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This course aims at presenting the main econometric methods, and their associated Stata commands, that can be used for the analysis of observational data which include surveys, firm data, or administrative data and can take the form of cross-section or panel data. This course relies on hands-on learning and offers a wide range of tools, which can be used in many research settings and topics, emphasizing applications and implementation procedures addressing real-world questions, econometric methods used in current empirical research.
Economic Models/Relationships/Expressions. Statistical Model. The Meaning Of Linearity. Data Types/Issues . Statistical Inference: Estimation. Inference: Hypothesis Testing. Confidence Intervals. The Multiple Regression Model. Dummy Variables. Examples of Model Variations. Box-Jenkins ARIMA Models. Seasonal ARIMA models. Unit roots. Cointegration. Granger causality. Vector Autoregression (VAR) models. Vector Error Correction (VEC) models.
Upon succesful completion of this course, a student will be able to
1. Demonstrate knowledge of, and a critical understanding of, the main concepts of time series analysis.
2. Utilize methods used to produce forecasts and how to interpret the results of econometric estimations.
3. Utilize Arch, Garch and other nonlinear time series models and their applications for modelling of financial data.
4. Utilize time series data well, and perform basic calculations and summaries of time series data.
5. Utilize and critically assess time series models fitted by computer packages and use a range of time series models to produce forecasts.
6. Communicate meaningfully and productively with others (including practitioners and professionals in the financial services industry) on time series analysis issues.
7. Be in a better position to critically evaluate your own work and the literature in management that uses regression analysis.
8. Write an applied econometrics project and present the results to the class
9. Gain a working knowledge of STATA.