ECON 3040: Introduction to Econometrics is a 3-credit course that provides a basic introduction to econometric theory and its application. Much of the emphasis of the course is on the linear multiple regression model, under standard assumptions. It begins with a review of probability and statistics, followed by ordinary least squares (OLS).

Main topics to be covered:

• Ordinary least squares (OLS) is a linear regression technique that estimates the parameters of a linear regression model by minimizing the sum of the squared differences between the observed and predicted values. In this course, OLS serves as a foundation for understanding linear regression models and their applications.

• Linear Regression with One Regressor delves into the relationship between a dependent variable (Y) and a single independent variable (X). The linear regression model with one regressor is given by Y = β0 + βX + ε, where β0 represents the constant intercept, β is the slope, and ε is the error term.

• Hypothesis Tests are a form of statistical inference that uses sample data to draw conclusions about a population parameter or a population probability distribution. The process involves formulating a null hypothesis (H0) and an alternative hypothesis (Ha), then using statistical tests to determine whether H0 can be rejected.

• Linear Regression with Multiple Regressors expands the linear regression model to include multiple independent variables. Multiple linear regression (MLR) is used to determine a mathematical relationship among several random variables, examining how multiple independent variables are related to one dependent variable.

• Hypothesis Tests in Multiple Regression covers hypothesis testing in the context of multiple regression, which involves testing the significance of individual regression coefficients and the overall model fit.

• Nonlinear Regression Functions explores regression models with nonlinear relationships between the dependent and independent variables.

• Instrumental Variables Regression covers the use of instrumental variables in regression analysis to address issues of endogeneity and omitted variable bias.

• Heteroscedasticity addresses the issue of unequal variances in the error terms of a regression model and its implications for econometric analysis.

• Autocorrelation deals with the correlation between the error terms in a time series regression model, which can affect the efficiency of the OLS estimator.

COURSE CODE

COURSE NAME

ACC 1100  

Introductory Financial Accounting

ACC 2010  

Intermediate Accounting – Assets

ACC 2020  

Intermediate Accounting – Equities

ACC 3040 

Cost Accounting

ACC 4030

Accounting Theory

ECON 1010 

Introduction to Microeconomic Principles

ECON 1020

Introduction to Macroeconomic Principles

ECON 2010

Microeconomic Theory 1

ECON 2040

Quantitative Methods in Economics

ECON 3040  

Introduction to Econometrics

ECON 4490  

Macroeconometrics for Policy Analysis

FIN 2200  

Corporate Finance

FIN 3410  

Investments

FIN 3460  

Financial Markets and Institutions

FIN 3480  

Corporate Finance Theory and Practice

FIN 4270  

Options and Futures

GMGT 2070  

Organizational Behaviour

LAW1102  

Contracts

MKT 2210  

Fundamentals of Marketing

STAT 1000  

Basic Statistical Analysis 1

STAT 3490  

Time Series Analysis