Podcast
Questions and Answers
What is the primary focus of the Understanding Society dataset?
What is the primary focus of the Understanding Society dataset?
Which wave of the Understanding Society data is currently being used?
Which wave of the Understanding Society data is currently being used?
What is one characteristic of the teaching dataset used for Understanding Society?
What is one characteristic of the teaching dataset used for Understanding Society?
Where can the Understanding Society Teaching Dataset be accessed?
Where can the Understanding Society Teaching Dataset be accessed?
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What index code is associated with the Understanding Society Teaching Dataset?
What index code is associated with the Understanding Society Teaching Dataset?
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What is the purpose of the Rubin causal model in regression analysis?
What is the purpose of the Rubin causal model in regression analysis?
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Which method is used to assess the stationarity of time series data?
Which method is used to assess the stationarity of time series data?
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What is the primary focus of Part II of the module?
What is the primary focus of Part II of the module?
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Which of the following tools is emphasized in the first part of the module?
Which of the following tools is emphasized in the first part of the module?
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What percentage of the assessment is dedicated to the online quiz via QMplus?
What percentage of the assessment is dedicated to the online quiz via QMplus?
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What does the Box-Jenkins methodology primarily aid in?
What does the Box-Jenkins methodology primarily aid in?
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Which of the following concepts is not part of Part III of the module?
Which of the following concepts is not part of Part III of the module?
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What aspect of coding may be examined in the module?
What aspect of coding may be examined in the module?
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Which statement best describes R?
Which statement best describes R?
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What notable characteristic does R have that enhances its functionality?
What notable characteristic does R have that enhances its functionality?
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What is a significant aspect of the R programming language?
What is a significant aspect of the R programming language?
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What resources are available for students needing feedback and support in the module?
What resources are available for students needing feedback and support in the module?
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Who should students contact for additional support regarding the module?
Who should students contact for additional support regarding the module?
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In which department was R originally developed?
In which department was R originally developed?
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What is the implication of R being open-source?
What is the implication of R being open-source?
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What is a common challenge faced by users when learning to use R?
What is a common challenge faced by users when learning to use R?
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What is one of the main advantages of using R as a programming language?
What is one of the main advantages of using R as a programming language?
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What is a potential downside of user-contributed packages in R?
What is a potential downside of user-contributed packages in R?
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Which R package is specifically designed for data manipulation and visualization?
Which R package is specifically designed for data manipulation and visualization?
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What is needed before you can install additional packages in R?
What is needed before you can install additional packages in R?
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What describes the package 'fixest' in R?
What describes the package 'fixest' in R?
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What command is used in R to install additional packages?
What command is used in R to install additional packages?
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Why is it suggested to install the full R suite on your device?
Why is it suggested to install the full R suite on your device?
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Which of the following is NOT true about R?
Which of the following is NOT true about R?
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What does Ti represent in the given context?
What does Ti represent in the given context?
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What is the fundamental problem described in the context?
What is the fundamental problem described in the context?
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What does ATE stand for in the context of treatment effects?
What does ATE stand for in the context of treatment effects?
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Which average treatment effect quantifies the impact specifically for those who did not receive treatment?
Which average treatment effect quantifies the impact specifically for those who did not receive treatment?
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The formula Yiobs = Yi(1) · Ti + Yi(0) · (1 − Ti) is used to depict what?
The formula Yiobs = Yi(1) · Ti + Yi(0) · (1 − Ti) is used to depict what?
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What does the local average treatment effect (LATE) focus on?
What does the local average treatment effect (LATE) focus on?
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What assumption is crucial for the interpretation of treatment effects?
What assumption is crucial for the interpretation of treatment effects?
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E(Yi(1) - Yi(0)|Ti = 1) represents which specific quantity?
E(Yi(1) - Yi(0)|Ti = 1) represents which specific quantity?
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What does D represent in the context of selection bias?
What does D represent in the context of selection bias?
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What is indicated by the term 'selection bias'?
What is indicated by the term 'selection bias'?
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Why is D not suitable for policy evaluations?
Why is D not suitable for policy evaluations?
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What effect can health insurance have on individuals according to the described scenario?
What effect can health insurance have on individuals according to the described scenario?
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What does the expression E(Yi(0)|Ti=1) represent?
What does the expression E(Yi(0)|Ti=1) represent?
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How does selection bias complicate comparisons between treated and untreated individuals?
How does selection bias complicate comparisons between treated and untreated individuals?
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What ideal condition is mentioned for measuring the effect of treatment?
What ideal condition is mentioned for measuring the effect of treatment?
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Which of the following represents an observed quantity in selection bias calculations?
Which of the following represents an observed quantity in selection bias calculations?
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Study Notes
ECOM216 - Applied Econometrics with R
- This module covers applied econometric methods.
- Students are equipped with essential skills to analyze data, make informed decisions, and prepare for advanced studies in econometrics and finance.
- Hands-on experience using econometric tools in R is emphasized to solve real-world problems.
- The module introduces key applied econometric methods (micro and macro).
- It covers methods for identifying causal impacts of policies, including randomized controlled trials, causal regressions, and differences-in-differences.
- Time series modeling, prediction, and the interpretation of econometric models are also included.
- The module utilizes R for practical applications, emphasizing implementation skills instead of just theoretical understanding.
- This practical learning approach involves experimentation with methods and coding within R.
- Prior R knowledge is not assumed, and learning requires active participation and effort.
- All lectures have associated code books.
- Reproducible examples and real-world data are used for better understanding.
- Lectures focus on theory and methods with live coding examples.
