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OLS CONFIDENCE INTERVALS IN NON-LINEAR MODELS: BOOTSTRAP APPROACH

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Linear models has been a powerful econometric tool used to show the relationship between two or more variables. Many studies also use linear approximation for nonlinear cases as it still might show valid results. OLS method requires the relationship of dependent and independent variables to be linear, although many studies employ OLS approximation even for nonlinear cases. In this study, we are introducing alternative method of intervals estimation, bootstrap, in linear regressions when the relationship is nonlinear. We compare the traditional and bootstrap confidence intervals when data has nonlinear relationship. As we need to know the true parameters, we carry out a simulation study. Our research findings indicate that when error term has non-normal shape, bootstrap interval will outperform the traditional method due to no distributional assumption and wider interval width.

AUTHORS

Z.Raximov

Toshkent Xalqaro Vestminster Universiteti

Tags

# accuracy# точность# аниқлик# нелинейная модель# nonlinear model# confidence interval# sample size# ишонч оралиғи# бутстрап# bootstrap# интервал ўлчами# размер выборки# размер интервала# interval size# OLS# variance.# : ОЛС# чизиқли бўлмаган модел# намуна ҳажми# юклаш чизиғи# дисперсия.# МНК# доверительный интервал

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Article Identifiers

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https://eroi.uz/11.0183/A0724-0020

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References

Freedman , D. A. (1981). Bootstrapping regression models. Annals of Statistics, 9, 1218 – 1228

Fox, J. (2016). An R companion to applied regression (3rd ed.). Sage Publications.

Davison , A. C. , and Hinkley , D. V. (1997). Bootstrap Methods and Their Applications. Cambridge University Press, Cambridge .

Efron , B., and Tibshirani , R. (1986). Bootstrap methods for standard errors, confidence intervals and other measures of statistical accuracy. Statistical Science. Vol. 1 , 54 – 77

Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1), 1-26.

Efron, B. (1982). The Jackknife, the Bootstrap and Other Resampling Plans. SIAM, Philadelphia

Efron, B., and Tibshirani, R. J. (1993). An introduction to the bootstrap. Chapman and Hall/CRC.

Efron, B., and Tibshirani, R. J. (1994). An introduction to the bootstrap (Vol. 57). Chapman and Hall/CRC.

Efron, B., and Tibshirani, R. J. (1998). The art of statistical learning (Vol. 1). Springer.

Faraway, J. J. (2006). Extending the linear model with R: Generalized linear, mixed effects and nonparametric regression models. Chapman and Hall/CRC.

Faraway, J. J. (2014). Linear models and extensions (Vol. 14). Springer.

Gamerman, J. A., and Lopes, H. F. (2006). Markov chain Monte Carlo: Stochastic simulation for Bayesian inference (2nd ed.). Chapman and Hall/CRC.

Harvey, D. I. (2013). Linear regression analysis for count data (2nd ed.). John Wiley and Sons.

Montgomery, D. C.,and Chatterjee, S. (2015). Design and analysis of experiments (8th ed.). John Wiley and Sons.

Pindyck, R. S., and Rubinfeld, D. L. (2013). Econometric models and economic forecasts (5th ed.). Pearson Education.

Wasserman, L. (2004). All of statistics: A concise course with applications. Springer.

Weisberg, S. (2014). Applied linear regression (4th ed.). John Wiley and Sons.

Wu, C. F. J. (2004). Asymptotic theory for nonlinear regression. Chapman and Hall/CRC