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Journal articles on the topic 'Endogenous regressor'

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Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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1

Hu, Yingyao, and Ji-Liang Shiu. "NONPARAMETRIC IDENTIFICATION USING INSTRUMENTAL VARIABLES: SUFFICIENT CONDITIONS FOR COMPLETENESS." Econometric Theory 34, no. 3 (June 19, 2017): 659–93. http://dx.doi.org/10.1017/s0266466617000251.

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This paper provides sufficient conditions for the nonparametric identification of the regression function $m\left( \cdot \right)$ in a regression model with an endogenous regressor x and an instrumental variable z. It has been shown that the identification of the regression function from the conditional expectation of the dependent variable on the instrument relies on the completeness of the distribution of the endogenous regressor conditional on the instrument, i.e., $f\left( {x|z} \right)$. We show that (1) if the deviation of the conditional density $f\left( {x|{z_k}} \right)$ from a known complete sequence of functions is less than a sequence of values determined by the complete sequence in some distinct sequence $\left\{ {{z_k}:k = 1,2,3, \ldots } \right\}$ converging to ${z_0}$, then $f\left( {x|z} \right)$ itself is complete, and (2) if the conditional density $f\left( {x|z} \right)$ can form a linearly independent sequence $\{ f( \cdot |{z_k}):k = 1,2, \ldots \}$ in some distinct sequence $\left\{ {{z_k}:k = 1,2,3, \ldots } \right\}$ converging to ${z_0}$ and its relative deviation from a known complete sequence of functions under some norm is finite then $f\left( {x|z} \right)$ itself is complete. We use these general results to provide specific sufficient conditions for completeness in three different specifications of the relationship between the endogenous regressor x and the instrumental variable $z.$

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2

Romeu, Andrés, and Marcos Vera‐Hernández. "Counts with an endogenous binary regressor: A series expansion approach." Econometrics Journal 8, no. 1 (March 1, 2005): 1–22. http://dx.doi.org/10.1111/j.1368-423x.2005.00148.x.

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3

Choi, Jin-young, and Myoung-jae Lee. "Bounding endogenous regressor coefficients using moment inequalities and generalized instruments." Statistica Neerlandica 66, no. 2 (October 21, 2011): 161–82. http://dx.doi.org/10.1111/j.1467-9574.2011.00501.x.

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4

DiTraglia, Francis J., and Camilo García-Jimeno. "Identifying the effect of a mis-classified, binary, endogenous regressor." Journal of Econometrics 209, no. 2 (April 2019): 376–90. http://dx.doi.org/10.1016/j.jeconom.2019.01.007.

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5

Karayiannidis, Y., and Z. Doulgeri. "Regressor-free prescribed performance robot tracking." Robotica 31, no. 8 (May 29, 2013): 1229–38. http://dx.doi.org/10.1017/s0263574713000325.

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SUMMARYFast and robust tracking against unknown disturbances is required in many modern complex robotic structures and applications, for which knowledge of the full exact nonlinear system is unreasonable to assume. This paper proposes a regressor-free nonlinear controller of low complexity which ensures prescribed performance position error tracking subject to unknown endogenous and exogenous bounded dynamics assuming that joint position and velocity measurements are available. It is theoretically shown and demonstrated by a simulation study that the proposed controller can guarantee tracking of the desired joint position trajectory with a priori determined accuracy, overshoot and speed of response. Preliminary experimental results to a simplified system are promising for validating the controller to more complex structures.

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6

Lewbel, Arthur. "Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models." Journal of Business & Economic Statistics 30, no. 1 (January 2012): 67–80. http://dx.doi.org/10.1080/07350015.2012.643126.

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7

Kawaguchi, Daiji, Yukitoshi Matsushita, and Hisahiro Naito. "Moment Estimation of the Probit Model with an Endogenous Continuous Regressor." Japanese Economic Review 68, no. 1 (May 18, 2016): 48–62. http://dx.doi.org/10.1111/jere.12091.

