The objective of this course is to understand the idea and principle of econometrics as an independent subject and basic quantitative analysis tool for empirical economic and financial studies; Assuming they have grasped basic classical econometric theories, students should be acquired a further understanding of extension of classical econometric theories in the model structure, estimation methods and data types. They would also learn how to use econometric theories and software to build econometric models, analyze empirically economic and financial phenomena in the real world and provide economic explanation for those phenomena.
This course mainly covers: (1) linear regression analysis, including parameter estimation and statistic inference in two-variable regression and multiple regression; (2) single equation econometric models where classical conditions are not satisfied, including the cases of multicollinearity, heteroskedasticity, autocorrelation and random independent variables; (3) the concept, identification, estimation and statistic inference of simultaneous equation system; (4) econometric models with non-classical structure, including non-linear single equation econometric models time-varying parameter linear econometric models and growth curve models; (5) co-integration and error correction model, introduction to time series econometric analysis, Granger causality analysis; (6) econometric models dealing with discrete data.
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