Lectures about OLS

Power of OLS and its Limitations

• Linear regression can be transformed to log-linear or log-log to obtain a normal distribution.

• Cross-sectional, time-series, and panel data are the three types of data.

• Measurement error in dependent variables does not bias the estimates, but estimates the relationship less precisely.

• Measurement error in independent variables always biases the coefficients towards zero.

• Missing observations lead to less precise estimates and omitted variables can lead to inconsistent results.

• Missing data can be missing completely at random (MCAR), missing not at random (MNAR), or missing at random (MAR).

• MCAR does not lead to bias in regression analysis, MNAR and MAR lead to bias in regression analysis.

• Data reduction, extra category, carry over value, imputation, multiple imputation, and weighting are the methods to deal with missing data.

• Listwise deletion reduces sample size and statistical power.

• Simple mean imputation is only possible for continuous variables.

• Regression mean imputation and multiple imputation are more sophisticated methods to deal with missing data.

• Weighting can be used to match the sample distribution of key variables with the known population distribution.Dealing with Data Issues in Statistical Analysis

• Creating weights for different variables can be difficult, especially when there are multiple key variables involved.

• None of the proposed methods for dealing with missing data is ideal, and most rely on strong assumptions.

• More complete data leads to better results and less bias, so it is important to report how missing data is dealt with.

• Data issues can include sampling errors, measurement errors, omitted variables, and non-response, among others.

• Bias can lead to consistency and efficiency issues in statistical analysis.

• Studying Schafer & Graham (2002) can provide valuable insights into missing data and its impact on analysis.

• Solutions to data issues should aim to ensure that the data still represents the population and maintain consistency and efficiency.

• It is important to make informed decisions on when to transform variables and when not to.

• Measurement errors should be described in detail, including their causes and consequences.

• The problems and impact of missing variables and values should be identified and addressed with appropriate solutions.

• The next lecture will cover computer lab sessions, and students should prepare for Mehmetoglu & Jakobsen's Chapter 6.

• Overall, dealing with data issues in statistical analysis requires careful consideration of multiple factors and a thorough understanding of the potential impact of missing data and other issues.

Power of OLS and its Limitations

• Linear regression can be transformed to log-linear or log-log to obtain a normal distribution.

• Cross-sectional, time-series, and panel data are the three types of data.

• Measurement error in dependent variables does not bias the estimates, but estimates the relationship less precisely.

• Measurement error in independent variables always biases the coefficients towards zero.

• Missing observations lead to less precise estimates and omitted variables can lead to inconsistent results.

• Missing data can be missing completely at random (MCAR), missing not at random (MNAR), or missing at random (MAR).

• MCAR does not lead to bias in regression analysis, MNAR and MAR lead to bias in regression analysis.

• Data reduction, extra category, carry over value, imputation, multiple imputation, and weighting are the methods to deal with missing data.

• Listwise deletion reduces sample size and statistical power.

• Simple mean imputation is only possible for continuous variables.

• Regression mean imputation and multiple imputation are more sophisticated methods to deal with missing data.

• Weighting can be used to match the sample distribution of key variables with the known population distribution.Dealing with Data Issues in Statistical Analysis

• Creating weights for different variables can be difficult, especially when there are multiple key variables involved.

• None of the proposed methods for dealing with missing data is ideal, and most rely on strong assumptions.

• More complete data leads to better results and less bias, so it is important to report how missing data is dealt with.

• Data issues can include sampling errors, measurement errors, omitted variables, and non-response, among others.

• Bias can lead to consistency and efficiency issues in statistical analysis.

• Studying Schafer & Graham (2002) can provide valuable insights into missing data and its impact on analysis.

• Solutions to data issues should aim to ensure that the data still represents the population and maintain consistency and efficiency.

• It is important to make informed decisions on when to transform variables and when not to.

• Measurement errors should be described in detail, including their causes and consequences.

• The problems and impact of missing variables and values should be identified and addressed with appropriate solutions.

• The next lecture will cover computer lab sessions, and students should prepare for Mehmetoglu & Jakobsen's Chapter 6.

• Overall, dealing with data issues in statistical analysis requires careful consideration of multiple factors and a thorough understanding of the potential impact of missing data and other issues.