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8

Ebbes, Peter, Michel Wedel, and Ulf Böckenholt. "Frugal IV alternatives to identify the parameter for an endogenous regressor." Journal of Applied Econometrics 24, no. 3 (March 3, 2009): 446–68. http://dx.doi.org/10.1002/jae.1058.

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9

O'Sullivan, Timothy, Robert Saddawi-Konefka, and Jack Bui. "IL-17D mediated cancer rejection (162.26)." Journal of Immunology 188, no. 1_Supplement (May 1, 2012): 162.26. http://dx.doi.org/10.4049/jimmunol.188.supp.162.26.

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Abstract Immune system interaction with cancers can either enhance or prevent tumor formation. Cytokine secretion by immune infiltrates and tumor cells are important in regulating this process. IL-17A, a pro-inflammatory member of the IL-17 family, has been shown to have conflicting results in regards to cancer progression or regression. Other members of the IL-17 family, such as IL-17D, have no known roles in immune responses to cancer, or endogenous function. Our lab possesses matched methylcholanthrene (MCA)-induced tumor cell lines that will continuously grow (Progressors) or reject (Regressors) after transplantation into syngeneic WT mice. The regresssor cell lines can grow in immune-deficient RAG2-/- mice, indicating that their rejection in WT mice requires an intact immune system. Our lab has used these cell lines to identify differential cytokine expression profiles from microarray analysis. We have obtained data that shows IL-17D transcript and protein are significantly overexpressed in regressor cell lines when compared to progressor cell lines. Overexpression of IL-7D in progressor tumor cell lines leads to their rejection in WT mice in a NK cell dependent manner. We hypothesize that IL-17D expression in regressor cell lines leads to immune-mediated tumor rejection by recruiting NK cells that polarize M1 macrophages in the tumor microenvironment.

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10

Choi, Jaerim, and Shu Shen. "Two-sample instrumental-variables regression with potentially weak instruments." Stata Journal: Promoting communications on statistics and Stata 19, no. 3 (September 2019): 581–97. http://dx.doi.org/10.1177/1536867x19874235.

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We develop a command, weaktsiv, for two-sample instrumentalvariables regression models with one endogenous regressor and potentially weak instruments. weaktsiv includes the classic two-sample two-stage least-squares estimator whose inference is valid only under the assumption of strong instruments. It also includes statistical tests and confidence sets with correct size and coverage probabilities even when the instruments are weak.

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11

Lewbel, Arthur. "Identification and estimation using heteroscedasticity without instruments: The binary endogenous regressor case." Economics Letters 165 (April 2018): 10–12. http://dx.doi.org/10.1016/j.econlet.2018.01.003.

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12

Dong, Yingying. "Endogenous regressor binary choice models without instruments, with an application to migration." Economics Letters 107, no. 1 (April 2010): 33–35. http://dx.doi.org/10.1016/j.econlet.2009.12.017.

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13

Zhang, Zhengyu, and Xiaobo He. "Estimation of a heteroscedastic binary choice model with an endogenous dummy regressor." Economics Letters 117, no. 3 (December 2012): 753–57. http://dx.doi.org/10.1016/j.econlet.2012.08.023.

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14

Goenner, Cullen F. "Simultaneity between Trade and Conflict: Endogenous Instruments of Mass Destruction." Conflict Management and Peace Science 28, no. 5 (November 2011): 459–77. http://dx.doi.org/10.1177/0738894211418414.

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According to the classical liberal belief, trade, which economically benefits countries, creates ties binding the interests of countries and reduces conflict. While the vast majority of the empirical literature supports this view, recent research questions these findings by also considering the reciprocal relationship between trade and conflict. If conflict also influences trade, then trade is an endogenous right-hand side regressor and previous estimates which ignore this are inconsistent. This article determines when one uses appropriate instruments for the endogenous regressors that trade reduces conflict and conflict reduces trade. Failure to use such instruments results in inconsistent estimates and can lead to the spurious conclusion that trade increases conflict. The lesson is the use of inappropriate instruments can be worse than not using them at all.

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15

Baum, Christopher F., and Arthur Lewbel. "Advice on using heteroskedasticity-based identification." Stata Journal: Promoting communications on statistics and Stata 19, no. 4 (December 2019): 757–67. http://dx.doi.org/10.1177/1536867x19893614.

