Abonazel, Mohamed R. (2015): How to Create a Monte Carlo Simulation Study using R: with Applications on Econometric Models.
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Abstract
In this workshop, we provide the main steps for making the Monte Carlo simulation study using R language. A Monte Carlo simulation is very common used in many statistical and econometric studies by many researchers. We will extend these researchers with the basic information about how to create their R-codes in an easy way. Moreover, this workshop provides some empirical examples in econometrics as applications. Finally, the simple guide for creating any simulation R-code has been produced.
Item Type: | MPRA Paper |
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Original Title: | How to Create a Monte Carlo Simulation Study using R: with Applications on Econometric Models |
English Title: | How to Create a Monte Carlo Simulation Study using R: with Applications on Econometric Models |
Language: | English |
Keywords: | Econometric Models; Monte Carlo simulation; R programming |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General C - Mathematical and Quantitative Methods > C5 - Econometric Modeling C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection |
Item ID: | 68708 |
Depositing User: | Dr. Mohamed R. Abonazel |
Date Deposited: | 09 Jan 2016 20:08 |
Last Modified: | 26 Sep 2019 08:47 |
References: | Abonazel, M. R. (2009). Some properties of random coefficients regression estimators. MSc thesis. Institute of Statistical Studies and Research. Cairo University. Abonazel, M. R. (2014a). Some estimation methods for dynamic panel data models. PhD thesis. Institute of Statistical Studies and Research. Cairo University. Abonazel, M. R. (2014b). Statistical analysis using R, Annual Conference on Statistics, Computer Sciences and Operations Research, Vol. 49. Institute of Statistical Studies and research, Cairo University. DOI: 10.13140/2.1.1427.2326. Barreto, H., Howland, F. (2005). Introductory econometrics: using Monte Carlo simulation with Microsoft excel. Cambridge University Press. Craft, R. K. (2003). Using spreadsheets to conduct Monte Carlo experiments for teaching introductory econometrics. Southern Economic Journal, 726-735. Crawley, M. J. (2012). The R book. John Wiley & Sons. Gentle, J. E. (2003). Random number generation and Monte Carlo methods. Springer Science & Business Media. Gujarati, D. N. (2003) Basic econometrics. 4th ed. McGraw-Hill Education. Gentle, J. E., Härdle, W. K., Mori, Y. (2012). Handbook of computational statistics: concepts and methods. Springer Science & Business Media. Mooney, C. Z. (1997). Monte Carlo simulation. Sage University Paper Series on Quantitative Applications in the Social Sciences, series no. 07-116. Thousand Oaks, CA: Sage. Mousa, A., Youssef, A. H., Abonazel, M. R. (2011). A Monte Carlo study for Swamy’s estimate of random coefficient panel data model. Working paper, No. 49768. University Library of Munich, Germany. Robert, C., Casella, G. (2009). Introducing Monte Carlo Methods with R. Springer Science & Business Media. Robert, C., Casella, G. (2013). Monte Carlo statistical methods. Springer Science & Business Media. Thomopoulos, N. T. (2012). Essentials of Monte Carlo Simulation: Statistical Methods for Building Simulation Models. Springer Science & Business Media. Youssef, A. H., Abonazel, M. R. (2009). A comparative study for estimation parameters in panel data model. Working paper, No. 49713. University Library of Munich, Germany. Youssef, A. H., Abonazel, M. R. (2015). Alternative GMM estimators for first-order autoregressive panel model: an improving efficiency approach. Communications in Statistics-Simulation and Computation (in press). DOI: 10.1080/03610918.2015.1073307. Youssef, A. H., El-sheikh, A. A., Abonazel, M. R. (2014). New GMM estimators for dynamic panel data models. International Journal of Innovative Research in Science, Engineering and Technology 3:16414–16425. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/68708 |