Lecture

Introduction to OLS

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In course

FIN-403: Econometrics

The course covers basic econometric models and methods that are routinely applied to obtain inference results in economic and financial applications. ...

Description

This lecture introduces the Linear Regression Model and Ordinary Least Squares (OLS) as the basic tool of econometrics, emphasizing its versatility and potential risks when misused. It covers the linear relationship between dependent and independent variables, different variable types, and the algebraic tools for determining coefficients. The lecture explains the graphical illustration with a single regressor, manual calculation of regression coefficients, goodness-of-fit measures like R-squared, and the interpretation of regression results. It concludes with a practical example using Stata to calculate regression coefficients and validate results, providing a comprehensive overview of OLS fundamentals.

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Instructor

Andreas Fuster

Andreas Fuster is an Associate Professor of Finance at Swiss Finance Institute @ EPFL and a Research Fellow at the CEPR. Previously, he worked at the Federal Reserve Bank of New York and the Swiss National Bank. Andreas's main research interests are in empirical finance, macroeconomics, and behavioral economics. His recent work has focused in particular on the effects of technological advances on household credit markets. Andreas’s research has been published in academic journals such as the Quarterly Journal of Economics, the Review of Economic Studies, the Journal of Finance, and the Review of Financial Studies. Andreas obtained his Ph.D. from Harvard University and also holds an M.Phil. from Oxford University and a B.A. from the University of Lausanne (Switzerland), all in economics.

Official source

Ontological neighbourhood

Statistics

Data analysis: Regression analysis

Related concepts (32)

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

Segmented regression

Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable is partitioned into intervals and a separate line segment is fit to each interval. Segmented regression analysis can also be performed on multivariate data by partitioning the various independent variables. Segmented regression is useful when the independent variables, clustered into different groups, exhibit different relationships between the variables in these regions.

Regression analysis

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features'). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion.

Nonlinear regression

In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations. In nonlinear regression, a statistical model of the form, relates a vector of independent variables, , and its associated observed dependent variables, . The function is nonlinear in the components of the vector of parameters , but otherwise arbitrary.

Logistic regression

In statistics, the logistic model (or logit model) is a statistical model that models the probability of an event taking place by having the log-odds for the event be a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients in the linear combination).

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