores. The weighting estimator for the LATE has a large variance when the IV is weak and the target population, i.e., the compliers, is relatively small. We propose a truncated LATE that can be estimated more reliably than the regular LATE in the presence of a weak IV. In our approach, subjects who contribute substantially to the weak IV are identified by their probabilities of being compliers, and they are removed based on a pre-specified threshold. We discuss interpretation of the proposed estimand and related inference method. Simulation and real data experiments demonstrate that the proposed truncated LATE can be estimated more precisely than the standard LATE.

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Chao, John C., and Peter C. B. Phillips. "Uniform Inference in Panel Autoregression." Econometrics 7, no. 4 (November 26, 2019): 45. http://dx.doi.org/10.3390/econometrics7040045.

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This paper considers estimation and inference concerning the autoregressive coefficient ( ρ ) in a panel autoregression for which the degree of persistence in the time dimension is unknown. Our main objective is to construct confidence intervals for ρ that are asymptotically valid, having asymptotic coverage probability at least that of the nominal level uniformly over the parameter space. The starting point for our confidence procedure is the estimating equation of the Anderson–Hsiao (AH) IV procedure. It is well known that the AH IV estimation suffers from weak instrumentation when ρ is near unity. But it is not so well known that AH IV estimation is still consistent when ρ = 1 . In fact, the AH estimating equation is very well-centered and is an unbiased estimating equation in the sense of Durbin (1960), a feature that is especially useful in confidence interval construction. We show that a properly normalized statistic based on the AH estimating equation, which we call the M statistic, is uniformly convergent and can be inverted to obtain asymptotically valid interval estimates. To further improve the informativeness of our confidence procedure in the unit root and near unit root regions and to alleviate the problem that the AH procedure has greater variation in these regions, we use information from unit root pretesting to select among alternative confidence intervals. Two sequential tests are used to assess how close ρ is to unity, and different intervals are applied depending on whether the test results indicate ρ to be near or far away from unity. When ρ is relatively close to unity, our procedure activates intervals whose width shrinks to zero at a faster rate than that of the confidence interval based on the M statistic. Only when both of our unit root tests reject the null hypothesis does our procedure turn to the M statistic interval, whose width has the optimal N - 1 / 2 T - 1 / 2 rate of shrinkage when the underlying process is stable. Our asymptotic analysis shows this pretest-based confidence procedure to have coverage probability that is at least the nominal level in large samples uniformly over the parameter space. Simulations confirm that the proposed interval estimation methods perform well in finite samples and are easy to implement in practice. A supplement to the paper provides an extensive set of new results on the asymptotic behavior of panel IV estimators in weak instrument settings.

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Le, Thi, and Ariful Hoque. "Pricing European Currency Options with High-Frequency Data." Risks 10, no. 11 (November 2, 2022): 208. http://dx.doi.org/10.3390/risks10110208.

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Technological innovation has changed the financial market significantly with the increasing application of high-frequency data in research and practice. This study examines the performance of intraday implied volatility (IV) in estimating currency options prices. Options quotations at a different trading time, such as the opening period, midday period and closing period of a trading day with one-month, two months’ and three months’ maturity, are employed to compute intraday IV for pricing currency options. We use the Mincer–Zarnowitz regression test to analyse the volatility forecast power of IV for three different forecast horizons (within a week, one week and one month). Intraday IV’s capability in estimating currency options price is measured by the mean squared error, mean absolute error and mean absolute percentage error measure. The empirical findings show that intraday IV is the key to accurately forecasting volatility and estimating currency options prices precisely. Moreover, IV at the closing period of the beginning of the week contains crucial information for options price estimation. Furthermore, the shorter maturity intraday IV is suitable for pricing options for a shorter horizon. In comparison, the intraday IV based on the longer maturity options subsumes appropriate information to price options with higher accuracy for the longer horizon. Our paper proposes a new approach to accurately pricing currency options using high-frequency data.

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Deuchert, Eva, and Martin Huber. "A Cautionary Tale About Control Variables in IV Estimation." Oxford Bulletin of Economics and Statistics 79, no. 3 (March 22, 2017): 411–25. http://dx.doi.org/10.1111/obes.12177.

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Kuersteiner, Guido M. "EFFICIENT IV ESTIMATION FOR AUTOREGRESSIVE MODELS WITH CONDITIONAL HETEROSKEDASTICITY." Econometric Theory 18, no. 3 (May 15, 2002): 547–83. http://dx.doi.org/10.1017/s0266466602183010.

