Data Analysis and Economic Forecasting 

Syllabus  
Note  lecture notes do not contain the applications of the concepts to Excel. 

Lecture 1:  Derivation of the TwoVariable Linear Regression and Properties of Estimators 
Lecture 2:  Review of Matrices and Derivation of Multiple Linear Regressions and its Properties 
Lecture 3:  Review of the z, t, and F distributions and Derivation of the ANOVA 
Lecture 4:  Inference and confidence intervals 
Lecture 5:  Goodness of Fit Measures such as R^{2}, adjusted R^{2}, Akaike Information Criterion (AIC), and Schwarz Information Criterion (SIC) 
Lecture 6:  Violations of Multiple Regression, such as autocorrelation, multicollinearity and heteroscedasticity. Includes tests and corrections 
Midterm Examination  
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 
Lecture 9:  Time series data; trends, seasonality, weakly stationary, and random walk process 
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) 
Final Examination  

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