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Lewbel (2012, Journal of Business and Economic Statistics 30: 67–80) provides a heteroskedasticity-based estimator for linear regression models containing an endogenous regressor when no external instruments or other such information is available. The estimator is implemented in the command ivreg2h by Baum and Schaffer (2012, Statistical Software Components S457555, Department of Economics, Boston College). In this article, we give advice and instructions to researchers who want to use this estimator.

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16

Davis, George C., and Sung-Yong Kim. "Measuring instrument relevance in the single endogenous regressor–multiple instrument case: a simplifying procedure." Economics Letters 74, no. 3 (February 2002): 321–25. http://dx.doi.org/10.1016/s0165-1765(01)00559-6.

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17

Mourifié, Ismael, and Romuald Méango. "A note on the identification in two equations probit model with dummy endogenous regressor." Economics Letters 125, no. 3 (December 2014): 360–63. http://dx.doi.org/10.1016/j.econlet.2014.10.006.

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18

D’Haultfoeuille, Xavier. "ON THE COMPLETENESS CONDITION IN NONPARAMETRIC INSTRUMENTAL PROBLEMS." Econometric Theory 27, no. 3 (September 24, 2010): 460–71. http://dx.doi.org/10.1017/s0266466610000368.

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The notion of completeness between two random elements has been considered recently to provide identification in nonparametric instrumental problems. This condition is quite abstract, however, and characterizations have been obtained only in special cases. This paper considers a nonparametric model between the two variables with an additive separability and a large support condition. In this framework, different versions of completeness are obtained, depending on which regularity conditions are imposed. This result allows one to establish identification in an instrumental nonparametric regression with limited endogenous regressor, a case where the control variate approach breaks down.

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19

Yildiz, Neşe. "ESTIMATION OF BINARY CHOICE MODELS WITH LINEAR INDEX AND DUMMY ENDOGENOUS VARIABLES." Econometric Theory 29, no. 2 (March 28, 2013): 354–92. http://dx.doi.org/10.1017/s0266466612000436.

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This paper presents computationally simple estimators for the index coefficients in a binary choice model with a binary endogenous regressor without relying on distributional assumptions or on large support conditions and yields root-n consistent and asymptotically normal estimators. We develop a multistep method for estimating the parameters in a triangular, linear index, threshold-crossing model with two equations. Such an econometric model might be used in testing for moral hazard while allowing for asymmetric information in insurance markets. In outlining this new estimation method two contributions are made. The first one is proposing a novel “matching” estimator for the coefficient on the binary endogenous variable in the outcome equation. Second, in order to establish the asymptotic properties of the proposed estimators for the coefficients of the exogenous regressors in the outcome equation, the results of Powell, Stock, and Stoker (1989, Econometrica 75, 1403–1430) are extended to cover the case where the average derivative estimation requires a first-step semiparametric procedure.

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20

Powell, James L. "Identification and Asymptotic Approximations: Three Examples of Progress in Econometric Theory." Journal of Economic Perspectives 31, no. 2 (May 1, 2017): 107–24. http://dx.doi.org/10.1257/jep.31.2.107.

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In empirical economics, the size and quality of datasets and computational power has grown substantially, along with the size and complexity of the econometric models and the population parameters of interest. With more and better data, it is natural to expect to be able to answer more subtle questions about population relationships, and to pay more attention to the consequences of misspecification of the model for the empirical conclusions. Much of the recent work in econometrics has emphasized two themes: The first is the fragility of statistical identification. The other, related theme involves the way economists make large-sample approximations to the distributions of estimators and test statistics. I will discuss how these issues of identification and alternative asymptotic approximations have been studied in three research areas: analysis of linear endogenous regressor models with many and/or weak instruments; nonparametric models with endogenous regressors; and estimation of partially identified parameters. These areas offer good examples of the progress that has been made in econometrics.

