Economic data involve much uncertainty as the economy, per se, is difficult to control and subject to various shocks. Econometric modeling pertains to the application of statistical methods and models in economic and business phenomenon with the purpose of giving empirical content to economic theories. In order to explain the relationship between variables of interest in an economic system, statistical models are employed that enable the generation of forecasts, which, in turn, assist firms, consumers, government planners, and other decision makers in modifying likely outcomes. This course discusses various tools and techniques, especially the classic linear regression model. The statistical properties of the ordinary least squares (OLS) estimator that allow for the use of t-tests and F-tests are studied. Various model selection criteria, model diagnostics, and interpretations of outputs from statistical software are also discussed. Course methodology includes lectures, discussions of case applications and hands on exercises.

Course Aim

At the end of the course, participants should be able to: state the assumptions, limitations and proper use of fundamental econometric techniques, especially Multiple Regression; familiarize themselves with the use of a statistical software for econometric modeling; and generate explanations, predictions and forecasts using econometric models that improve policy and decision making process in their respective offices..

Course Participants

Tailored for technical staff involved in developing or using econometric models or preparing forecasts, economic research, policy analysis and other related functions. Background in basic statistical concepts and regression analysis is necessary.

Course Coverage

Introduction to Econometric Modeling
Review of Basic Statistical Concepts
Simple Linear Regression (SLR)
Simple Diagnostic Checking for SLR
Transformation of Variables in SLR
Analysis of Variance Approach to Regression Analysis
Multiple Linear Regression Analysis/Assumptions and Estimation
Test of Significance/Models with Dummy Variables
Diagnostic Checking/Residual Analysis
Multicollinearity/Model Selection
Nonlinear Models/Time Series Analysis
Bayesian and Modern (Nonparametric) Regression Approaches
Structural Equation Models
Case Study

Course Duration: 36 hours/5 days

Registration:

To register and further inquiry, please contact the Training Division at Telefax Nos. (632) 436-1426/9297543 or email it to japebenito@srtc.gov.ph or cemojica@srtc.gov.ph.

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