Embedded Files
Aubrey Poon - University of Kent
1. Principles of Forecasting: What is Forecasting, Data Generating Processes, Forecast Horizons
Lecture 1 - Principles of Forecasting.pdf2. Principles of Forecasting: Iterated & Direct Forecasts; Point, Interval & Density Forecasts;
Loss Function (MSE & MAE); Akaike & Bayesian Information Criterion;
Out of Sample Forecasting; Estimating AR(p) Processes
Lecture 1 - Principles of Forecasting.pdfMatlab Files
Basic.m - Ways to Transform a Matrix in Matlab
GrowthModel.m - Solow-Swan Growth Model
LinReg.m - How to Create a Simple Linear Regression Model
LinReg1.m - Linear Regression with Multiple Dependent Variables, Saving to Matrices, with 'for' loops and 'parfor' loops
London Data (xlsx) - Dataset [1520, 10]
Simulation.m - Generating Data (Standard Normal & Normal), Generating Artificial Data from an Auto-Regressive (1) Model & OLS Estimate
Lecture Recording
3. Ad Hoc Forecasting Methods:
Exponential Smoothing Methods (with Seasonality; Exponential Smoothing Forecasting Methods
Lecture 2 - Ad Hoc Forecasting Models.pdfMatlab Files
AdHocModels.m - Simple Exponential Smoothing Method; Holt-Winters Smoothing Method
AdHocModelsForecasting.m - Simple Exponential Smoothing Forecasting Method; Holt-Winters Smoothing Forecasting Method
EcomNSA.csv - Dataset [87, 2]
ForecastingSeasonDummies.m - Forecasting with Seasonal Dummies, AIC & BIC; Out-of-Sample Forecasting Exercise
Lecture Recording
4. Ad Hoc Forecasting Methods:
Seasonality with Trend Compenents;
Lecture 2 - Ad Hoc Forecasting Models.pdfMatlab Files
AdHocModels.m - Simple Exponential Smoothing Method; Holt-Winters Smoothing Method
AdHocModelsForecasting.m - Simple Exponential Smoothing Forecasting Method; Holt-Winters Smoothing Forecasting Method
EcomNSA.csv - Dataset [87, 2]
ForecastingSeasonDummies.m - Forecasting with Seasonal Dummies, AIC & BIC; Out-of-Sample Forecasting Exercise
Lecture Recording
5. Univariate Time-Series Models: ARMA Models; Covariance Stationarity; Auto-Regressive Models
Lecture 3 - Univariate Time-Series Models.pdf6. Univariate Time-Series Models: Wold Representation Theorem
Lecture 3 - Univariate Time-Series Models.pdf7. Maximum Likelihood Estimation
Lecture 3 - Maximum Likelihood Estimation.pdfMatlab Files
ARMA_MLE.m - ARMA Maximum Likelihood Estimation
loglike_ARMA.m - Function
loglike_MA1.m - Function
USData.xlsx - Data
Lecture Notes as Matlab File
Lecture Recording
8. ARCH & GARCH Models
Lecture 3 - ARCH and GARCH Models.pdfMatlab Files
ARCH.m - Auto-Regressive Conditional Heteroskedasticy Model
SP500.mat - Data
GARCH.m - Generalised Auto-Regressive Conditional Heteroskedasticity Model
ARIMA(1,0,0)GARCH.m - ARIMA (1,0,0) Auto-Regressive Conditional Heteroskedasticity Model on Inflation
GARCHForecasting.m - Forecasting with Generalised Auto-Regressive Heteroskedasticity Model on Inflation
USData.mat - Data
Quant Econ - Courses for Quantitative Economics in Python
Lecture Recording
9. A Guide to Writing Economic Research
A Guide for Economics Research.pdf10. Big Data and Machine Learning: Ridge, Lasso & Elastic Net
Lecture 4 - Big Data & Machine Learning Techniques.pdf11. Big Data and Machine Learning: Principal Component Analysis
Lecture 4 - Big Data & Machine Learning Techniques.pdf12. State Space Models: Unobserved Components
Lecture 5 - State Space and VAR Models.pdf13. Vector-Autoregression Models
Lecture 5 - State Space and VAR Models.pdf14. Introduction to Bayesian Econometrics
Lecture 6 - Introduction to Bayesian Econometrics (New).pdf15. Introduction to Bayesian Econometrics
Lecture 6 - Introduction to Bayesian Econometrics (New).pdfMatlab Files
SSexample.m - How to generate a generic state space model (with generated data) using precision based methods and the Kalman filter
Filterest.m (Functions for SSexample.m) - The Kalman filter and Carter Kohn Smoother
UCSV_Aubrey.m - Basic Stock & Watson 2007 paper
SVRW.m (Functions for UCSV_Aubrey.m) - seven mixture model with sampling of particular SV
Lecture Recording
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