Data Analysis and
Economic Forecasting
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Syllabus |
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Note - lecture notes do not contain the applications of the concepts to Excel. |
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Lecture 1: |
Derivation of the Two-Variable Linear Regression and Properties of Estimators |
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Lecture 2: |
Review of Matrices and Derivation of Multiple Linear Regressions and its Properties |
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Lecture 3: |
Review of the z, t, and F distributions and Derivation of the ANOVA |
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Lecture 4: |
Inference and confidence intervals |
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Lecture 5: |
Goodness of Fit Measures such as R2, adjusted R2, Akaike Information Criterion (AIC), and Schwarz Information
Criterion (SIC) |
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Lecture 6: |
Violations of Multiple Regression, such as autocorrelation, multicollinearity and heteroscedasticity. Includes tests and corrections |
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Midterm Examination |
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Lecture 8: |
Forecasting using trend analysis, such as linear trend, quadratic trend, exponential growth, and moving average; Measuring prediction error with Mean Squared Errors (MSE) and Root Mean Squared Errors, and Composite Forecasting |
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Lecture 9: |
Time series data; trends, seasonality, weakly stationary, and random walk process |
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Lecture 10: |
Autoregressive process (AR) and moving average process (MA) |
| Lecture 11: |
Review of the Pearson correlation and covariance. Then introduce the Autocorrelation function (ACF) and partial autocorrelation function (PACF) and their plots. Then tie the ACF and PACF to the AR and MA processes. |
| Lecture 12: |
Introduction to the ARMA(p, q) process and their graphs and forecasting |
| Lecture 13: |
Introduction to the ARIMA(p,q) process and their graphs and forecasting |
| Lecture 14: |
Introduction to Vector Auto Regressions (VARs) |
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Final Examination |
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Last updated January 24, 2012
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