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This paper analyzes autoregressive time series models where the errors are assumed to be martingale difference sequences that satisfy an additional symmetry condition on their fourth-order moments. Under these conditions quasi maximum likelihood estimators of the autoregressive parameters are no longer efficient in the generalized method of moments (GMM) sense. The main result of the paper is the construction of efficient semiparametric instrumental variables estimators for the autoregressive parameters. The optimal instruments are linear functions of the innovation sequence.It is shown that a frequency domain approximation of the optimal instruments leads to an estimator that only depends on the data periodogram and an unknown linear filter. Semiparametric methods to estimate the optimal filter are proposed.The procedure is equivalent to GMM estimators where lagged observations are used as instruments. As a result of the additional symmetry assumption on the fourth moments the number of instruments is allowed to grow at the same rate as the sample. No lag truncation parameters are needed to implement the estimator, which makes it particularly appealing from an applied point of view.

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Blundell, Richard, Xiaohong Chen, and Dennis Kristensen. "Semi-Nonparametric IV Estimation of Shape-Invariant Engel Curves." Econometrica 75, no. 6 (November 2007): 1613–69. http://dx.doi.org/10.1111/j.1468-0262.2007.00808.x.

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Shin, Dong Wan, Hyo Jin Kim, and Jinwook Seo. "SUR Approach for IV Estimation of Canonical Contagion Models." Communications in Statistics - Simulation and Computation 45, no. 1 (June 9, 2014): 378–87. http://dx.doi.org/10.1080/03610918.2013.864764.

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35

Hong, Han, and Denis Nekipelov. "Efficient local IV estimation of an empirical auction model." Journal of Econometrics 168, no. 1 (May 2012): 60–69. http://dx.doi.org/10.1016/j.jeconom.2011.09.009.

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Grieser, William D., and Charles J. Hadlock. "Panel-Data Estimation in Finance: Testable Assumptions and Parameter (In)Consistency." Journal of Financial and Quantitative Analysis 54, no. 1 (September 14, 2018): 1–29. http://dx.doi.org/10.1017/s0022109018000996.

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We investigate the strict-exogeneity assumption, a necessary condition for estimator consistency in many finance panel-data applications. We outline tests for strict exogeneity in both traditional (non–instrumental variable (IV)) and IV settings. When we apply these tests in common traditional finance panel regressions, we find that the strict-exogeneity assumption is often strongly rejected, suggesting large inference errors. We test for strict exogeneity in specific finance panel-data IV settings and illustrate the potential for these tests to help confirm, or rule out, the validity of common panel-data IV estimators. We offer recommendations to address the strict-exogeneity issue in finance research.

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Andreyko, Sergey S., Dmitry A. Bobrov, Egor A. Nesterov, and Elena V. Lukyanets. "Estimation of Gas Bearing Capacity and Gas Dynamic Behaviour of Rocks of Salt and Shaly-Carbonate Rock Units in the Mine Field of the Mine of the Second Mining Department of Belaruskali JSC." Вестник Пермского национального исследовательского политехнического университета. Геология. Нефтегазовое и горное дело 20, no. 3 (August 2020): 270–79. http://dx.doi.org/10.15593/2712-8008/2020.3.7.

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The paper presents results of quantitative estimations of gas bearing capacity and gas dynamic behaviour of shaly-carbonate and salt rock units along the geologic profile of the Starobinsky potash field between the III and IV potash horizons. Underground experimental studies of the gas bearing capacity and gas dynamic behaviour of the shaly-carbonate and salt unit rocks were conducted in the slopes opening the IV potash horizons in the mine field of the mine of the Second Mining Department of Belaruskali, JSC. The underground experimental research procedure provided for quantitative estimation of the following indicators: nonassociated gas bearing capacity, initial gas emission rate and gas pressure of rocks in the 12th, 10th and 8th shalycarbonate units, as well as the 11th and 9th salt units. Based on the underground experimental research data, a quantitative estimation of the gas bearing capacity and gas dynamic behaviour of rocks in the 12th, 10th and 8th shaly-carbonate units as well as the 11th and 9th salt units was performed. Identified were the most gas-bearing beds of rocks in the shaly-carbonate and salt rock units, as well as the pattern of changes in the gas bearing capacity and gas dynamic behaviour of rocks in the geologic profile between the III and IV potash horizons.

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38

Kim, Sung-Geon, Eun-Ho Lee, Soon-Taeg Hwang, Kwangbai Park, Jeanyung Chey, Sang-Hwang Hong, and Ji-Hae Kim. "Estimation of K-WAIS-IV Premorbid Intelligence in South Korea: Development of the KPIE-IV." Clinical Neuropsychologist 29, sup1 (August 6, 2015): 19–29. http://dx.doi.org/10.1080/13854046.2015.1072248.

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Rogers, Paul, and Julie Stoner. "Assessment of a Modified Sandwich Estimator for Generalized Estimating Equations with Application to Opioid Poisoning in MIMIC-IV ICU Patients." Stats 4, no. 3 (August 12, 2021): 650–64. http://dx.doi.org/10.3390/stats4030039.