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21

Tan, Lili, and Yichong Zhang. "ROOT-N CONSISTENCY OF INTERCEPT ESTIMATORS IN A BINARY RESPONSE MODEL UNDER TAIL RESTRICTIONS." Econometric Theory 34, no. 6 (November 2, 2017): 1180–206. http://dx.doi.org/10.1017/s026646661700041x.

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The intercept of the binary response model is not regularly identified (i.e., $\sqrt n$ consistently estimable) when the support of both the special regressor V and the error term ε are the whole real line. The estimator of the intercept potentially has a slower than $\sqrt n$ convergence rate, which can result in a large estimation error in practice. This paper imposes additional tail restrictions which guarantee the regular identification of the intercept and thus the $\sqrt n$-consistency of its estimator. We then propose an estimator that achieves the $\sqrt n$ rate. Last, we extend our tail restrictions to a full-blown model with endogenous regressors.

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22

Banerjee, Souvik, and Anirban Basu. "Estimating Endogenous Treatment Effects Using Latent Factor Models with and without Instrumental Variables." Econometrics 9, no. 1 (March 17, 2021): 14. http://dx.doi.org/10.3390/econometrics9010014.

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We provide evidence on the least biased ways to identify causal effects in situations where there are multiple outcomes that all depend on the same endogenous regressor and a reasonable but potentially contaminated instrumental variable that is available. Simulations provide suggestive evidence on the complementarity of instrumental variable (IV) and latent factor methods and how this complementarity depends on the number of outcome variables and the degree of contamination in the IV. We apply the causal inference methods to assess the impact of mental illness on work absenteeism and disability, using the National Comorbidity Survey Replication.

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23

Kim, Kyoo il. "Sample selection models with a common dummy endogenous regressor in simultaneous equations: A simple two-step estimation." Economics Letters 91, no. 2 (May 2006): 280–86. http://dx.doi.org/10.1016/j.econlet.2005.12.003.

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24

Salim, Jana, and Hamparsum Bozdogan. "A Novel Approach to Forecasting High Dimensional S&P500 Portfolio Using VARX Model with Information Complexity." Journal of Economics and Technology Research 3, no. 2 (May 17, 2022): p1. http://dx.doi.org/10.22158/jetr.v3n2p1.

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This study considers vector autoregressive models that allow for endogenous and exogeneous regressors VARX using multivariate OLS regression. For the model selection, we follow bozdogan’s entropic or information-theoretic measure of complexity ICOMP criterion of the estimated inverse Fisher information matrix IFIM in choosing the best VARX lag parameter and we established that ICOMP outperform the conventional information criteria. As an empirical illustration, we reduced the dimension of the S&P500 multivariate time series using Sparse Principal Component Analysis (SPCA) and chose the best subset of 37 stocks belonging to six sectors. We then performed a portfolio of stocks based on the highest SPC loading weight matrix, plus the S&P500 index. Furthermore, we applied the proposed VARX model to predict the price movements in the constructed portfolio, where the S&P500 index was treated as an exogeneous regressor of the VARX model. It has been deduced too that the buy-sell decision making in response to VARX (4,0) for a stock outperforms investing and holding the stock over the out-of-sample period.

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25

Zhang, Zhengyu. "SEMIPARAMETRIC ESTIMATION OF PARTIALLY LINEAR TRANSFORMATION MODELS UNDER CONDITIONAL QUANTILE RESTRICTION." Econometric Theory 32, no. 2 (December 19, 2014): 458–97. http://dx.doi.org/10.1017/s0266466614000887.

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This article is concerned with semiparametric estimation of a partially linear transformation model under conditional quantile restriction with no parametric restriction imposed either on the link functional form or on the error term distribution. We describe for the finite-dimensional parameter a$\sqrt n$-consistent estimator which combines the features of Chen (2010)’s maximum integrated score estimator as well as Lee (2003)’s average quantile regression. We show the remaining two infinite-dimensional unknown functions in the model can be separately identified and propose estimators for these functions based on the marginal integration method. Furthermore, a simple approach is proposed to estimate the average partial quantile effect. Two important extensions, i.e., random censoring as well as estimating a transformation model with an endogenous regressor are also considered.