- Tutorials focus on coding aspects, including clarifying and expanding lecture material and introducing new tools.
Part I - General Concepts
- This section covers fundamental concepts, spanning two lectures.
- It introduces R, core R functions, the tidyverse library, and essential data manipulation techniques.
- Data loading, manipulation, and regression analysis are explored, along with interpretations from models such as OLS.
- Key properties of Ordinary Least Squares (OLS) regression and concepts of exogeneity and causality are covered.
Part II - Causality
- This section, comprising four lectures, focuses on causal interpretations of econometric models.
- It explains how to establish causal effects from regressions.
- The Rubin causal model, randomization ideals, and implementation using regression models are discussed.
- Control regressions and conditional models are highlighted.
- The module emphasizes moving beyond simple controls to methods like fixed effects and matching for more rigorous causal inference.
- It covers differences-in-differences, staggered treatments, and continuous treatment variables.
Part III - Forecasting & Time Series Models
- This part covers forecasting and time series models, spanning four lectures.
- It includes introductory concepts in time series, stationarity, autocorrelation functions, and partial autocorrelation functions.
- The module delves into autoregressive (AR) and moving average (MA) processes.
- Discusses Dickey-Fuller unit root tests for assessing stationarity.
- It explores Autoregressive Integrated Moving Average (ARIMA) models and seasonal terms.
- The module provides a practical approach with external variables, with a focus on model selection using Box-Jenkins methodology.
Assessment
- Assessment is 80% exam and 20% online quiz through QMplus.
- The online quiz timing is either in week 8 or week 9, with the exact date announced by the office.
- Coding is examinable, with reasoned constraints.
- Questions may involve interpreting code, outcomes, and next steps, and suggesting strategies for particular problems or questions.
- Live coding will not be assessed.
Feedback and Support
- Office hours, typically held on Tuesdays at 4 p.m., with additional sessions available upon request.
- Support sessions occur via Zoom and require prior booking.
- Additional timeslots may be scheduled to accommodate busy periods, and online feedback is welcomed.
What is R?
- R is a free software environment for statistical computing and graphics.
- It is open-source, user-supplied, with a strong and active community.
- There are over 20,000 packages, some with their own unique syntaxes.
Understanding Society
- This dataset is a nationally representative, longitudinal survey of individuals.
- Data are collected over many years, including demographic, household, well-being, and life satisfaction, occupation, employment, politics, and environmental data.
- The course uses a subset of data including waves 1-9 (up to 2019).
- Data definitions are harmonized, and any distortion from COVID-19 is removed.
- Students can access the Understanding Society teaching dataset from the UK Data Service website using index code SN 8715.
Possible Interpretations of an Econometric Model
- Econometric models can be categorized as predictive (forecasting) or causal.
- Predictive models use existing data to predict future outcomes.
- Causal models assess the effect of policies or actions on individuals, households, etc.
- External validity assesses results' applicability to other settings.
- Models' interpretations must consider confounders, selection biases, unobserved variables, and other complexities to yield valid conclusions.
Use Case - Predictive Models
- Models use satellite data and night-lights to enhance official GDP statistical data for more precise forecasting, by identifying economic activity indirectly by linking to physical activity.
- This is an area of active research frequently updating and expanding the scope of applications.
Use Case - Causal Models
- Causal models can analyze the effect of events or interventions on economic outcomes, e.g., the 1992 mortgage reform in Denmark.
- This helps identify the relationship between financial frictions, interest rates, labor markets, and other outcomes of interest on economic data collected from surveys and observations.
We think counterfactual when we say causal
- Causal estimates require considering alternative scenarios (e.g., what would have happened if a policy wasn't implemented).
- Isolating policy effects from other influences (e.g., confounders, selection issues, unobserved variables) is crucial but challenging.
- Time-dependent factors, constantly changing reality, create complications to policy prescriptions based on causal interpretations of data.
Counterfactual, Identification, Selection
- Complexities arise in understanding health outcomes when considering the interaction between hospitalization and health status, which might not lead to causality as inferred.
- Comparisons should consider potential underlying reasons causing a difference in observation outcome.
- Comparisons/analyses need to consider individuals' starting conditions (e.g. pre-existing health factors).
Towards a Causal Model
- Causal models draw upon past work.
- Potential outcomes and methods for estimating causal effects from observed data are introduced, along with various challenges.
- The fundamental problem states that outcomes under different treatment scenarios are not directly observed; potential outcomes are inferred to quantify a potential causal relationship.
ATE, ATT, ATU and not too LATE
- This section covers various treatment effects, conditional on specified variables for better evaluation.
- Concepts of Average Treatment Effects (ATE), Average Treatment Effects on the Treated (ATT), and Average Treatment Effects on the Untreated (ATU) are introduced along with their conditions of use.
- These effects quantify the impact of specific treatments applied to different groups.
Selection Bias
- Selection bias occurs when individuals' choices to participate in a treatment influence analyses.
- It highlights the difficulties in evaluating treatment effects and the challenges in drawing causal assessments from observed data.
- It also considers how this bias arises when an intervention/treatment is chosen by individuals who's circumstances are fundamentally different from those not chosen, and this difference influences the outcome assessment.
Conclusion
- A summary of the concepts presented, with core R, tidyverse basics, data loading, running regressions and interpreting outputs covered in the lectures and this introduction section.
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Description
This quiz focuses on applied econometric methods using R, essential for analyzing data and making informed decisions in economics and finance. It covers causal analysis, time series modeling, and real-world problem-solving, providing students with hands-on experience in coding and implementation. Active participation is encouraged as students learn crucial econometric tools and techniques.