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Longitudinal data is encountered frequently in many healthcare research areas to include the critical care environment. Repeated measures from the same subject are expected to correlate with each other. Models with binary outcomes are commonly used in this setting. Regression models for correlated binary outcomes are frequently fit using generalized estimating equations (GEE). The Liang and Zeger sandwich estimator is often used in GEE to produce unbiased standard error estimation for regression coefficients in large sample settings, even when the covariance structure is misspecified. The sandwich estimator performs optimally in balanced designs when the number of participants is large with few repeated measurements. The sandwich estimator’s asymptotic properties do not hold in small sample and rare-event settings. Under these conditions, the sandwich estimator underestimates the variances and is biased downwards. Here, the performance of a modified sandwich estimator is compared to the traditional Liang-Zeger estimator and alternative forms proposed by authors Morel, Pan, and Mancl-DeRouen. Each estimator’s performance was assessed with 95% coverage probabilities for the regression coefficients using simulated data under various combinations of sample sizes and outcome prevalence values with independence and autoregressive correlation structures. This research was motivated by investigations involving rare-event outcomes in intensive care unit settings.

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Zlateva, Ivelina Yordanova, and Nikola Nikolov. "Two-step Procedure Based on the Least Squares and Instrumental Variable Methods for Simultaneous Estimation of von Bertalanffy Growth Parameters." International Journal of Agricultural and Environmental Information Systems 10, no. 2 (April 2019): 49–81. http://dx.doi.org/10.4018/ijaeis.2019040103.

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Advanced in the present article is a Two-step procedure designed on the methods of the least squares (LS) and instrumental variable (IV) techniques for simultaneous estimation of the three unknown parameters L∞, K and t0, which represent the individual growth of fish in the von Bertalanffy growth equation. For the purposes of the present analysis, specific MATLAB-based software has been developed through simulated data sets to test the operational workability of the proposed procedure and pinpoint areas of improvement. The resulting parameter estimates have been analyzed on the basis of consecutive comparison (the initial conditions being the same) between the results delivered by the two-step procedure for simultaneous estimation of L∞, K and t0 and the results obtained via the most commonly employed methods for estimating growth parameters; first, use has been made of the Gulland-and-Holt method for estimating the asymptotic length L∞and the curvature parameter K, followed by the von Bertalanffy method for estimation of t0.

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Park, Dongjoo, Soyoung You, Jeonghyun Rho, Hanseon Cho, and Kangdae Lee. "Investigating optimal aggregation interval sizes of loop detector data for freeway travel-time estimation and prediction." Canadian Journal of Civil Engineering 36, no. 4 (April 2009): 580–91. http://dx.doi.org/10.1139/l08-129.

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With recent increases in the deployment of intelligent transportation system (ITS) technologies, traffic management centers have the ability to obtain and archive large amounts of data regarding the traffic system. These data can then be employed in estimations of current conditions and the prediction of future conditions on the roadway network. In this paper, we propose a general solution methodology for the identification of the optimal aggregation interval sizes of loop detector data for four scenarios (i) link travel-time estimation, (ii) corridor / route travel-time estimation, (iii) link travel-time forecasting, and (iv) corridor / route travel-time forecasting. This study applied cross validated mean square error (CVMSE) model for the link and route travel-time estimations, and a forecasting mean square error (FMSE) model for the link and corridor / route travel-time forecasting. These models were applied to loop detector data obtained from the Kyeongbu expressway in Korea. It was found that the optimal aggregation sizes for the travel-time estimation and forecasting were 3 to 5 min and 10 to 20 min, respectively.

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Coenen, M., F. Rottensteiner, and C. Heipke. "RECOVERING THE 3D POSE AND SHAPE OF VEHICLES FROM STEREO IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2 (May 28, 2018): 73–80. http://dx.doi.org/10.5194/isprs-annals-iv-2-73-2018.

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The precise reconstruction and pose estimation of vehicles plays an important role, e.g. for autonomous driving. We tackle this problem on the basis of street level stereo images obtained from a moving vehicle. Starting from initial vehicle detections, we use a deformable vehicle shape prior learned from CAD vehicle data to fully reconstruct the vehicles in 3D and to recover their 3D pose and shape. To fit a deformable vehicle model to each detection by inferring the optimal parameters for pose and shape, we define an energy function leveraging reconstructed 3D data, image information, the vehicle model and derived scene knowledge. To minimise the energy function, we apply a robust model fitting procedure based on iterative Monte Carlo model particle sampling. We evaluate our approach using the object detection and orientation estimation benchmark of the KITTI dataset (Geiger et al., 2012). Our approach can deal with very coarse pose initialisations and we achieve encouraging results with up to 82&thinsp;% correct pose estimations. Moreover, we are able to deliver very precise orientation estimation results with an average absolute error smaller than 4&deg;.

