Abstract
The treatment of an econometric model requires a clearly defined sequence of tasks. The identification of the model leads us to review the literature, to justify the defined relationship between the dependent variable and the independent variables. Model estimation uses the mathematical apparatus to find the equation of fit. Once the model has been estimated, it must be properly diagnosed using statistical tests. After the diagnosis phase, one can use the model to make predictions. This contribution deals with the identification, estimation, diagnosis, and prediction phases for the treatment of econometric models. Likewise, the diagnosis is deepened by developing the problems of autocorrelation, heteroscedasticity, residual normality, multicollinearity, endogeneity, and others.
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Mentorship of Prof. Marcin Paprzycki, Galgotias University, Greater Noida, India, is acknowledged and appreciated.
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Singh, P., Singh, S. (2023). Autocorrelation of an Econometric Model. In: Zhang, YD., Senjyu, T., So-In, C., Joshi, A. (eds) Smart Trends in Computing and Communications. Lecture Notes in Networks and Systems, vol 396. Springer, Singapore. https://doi.org/10.1007/978-981-16-9967-2_28
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DOI: https://doi.org/10.1007/978-981-16-9967-2_28
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