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26

Zimmer, David. "Using copulas to estimate the coefficient of a binary endogenous regressor in a Poisson regression: Application to the effect of insurance on doctor visits." Health Economics 27, no. 3 (October 18, 2017): 545–56. http://dx.doi.org/10.1002/hec.3605.

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27

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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28

Dunning, Thad. "Model Specification in Instrumental-Variables Regression." Political Analysis 16, no. 3 (2008): 290–302. http://dx.doi.org/10.1093/pan/mpm039.

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In many applications of instrumental-variables regression, researchers seek to defend the plausibility of a key assumption: the instrumental variable is independent of the error term in a linear regression model. Although fulfilling this exogeneity criterion is necessary for a valid application of the instrumental-variables approach, it is not sufficient. In the regression context, the identification of causal effects depends not just on the exogeneity of the instrument but also on the validity of the underlying model. In this article, I focus on one feature of such models: the assumption that variation in the endogenous regressor that is related to the instrumental variable has the same effect as variation that is unrelated to the instrument. In many applications, this assumption may be quite strong, but relaxing it can limit our ability to estimate parameters of interest. After discussing two substantive examples, I develop analytic results (simulations are reported elsewhere). I also present a specification test that may be useful for determining the relevance of these issues in a given application.

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29

Zhang, Zhengyu, and Xiaobo He. "Corrigendum to “Estimation of heteroscedastic binary choice model with an endogenous dummy regressor” [Econom. Lett. 117 (3) (2012) 753–757]." Economics Letters 119, no. 1 (April 2013): 67. http://dx.doi.org/10.1016/j.econlet.2013.01.020.

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30

Davis, George C. "Corrigendum to “Measuring instrument relevance in the single endogenous regressor-multiple instrument case: A simplifying procedure” [Economics Letters 74 (2002) 321–325]." Economics Letters 101, no. 2 (November 2008): 151. http://dx.doi.org/10.1016/j.econlet.2008.07.004.

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31

Wang, Qiying, and Peter C. B. Phillips. "NONPARAMETRIC COINTEGRATING REGRESSION WITH ENDOGENEITY AND LONG MEMORY." Econometric Theory 32, no. 2 (December 18, 2014): 359–401. http://dx.doi.org/10.1017/s0266466614000917.

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This paper explores nonparametric estimation, inference, and specification testing in a nonlinear cointegrating regression model where the structural equation errors are serially dependent and where the regressor is endogenous and may be driven by long memory innovations. Generalizing earlier results of Wang and Phillips (2009a,b,Econometric Theory25, 710–738,Econometrica77, 1901–1948), the conventional nonparametric local level kernel estimator is shown to be consistent and asymptotically (mixed) normal in these cases, thereby opening up inference by conventional nonparametric methods to a wide class of potentially nonlinear cointegrated relations. New results on the consistency of parametric estimates in nonlinear cointegrating regressions are provided, extending earlier research on parametric nonlinear regression and providing primitive conditions for parametric model testing. A model specification test is studied and confirmed to provide a valid mechanism for testing parametric specifications that is robust to endogeneity. But under long memory innovations the test is not pivotal, its convergence rate is parameter dependent, and its limit theory involves the local time of fractional Brownian motion. Simulation results show good performance for the nonparametric kernel estimates in cases of strong endogeneity and long memory, whereas the specification test is shown to be sensitive to the presence of long memory innovations, as predicted by asymptotic theory.

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32

Blundell, Richard, and James L. Powell. "Censored regression quantiles with endogenous regressors." Journal of Econometrics 141, no. 1 (November 2007): 65–83. http://dx.doi.org/10.1016/j.jeconom.2007.01.016.

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33

Hyder, Asma, and Ather Maqsood Ahmed. "The Dynamics of Moonlighting in Pakistan." Pakistan Development Review 48, no. 4II (December 1, 2009): 497–507. http://dx.doi.org/10.30541/v48i4iipp.497-507.

