Survey Research Methods

Study Notes

What is Applied Econometrics?

Applied econometrics is the application of econometric methods to economic data to estimate economic models and test hypotheses.
It involves using statistical techniques to analyze economic data and draw conclusions about economic phenomena.

Types of Data in Applied Econometrics

Time Series Data: A set of observations recorded at regular time intervals, used to analyze patterns and trends over time.
Cross-Sectional Data: A set of observations collected at a single point in time, used to analyze differences between individuals, firms, or countries.
Panel Data: A combination of time series and cross-sectional data, used to analyze individual behavior over time.

Key Concepts in Applied Econometrics

Endogeneity: A problem that occurs when an independent variable is correlated with the error term, leading to biased estimates.
Exogeneity: A condition in which an independent variable is uncorrelated with the error term, ensuring unbiased estimates.
Omitted Variable Bias: A problem that occurs when a relevant independent variable is omitted from the model, leading to biased estimates.
Reverse Causality: A problem that occurs when the dependent variable affects the independent variable, leading to biased estimates.

Econometric Models in Applied Econometrics

Simple Linear Regression (SLR): A model that estimates the relationship between a dependent variable and a single independent variable.
Multiple Linear Regression (MLR): A model that estimates the relationship between a dependent variable and multiple independent variables.
Generalized Linear Models (GLM): A family of models that extends traditional linear regression to accommodate non-normal responses and non-linear relationships.
Vector Autoregression (VAR): A model that estimates the relationships between multiple time series variables.

Estimation and Inference in Applied Econometrics

Ordinary Least Squares (OLS): A method of estimating the parameters of a linear regression model by minimizing the sum of squared residuals.
Maximum Likelihood Estimation (MLE): A method of estimating the parameters of a model by maximizing the likelihood function.
Hypothesis Testing: A procedure for testing hypotheses about the population parameters based on sample data.
Confidence Intervals: A range of values within which the population parameter is likely to lie.

Common Applications of Applied Econometrics

Impact Evaluation: Estimating the causal effect of a policy or intervention on economic outcomes.
Forecasting: Predicting future economic outcomes based on past data.
Cost-Benefit Analysis: Evaluating the costs and benefits of a policy or project.
Risk Analysis: Assessing the risk associated with a particular investment or decision.

What is Applied Econometrics?

Applied econometrics is the application of econometric methods to economic data to estimate economic models and test hypotheses.
It involves using statistical techniques to analyze economic data and draw conclusions about economic phenomena.

Types of Data in Applied Econometrics

Time Series Data: A set of observations recorded at regular time intervals, used to analyze patterns and trends over time.
Cross-Sectional Data: A set of observations collected at a single point in time, used to analyze differences between individuals, firms, or countries.
Panel Data: A combination of time series and cross-sectional data, used to analyze individual behavior over time.

Key Concepts in Applied Econometrics

Endogeneity: A problem that occurs when an independent variable is correlated with the error term, leading to biased estimates.
Exogeneity: A condition in which an independent variable is uncorrelated with the error term, ensuring unbiased estimates.
Omitted Variable Bias: A problem that occurs when a relevant independent variable is omitted from the model, leading to biased estimates.
Reverse Causality: A problem that occurs when the dependent variable affects the independent variable, leading to biased estimates.

Econometric Models in Applied Econometrics

Simple Linear Regression (SLR): A model that estimates the relationship between a dependent variable and a single independent variable.
Multiple Linear Regression (MLR): A model that estimates the relationship between a dependent variable and multiple independent variables.
Generalized Linear Models (GLM): A family of models that extends traditional linear regression to accommodate non-normal responses and non-linear relationships.
Vector Autoregression (VAR): A model that estimates the relationships between multiple time series variables.

Estimation and Inference in Applied Econometrics

Ordinary Least Squares (OLS): A method of estimating the parameters of a linear regression model by minimizing the sum of squared residuals.
Maximum Likelihood Estimation (MLE): A method of estimating the parameters of a model by maximizing the likelihood function.
Hypothesis Testing: A procedure for testing hypotheses about the population parameters based on sample data.
Confidence Intervals: A range of values within which the population parameter is likely to lie.

Common Applications of Applied Econometrics

Impact Evaluation: Estimating the causal effect of a policy or intervention on economic outcomes.
Forecasting: Predicting future economic outcomes based on past data.
Cost-Benefit Analysis: Evaluating the costs and benefits of a policy or project.
Risk Analysis: Assessing the risk associated with a particular investment or decision.

Description

Applied econometrics involves using statistical techniques to analyze economic data and draw conclusions about economic phenomena. It involves using econometric methods to estimate economic models and test hypotheses.