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Jeong, Ki-Seok, Young-Seok Kim, Chong-Min Kim, and Oh-Min Kwon. "Statistical Life Estimation according to Dielectric Breakdown Test of Aged IV Wires." Transactions of The Korean Institute of Electrical Engineers 69, no. 4 (April 30, 2020): 623–28. http://dx.doi.org/10.5370/kiee.2020.69.4.623.

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Chen, Hsinyi, and Mau-Sun Hua. "Selecting Tetradic Short Forms of the Taiwan Wechsler Adult Intelligence Scale IV." Assessment 27, no. 7 (March 10, 2019): 1633–44. http://dx.doi.org/10.1177/1073191119831787.

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Factor-based Wechsler Adult Intelligence Scale–Fourth Edition (WAIS-IV) tetrads were investigated using the Taiwan WAIS-IV standardization sample of 1,105 normal adults aged between 16 and 90 years. Various psychometric characteristics, time constraints, and qualities of estimation were compared among 90 tetrads using linear equation procedures. Among the tetrads, the Information–Visual Puzzle–Digit Span–Digit Symbol combination had higher performance than the other combinations with respect to overall estimation quality and time saved. Moreover, the Similarities–Visual Puzzle–Digit Span–Digit Symbol, Information–Matrix Reasoning–Digit Span–Digit Symbol, and Information–Visual Puzzle–Letter Number Sequencing–Digit Symbol combinations obtained the most efficient estimates. For clinicians who value the utility of Block Design, the Information–Block Design–Digit Span–Digit Symbol combination was found to provide high estimation quality. The findings also revealed that the previously recommended Wechsler Adult Intelligence Scale–Third Edition tetrads are no longer the best solutions for the WAIS-IV. Furthermore, even the selected WAIS-IV tetrads had substantial misclassification rates; the four-factor short forms tend to underestimate the full-scaled IQ for highly intelligent adults. Therefore, these short forms should be used cautiously and for screening purposes only.

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Li, Jun, Kutluyil Dogancay, and Hatem Hmam. "Closed-Form Pseudolinear Estimators for DRSS-AOA Localization." Sensors 21, no. 21 (October 28, 2021): 7159. http://dx.doi.org/10.3390/s21217159.

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This paper investigates the hybrid source localization problem using differential received signal strength (DRSS) and angle of arrival (AOA) measurements. The main advantage of hybrid measurements is to improve the localization accuracy with respect to a single sensor modality. For sufficiently short wavelengths, AOA sensors can be constructed with size, weight, power and cost (SWAP-C) requirements in mind, making the proposed hybrid DRSS-AOA sensing feasible at a low cost. Firstly the maximum likelihood estimation solution is derived, which is computationally expensive and likely to become unstable for large noise levels. Then a novel closed-form pseudolinear estimation method is developed by incorporating the AOA measurements into a linearized form of DRSS equations. This method eliminates the nuisance parameter associated with linearized DRSS equations, hence improving the estimation performance. The estimation bias arising from the injection of measurement noise into the pseudolinear data matrix is examined. The method of instrumental variables is employed to reduce this bias. As the performance of the resulting weighted instrumental variable (WIV) estimator depends on the correlation between the IV matrix and data matrix, a selected-hybrid-measurement WIV (SHM-WIV) estimator is proposed to maintain a strong correlation. The superior bias and mean-squared error performance of the new SHM-WIV estimator is illustrated with simulation examples.

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Francis, J. A., W. Mazur, and H. K. D. H. Bhadeshia. "Estimation of Type IV Cracking Tendency in Power Plant Steels." ISIJ International 44, no. 11 (2004): 1966–68. http://dx.doi.org/10.2355/isijinternational.44.1966.

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Young, Peter C. "OPTIMAL IV IDENTIFICATION AND ESTIMATION OF CONTINUOUS-TIME TF MODELS." IFAC Proceedings Volumes 35, no. 1 (2002): 109–14. http://dx.doi.org/10.3182/20020721-6-es-1901.01004.

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Thomaseth, K., P. Young, and C. Cobelli. "Recursive-Iterative IV Estimation of Multiple-Input Transfer Function Models." IFAC Proceedings Volumes 18, no. 5 (July 1985): 1345–50. http://dx.doi.org/10.1016/s1474-6670(17)60751-1.

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49

Feig, E. "Sequence estimation for class-IV partial-response channels with jitter." IEEE Transactions on Communications 38, no. 8 (1990): 1181–89. http://dx.doi.org/10.1109/26.58751.

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50

Schwetz, B. "Summary of session iv: Prospects for improving human risk estimation." Reproductive Toxicology 11, no. 2-3 (June 1997): 461–63. http://dx.doi.org/10.1016/s0890-6238(96)00162-1.

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Journal articles on the topic 'IV estimation'

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