Carbajal De Nova, Carolina (2014): Synthetic data: an endogeneity simulation.

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Abstract
This paper uses synthetic data and different scenarios to test treatments for endogeneity problems under different parameter settings. The model uses initial conditions and provides the solution for a hypothetical equation system with an embedded endogeneity problem. The behavioral and statistical assumptions are underlined as they are used through this research. A methodology is proposed for constructing and computing simulation scenarios. The econometric modeling of the scenarios is developed accordingly with the feedback obtained from previous scenarios. The inputs for these scenarios are synthetic data, which are constructed using random number machines and/or Monte Carlo simulations. The outputs of the scenarios are the model estimators. The research results demonstrated that a treatment for endogeneity can be developed as the sample size increases.
Item Type:  MPRA Paper 

Original Title:  Synthetic data: an endogeneity simulation 
English Title:  Synthetic data: an endogeneity simulation 
Language:  English 
Keywords:  synthetic data, endogeneity problems, scenarios, Monte Carlo simulations. 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C55  Large Data Sets: Modeling and Analysis C  Mathematical and Quantitative Methods > C8  Data Collection and Data Estimation Methodology ; Computer Programs > C87  Econometric Software 
Item ID:  79067 
Depositing User:  Carolina Carbajal De Nova 
Date Deposited:  12 May 2017 05:59 
Last Modified:  12 May 2017 06:00 
References:  1. Alvarez, J., and M. Arellano. 1998. “The Time Series and CrossSection Asymptotics of Dynamic Panel Data Estimators.” Working Paper no. 9808 CEMFI, pp. 164. 2. Anderson, T. W., and C. Hsiao. 1981. “Estimation of Dynamic Models with Error Components.” Journal of the American Statistical Association 76(375), pp. 598606. 3. Carbajal, C. 2013. “How to use Matlab in a Statistical Application.” Unpublished mimeo. 4. Casella, G. and R. L. Berger. 1990. Statistical Inference, Belmont: Duxbury Press. 5. Greene, W. H. 2012. Econometric Analysis, New Jersey: Pearson Education, Inc. 6. Hamilton, J. D. 1994. Time Series Analysis, New Jersey: Princeton University Press. 7. Hayashi, Fumio. 2000. Econometrics, New Jersey: Princeton University Press. 8. Hart, P. E., G. Mills and J. K. Whitaker. 1964. Econometric Analysis for National Economic Planning, London: Butterworths. 9. Hogg, R. V., J. W. McKean and A. T. Craig. 2013. Introduction to Mathematical Statistics, New Jersey: Pearson Education, Inc. 10. Kantz, H. and T. Schereiber. 2003. Nonlinear Time Series Analysis, Cambridge: Cambridge University Press. 11. Kuh, E., J. W. Neese and P. Hollinger. 1985. Structural Sensitivity in Econometric Models, New York: John Wiley and Sons. 12. Spanos, A. 2011. “Foundational Issues in Statistical Modeling: Statistical Model Specification and Validation.” Rationality, Markets and Morals 2, pp. 146178. 13. Swamy, P.A.V.B.; Mehta, J.S; Chang, I.L. Endogeneity, TimeVarying Coefficients, and Incorrect vs. Correct Ways of Specifying the Error Terms of Econometric Models. Econometrics MDPI. 2017, 5, 8. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/79067 