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The study explores the dynamics of moonlighting, demographics, human capital and association of occupations between primary and secondary job. The paper is based on cross-section data Labour Force Survey 2006-07 and limited to male wage workers residing in urban areas. Among two motives according to theoretical framework of moonlighting; first, constraint on hours worked in first job and second is wage rate is lower than the reservation wage in the primary occupation; within limited information available on different variables our results are skewed toward first motive and earnings from the primary occupation are insignificant in moonlighting decision. The model specification also attempts to correct the endogenous regressor in probit estimation. Among moonlighters ‘Professionals’ and ‘Technicians’ are holding their secondary job in same occupational category; apart from these two occupational categories managers and elementary occupations also seems popular for moonlighting. JEL classification: J22, J24 Keywords: Moonlighting, Labour Mobility, Occupational Association

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34

Caparrós Ruiz, Antonio. "Wage inequality of immigrants by type of contract in Spain." International Journal of Manpower 35, no. 6 (August 26, 2014): 817–33. http://dx.doi.org/10.1108/ijm-11-2012-0170.

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Purpose – The purpose of this paper is to approach a new aspect of the assimilation of immigrants in Spain. In particular, it is analyzed the effect of the type of contract on immigrant wages. The data used in this analysis come from the Spanish National Immigrant Survey, which was conducted by the Spanish Statistics Institute (INE) between 2006 and 2007. Design/methodology/approach – First, the methods and econometric specifications applied develop a wage model where the variable “contract type” is considered as an endogenous regressor. Second, the average wage gap between temporary and permanent workers is decomposed between a portion attributable to differences in characteristics and another to differences in coefficients. Findings – It is found that workers with a permanent contract received a wage premium with respect to temporary workers even for equal work and equal productivity. Social implications – Results indicate the importance of job stability for the integration and assimilation of immigrants in Spain, and offer an economic argument to support labour policies that encourage stable employment relationships. Originality/value – This paper takes a novel approach of the assimilation of immigrants in the Spanish labour market.

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35

Ozabaci, Deniz, Daniel J. Henderson, and Liangjun Su. "Additive Nonparametric Regression in the Presence of Endogenous Regressors." Journal of Business & Economic Statistics 32, no. 4 (October 2, 2014): 555–75. http://dx.doi.org/10.1080/07350015.2014.917590.

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36

Kripfganz, Sebastian, and Jan F. Kiviet. "kinkyreg: Instrument-free inference for linear regression models with endogenous regressors." Stata Journal: Promoting communications on statistics and Stata 21, no. 3 (September 2021): 772–813. http://dx.doi.org/10.1177/1536867x211045575.

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In models with endogenous regressors, a standard regression approach is to exploit just-identifying or overidentifying orthogonality conditions by using instrumental variables. In just-identified models, the identifying orthogonality assumptions cannot be tested without the imposition of other nontestable assumptions. While formal testing of overidentifying restrictions is possible, its interpretation still hinges on the validity of an initial set of untestable just-identifying orthogonality conditions. We present the kinkyreg command for kinky least-squares inference, which adopts an alternative approach to identification. By exploiting nonorthogonality conditions in the form of bounds on the admissible degree of endogeneity, feasible test procedures can be constructed that do not require instrumental variables. The kinky least-squares confidence bands can be more informative than confidence intervals obtained from instrumental-variables estimation, especially when the instruments are weak. Moreover, the approach facilitates a sensitivity analysis for standard instrumental-variables inference. In particular, it allows the user to assess the validity of previously untestable just-identifying exclusion restrictions. Further instrument-free tests include linear hypotheses, functional form, heteroskedasticity, and serial correlation tests.

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37

Kripfganz, Sebastian, and Jan F. Kiviet. "kinkyreg: Instrument-free inference for linear regression models with endogenous regressors." Stata Journal: Promoting communications on statistics and Stata 21, no. 3 (September 2021): 772–813. http://dx.doi.org/10.1177/1536867x211045575.

