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Questions and Answers
What is the literal meaning of econometrics?
What is the literal meaning of econometrics?
Measurement in economics
What type of questions does econometrics aim to answer?
What type of questions does econometrics aim to answer?
How much type questions.
Which of the following are types of data used in econometrics? (Select all that apply)
Which of the following are types of data used in econometrics? (Select all that apply)
Which type of data can take on any value?
Which type of data can take on any value?
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Which of the following is NOT a type of numerical data used in econometrics?
Which of the following is NOT a type of numerical data used in econometrics?
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Ratio numbers have an absolute zero point.
Ratio numbers have an absolute zero point.
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Interval scale data has a true zero point.
Interval scale data has a true zero point.
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Ordinal scale data provides information about the order of values.
Ordinal scale data provides information about the order of values.
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What are examples of the type of problems econometricians might solve?
What are examples of the type of problems econometricians might solve?
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List the steps commonly involved in the formulation of econometric models?
List the steps commonly involved in the formulation of econometric models?
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Econometric model building typically follows a classical statistical approach.
Econometric model building typically follows a classical statistical approach.
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Bayesian statistics involves developing the theory and model simultaneously.
Bayesian statistics involves developing the theory and model simultaneously.
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Bayesian statistics utilizes priors, which are assessments of existing knowledge or beliefs represented in the model.
Bayesian statistics utilizes priors, which are assessments of existing knowledge or beliefs represented in the model.
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Bayesian statistics is more popular than classical statistics in econometric modeling.
Bayesian statistics is more popular than classical statistics in econometric modeling.
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Classical researchers may have concerns about the subjective nature of priors in Bayesian statistics.
Classical researchers may have concerns about the subjective nature of priors in Bayesian statistics.
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If priors in Bayesian statistics are very strong, they can be easily overturned by data.
If priors in Bayesian statistics are very strong, they can be easily overturned by data.
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Classical statistics is considered more objective than Bayesian statistics.
Classical statistics is considered more objective than Bayesian statistics.
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Which of the following is NOT a point to consider when reading papers in academic literature?
Which of the following is NOT a point to consider when reading papers in academic literature?
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Econometric modeling requires understanding the non-statistical aspects of the real-life system being studied.
Econometric modeling requires understanding the non-statistical aspects of the real-life system being studied.
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Historical context does not play a significant role in econometric modeling.
Historical context does not play a significant role in econometric modeling.
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The way data is gathered is irrelevant for econometric modeling.
The way data is gathered is irrelevant for econometric modeling.
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It is essential to choose the most sophisticated econometric techniques, even if they are complex.
It is essential to choose the most sophisticated econometric techniques, even if they are complex.
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Econometric modelers should prioritize the technical complexity of their model over its practical usefulness.
Econometric modelers should prioritize the technical complexity of their model over its practical usefulness.
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Testing the estimation of an econometric model helps ensure the results make sense.
Testing the estimation of an econometric model helps ensure the results make sense.
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Sensitivity analysis is not essential in econometric modeling.
Sensitivity analysis is not essential in econometric modeling.
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The signs and magnitude of coefficients in an econometric model should be consistent with economic theory.
The signs and magnitude of coefficients in an econometric model should be consistent with economic theory.
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A sensitivity analysis determines whether the results are significantly affected by changes in the sample period.
A sensitivity analysis determines whether the results are significantly affected by changes in the sample period.
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Robust estimation results should be significantly different from standard results.
Robust estimation results should be significantly different from standard results.
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Study Notes
Introduction to Econometrics
- Econometrics is about using economic theory, data, and statistical tools to answer "how much" type questions.
- Its literal meaning is "measurement in economics".
- Econometrics uses theory and data from economics, business, and social sciences, along with statistical tools.
Types of Data
- Econometricians use three types of data:
- Time series data
- Cross-sectional data
- Panel data (a mix of time series and cross-sectional data)
- Data can be quantitative (e.g., profit, price, cost, demand) or qualitative (e.g., motivation, satisfaction, service quality).
Continuous and Discrete Data
- Continuous data can take on any value (e.g., profit).
- Discrete data takes on specific values, usually integers (e.g., number of employees, quantity sold).
Ratio, Interval, Ordinal, and Nominal Numbers
- Numbers can be classified as cardinal, ordinal, or nominal.
- Ratio numbers have numerical values with meaning and an absolute zero point (equal distance between values).
- Interval numbers have meaningful order but no absolute zero (equal intervals between values).
- Ordinal numbers show order or position, but not meaningful differences between values.
- Nominal numbers have no natural order.
Examples of Econometric Problems
- Testing if financial markets are informationally efficient (weak form).
- Evaluating if CAPM or APT are better models for predicting returns on risky assets.
- Measuring and forecasting bond return volatility.
- Determining the factors that affect bond credit ratings.
- Modeling long-term price and exchange rate relationships.
- Finding the optimal hedge ratio for oil.
- Testing technical trading rules.
- Examining if earnings or dividend announcements affect stock prices.
- Evaluating if spot or futures markets react more quickly to news.
- Forecasting the correlation between stock indices of different countries.
Steps in Econometric Modeling
- Start with existing economic theory or evidence.
- Develop an estimable theoretical model.
- Collect data.
- Estimate the model.
- Evaluate the model's statistical adequacy.
- If not adequate, reformulate the model and repeat the process.
- If adequate, interpret the model.
Bayesian vs. Classical Statistics
- Classical statistics involves postulating a theory, building a model, and testing it with data.
- Bayesian statistics involves developing theory and model together, using prior beliefs (probabilities) combined with data to form posterior probabilities.
Considerations When Reading Econometric Papers
- Does the paper develop a theoretical model or just use techniques for data mining?
- Is the data quality good and from a reliable source? Is the sample size large enough?
- Were the techniques used validly? Were appropriate diagnostic tests conducted for assumptions?
- Are the conclusions sensible and justified by the results? Is the importance of the results overstated?
Additional Points in Applied Econometrics
- Use common sense and accounting/finance theory. Theory isn't just for model development, also crucial for interpretation and identifying testable predictions.
- Understand the real-life context of the system (history, institutions, peculiarities).
- How were the data gathered?
- Keep models sensibly simple. Use appropriate sophisticated techniques, not just because they are novel.
- Carefully check the estimation results (signs, significance, magnitudes). Ensure results are consistent with theory.
- Evaluate the sensitivity of results to factors such as sample period, explanatory variables.
- Assess the differences from using robust estimators.
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