University of Manitoba - ECON 3040: Introduction to Econometrics (Solutions)

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.

ACC 1100  

ACC 2010  

ACC 2020