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In models with endogenous regressors, a standard regression approach is to exploit just-identifying or overidentifying orthogonality conditions by using instrumental variables. In just-identified models, the identifying orthogonality assumptions cannot be tested without the imposition of other nontestable assumptions. While formal testing of overidentifying restrictions is possible, its interpretation still hinges on the validity of an initial set of untestable just-identifying orthogonality conditions. We present the kinkyreg command for kinky least-squares inference, which adopts an alternative approach to identification. By exploiting nonorthogonality conditions in the form of bounds on the admissible degree of endogeneity, feasible test procedures can be constructed that do not require instrumental variables. The kinky least-squares confidence bands can be more informative than confidence intervals obtained from instrumental-variables estimation, especially when the instruments are weak. Moreover, the approach facilitates a sensitivity analysis for standard instrumental-variables inference. In particular, it allows the user to assess the validity of previously untestable just-identifying exclusion restrictions. Further instrument-free tests include linear hypotheses, functional form, heteroskedasticity, and serial correlation tests.

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38

Severini, Thomas A., and Gautam Tripathi. "Efficiency bounds for estimating linear functionals of nonparametric regression models with endogenous regressors." Journal of Econometrics 170, no. 2 (October 2012): 491–98. http://dx.doi.org/10.1016/j.jeconom.2012.05.018.

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39

Guo, Jing, Lei Wang, and Zhengyu Zhang. "Identification and estimation of a heteroskedastic censored regression model with random coefficient dummy endogenous regressors." Economic Modelling 110 (May 2022): 105799. http://dx.doi.org/10.1016/j.econmod.2022.105799.

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40

Kurozumi, Eiji. "Monitoring Parameter Constancy with Endogenous Regressors." Journal of Time Series Analysis 38, no. 5 (April 20, 2017): 791–805. http://dx.doi.org/10.1111/jtsa.12236.

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41

Jenish, Nazgul. "SPATIAL SEMIPARAMETRIC MODEL WITH ENDOGENOUS REGRESSORS." Econometric Theory 32, no. 3 (December 18, 2014): 714–39. http://dx.doi.org/10.1017/s0266466614000905.

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This paper proposes a semiparametric generalized method of moments estimator (GMM) estimator for a partially parametric spatial model with endogenous spatially dependent regressors. The finite-dimensional estimator is shown to be consistent and root-n asymptotically normal under some reasonable conditions. A spatial heteroscedasticity and autocorrelation consistent covariance estimator is constructed for the GMM estimator. The leading application is nonlinear spatial autoregressions, which arise in a wide range of strategic interaction models. To derive the asymptotic properties of the estimator, the paper also establishes a stochastic equicontinuity criterion and functional central limit theorem for near-epoch dependent random fields.

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Kutlu, Levent. "Battese-coelli estimator with endogenous regressors." Economics Letters 109, no. 2 (November 2010): 79–81. http://dx.doi.org/10.1016/j.econlet.2010.08.008.

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Kobayashi, Genya. "Bayesian Endogenous Tobit Quantile Regression." Bayesian Analysis 12, no. 1 (March 2017): 161–91. http://dx.doi.org/10.1214/16-ba996.

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Becker, Jan-Michael, Dorian Proksch, and Christian M. Ringle. "Revisiting Gaussian copulas to handle endogenous regressors." Journal of the Academy of Marketing Science 50, no. 1 (October 11, 2021): 46–66. http://dx.doi.org/10.1007/s11747-021-00805-y.

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AbstractMarketing researchers are increasingly taking advantage of the instrumental variable (IV)-free Gaussian copula approach. They use this method to identify and correct endogeneity when estimating regression models with non-experimental data. The Gaussian copula approach’s original presentation and performance demonstration via a series of simulation studies focused primarily on regression models without intercept. However, marketing and other disciplines’ researchers mainly use regression models with intercept. This research expands our knowledge of the Gaussian copula approach to regression models with intercept and to multilevel models. The results of our simulation studies reveal a fundamental bias and concerns about statistical power at smaller sample sizes and when the approach’s primary assumptions are not fully met. This key finding opposes the method’s potential advantages and raises concerns about its appropriate use in prior studies. As a remedy, we derive boundary conditions and guidelines that contribute to the Gaussian copula approach’s proper use. Thereby, this research contributes to ensuring the validity of results and conclusions of empirical research applying the Gaussian copula approach.

