Autocorrelation and Instrumental Variables

This lecture covers the concepts of autocorrelation in error terms, focusing on first-order autocorrelation and its implications in econometrics. It discusses the consequences of autocorrelation, strategies for dealing with it, and tests such as the Breusch-Godfrey and Durbin-Watson tests. Additionally, it introduces the topic of instrumental variables, highlighting the importance of OLS assumptions and the impact of omitted variable bias. The lecture emphasizes the challenges posed by measurement error, omitted variable bias, and simultaneity in regression analysis, providing insights into how these issues can affect the consistency and validity of regression results.

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FIN-403: Econometrics

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

Autocorrelation

Autocorrelation, sometimes known as serial correlation in the discrete time case, is the correlation of a signal with a delayed copy of itself as a function of delay. Informally, it is the similarity between observations of a random variable as a function of the time lag between them. The analysis of autocorrelation is a mathematical tool for finding repeating patterns, such as the presence of a periodic signal obscured by noise, or identifying the missing fundamental frequency in a signal implied by its harmonic frequencies.

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.

Generalized least squares

In statistics, generalized least squares (GLS) is a method used to estimate the unknown parameters in a linear regression model when there is a certain degree of correlation between the residuals in the regression model. Least squares and weighted least squares may need to be more statistically efficient and prevent misleading inferences. GLS was first described by Alexander Aitken in 1935. In standard linear regression models one observes data on n statistical units.

Homoscedasticity and heteroscedasticity

In statistics, a sequence (or a vector) of random variables is homoscedastic (ˌhoʊmoʊskəˈdæstɪk) if all its random variables have the same finite variance; this is also known as homogeneity of variance. The complementary notion is called heteroscedasticity, also known as heterogeneity of variance. The spellings homoskedasticity and heteroskedasticity are also frequently used.

Ordinary least squares

In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values of the variable being observed) in the input dataset and the output of the (linear) function of the independent variable.

Instrumental Variables: Addressing Measurement Error and Reverse Causality FIN-403: Econometrics

Explores how instrumental variables correct biases from measurement error and reverse causality in regression models.

Linear Regression Basics FIN-403: Econometrics

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

Linear Regression Basics FIN-403: Econometrics

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Heteroskedasticity and Autocorrelation FIN-403: Econometrics

Explores heteroskedasticity and autocorrelation in econometrics, covering implications, applications, testing methods, and hypothesis testing consequences.

Heteroskedasticity: Ch. 4a FIN-403: Econometrics

Explores heteroskedasticity in econometrics, discussing its impact on standard errors, alternative estimators, testing methods, and implications for hypothesis testing.