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Hong, Han, and Elie Tamer. "Inference in Censored Models with Endogenous Regressors." Econometrica 71, no. 3 (May 2003): 905–32. http://dx.doi.org/10.1111/1468-0262.00430.

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Kim, Chang-Jin. "Time-varying parameter models with endogenous regressors." Economics Letters 91, no. 1 (April 2006): 21–26. http://dx.doi.org/10.1016/j.econlet.2005.10.007.

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Terza, Joseph V. "Parametric Nonlinear Regression with Endogenous Switching." Econometric Reviews 28, no. 6 (July 2, 2009): 555–80. http://dx.doi.org/10.1080/07474930802473751.

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Ullah Khan, Safi. "How funding matters: Reinitiating of New Product Development and the moderating effect of extramural R&D." Journal of Entrepreneurship, Management and Innovation 18, no. 4 (2022): 185–219. http://dx.doi.org/10.7341/20221846.

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PURPOSE: We examine whether financial obstacles affect the probability of a firm undertaking previously suspended (or abandoned) innovation projects for new product development (NPD), and whether extramural R&D, as well as the breadth of the types of R&D collaboration (i.e., knowledge sources), moderate the relationship between financing constraints and the probability of restarting previously suspended NPD from selected South Asian economies. METHODOLOGY: This study controls for potential endogeneity in innovation propensity and finance access by employing a recursive bivariate probit model. We also adopt an instrumental variable approach by employing a probit model with continuous endogenous regressor to account for the potential endogeneity between the breadth of collaboration partners and innovation propensity. FINDINGS: Financial obstacles significantly impact previously suspended NPD. Furthermore, extramural R&D positively affects the probability of a firm undertaking NPD projects and attenuates the relationship between financing constraints and the likelihood of restarting abandoned/suspended NPD projects, suggesting that extramural R&D alleviates financing constraints, which increases the likelihood of NPD restarts. However, the breadth of collaborating partners is not positively associated with the probability of a firm restarting NPD. This is consistent with the view that extramural R&D with diverse sets of partners is exposed to the risks of the “two worlds paradox” arising from a firm’s collaboration with universities, research institutions, and consulting firms. IMPLICATIONS: The findings corroborate the view that firms must maintain a balance between their internal knowledge base and extramural R&D to optimize innovation outcomes. Nevertheless, extramural R&D reduces the reliance of financially constrained firms on resource requirements, improves access to financing, and enhances R&D productivity in NPD. ORIGINALITY AND VALUE: We provide the first firm-level and multi-country evidence of the importance of financial obstacles in the probability of reinitiating previously suspended NPD at the execution phase. Second, to the best of our knowledge, this is the first study to examine the relationship between inter-organizational R&D collaboration diversity and the probability of a firm reinitiating previously abandoned (or suspended) NPD.

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Bai, Jushan, and Serena Ng. "INSTRUMENTAL VARIABLE ESTIMATION IN A DATA RICH ENVIRONMENT." Econometric Theory 26, no. 6 (March 17, 2010): 1577–606. http://dx.doi.org/10.1017/s0266466609990727.

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We consider estimation of parameters in a regression model with endogenous regressors. The endogenous regressors along with a large number of other endogenous variables are driven by a small number of unobservable exogenous common factors. We show that the estimated common factors can be used as instrumental variables and they are more efficient than the observed variables in our framework. Whereas standard optimal generalized method of moments estimator using a large number of instruments is biased and can be inconsistent, the factor instrumental variable estimator (FIV) is shown to be consistent and asymptotically normal, even if the number of instruments exceeds the sample size. Furthermore, FIV remains consistent even if the observed variables are invalid instruments as long as the unobserved common components are valid instruments. We also consider estimating panel data models in which all regressors are endogenous but share exogenous common factors. We show that valid instruments can be constructed from the endogenous regressors. Although single equation FIV requires no bias correction, the faster convergence rate of the panel estimator is such that a bias correction is necessary to obtain a zero-centered normal distribution.

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Bhargava, Alok. "Identification and Panel Data Models with Endogenous Regressors." Review of Economic Studies 58, no. 1 (January 1991): 129. http://dx.doi.org/10.2307/2298050.

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