Economics
The two short papers ask you to identify and explain economic reasoning underlying conclusions about current events. For the final paper, you will need to make an argument about economic analysis using current data. Specifically, you must choose an economic concept covered in class and provide current evidence either in favor of this concept or in disagreement with this concept. To do this you should collect data relating to this monetary economics concept and then analyze the data using theories developed in class. For instance, you might look at how monetary policy affects other macro variables, such as interest rates, prices, output, or exchange rates. You may want to take an important course concept, such as the quantity theory of money, the Fisher effect, or expectations hypothesis of the yield curve and then test that concept using real-world data. You can use U.S. data or data for one or more foreign countries. You can use time-series data or cross-sectional data. Those who don’t have a strong background in statistical analysis should use tabular and graphical analysis.

The topic you choose must be a topic we have covered in class on or before Dec. 1st. I will not accept papers that deal with the foreign exchange market or with the international financial system.

To receive full credit for this paper, it should:
1. Discuss the theory that you plan to test.
2. Discuss the country or countries that you plan to examine and the time period.
3. Discuss the data you plan to use. The source of your data must be publicly available, and you must include the most recent values available. I should be able to reproduce your data.
4. Explain any modifications made to the data. For example, are the values real or nominal variables? Are the values measured in levels or in rates of change?
5. Describe how you plan to use the data to test the theory. Examination of graphs and tables? Regression analysis?
6. Explain what the tests show. Are your results consistent with your hypotheses?
7. What might explain discrepancies between your findings and the theory’s predictions?
.
The following suggestions may also be helpful:
1. You should clearly distinguish between real and nominal variables. You need to think about which is more appropriate for your model. If you use or generate real data, explain how this was done.
2. You need to distinguish clearly between levels and rates of change. Again, you need to think about which is more appropriate for your model. For instance, most macroeconomic aggregates show an upward trend thus a time-series analysis may show a significant correlation even when the variables are actually unrelated. This problem can be reduced (but not always eliminated) by using rates of change rather than levels.
3. You should clearly discuss the issue of causality. If two variables are statistically related discuss what causes that relationship.
4. Please be aware of the identification problem. For instance, the correlation between price and quantity will depend on whether the market is impacted by demand shifts or supply shifts.

Suggested topics:
1. Fisher Effect
2. Yield curves as a predictor of inflation
3. TIPS, expected inflation, actual inflation, and rational expectations
4. Asset-price bubbles and efficient markets
5. Quantity Theory of Money (price levels, inflation)
6. Budget deficits and inflation
7. Money demand (interest rates, stability)
8. Liquidity Trap

Key variables:
1. Interest rates (real, nominal, long term, short term)
2. Inflation and the price level
3. The money supply and its growth rate (MB, M1, M2, MZM, etc.)
4. Real and nominal GDP, levels and growth rates, unemployment rate

Sources of data:
1. Trading Room
2. Web sites such as:
St. Louis Fed – https://research.stlouisfed.org/fred2/
World Bank – http://databank.worldbank.org/data/databases.aspx
OECD – https://data.oecd.org/
Yahoo Finance – http://finance.yahoo.com/
3. Library

Types of data sets:
1. Time series (watch out for serial correlation—use rates of change where possible)
2. Cross-sectional (try to get at least 25 observations)

Types of statistical analysis (don’t test tautologies like MV=PY or i = r + p):
1. Regression analysis (simple, multiple) if you feel comfortable interpreting the output.
2. Correlation.
3. Graphical analysis (label completely).
4. Tables and summary statistics (mean, median, standard deviation, etc.)
5. Event studies (a study of the immediate market reaction to news events.

Economics
The two short papers ask you to identify and explain economic reasoning underlying conclusions about current events. For the final paper, you will need to make an argument about economic analysis using current data. Specifically, you must choose an economic concept covered in class and provide current evidence either in favor of this concept or in disagreement with this concept. To do this you should collect data relating to this monetary economics concept and then analyze the data using theories developed in class. For instance, you might look at how monetary policy affects other macro variables, such as interest rates, prices, output, or exchange rates. You may want to take an important course concept, such as the quantity theory of money, the Fisher effect, or expectations hypothesis of the yield curve and then test that concept using real-world data. You can use U.S. data or data for one or more foreign countries. You can use time-series data or cross-sectional data. Those who don’t have a strong background in statistical analysis should use tabular and graphical analysis.

The topic you choose must be a topic we have covered in class on or before Dec. 1st. I will not accept papers that deal with the foreign exchange market or with the international financial system.

To receive full credit for this paper, it should:
1. Discuss the theory that you plan to test.
2. Discuss the country or countries that you plan to examine and the time period.
3. Discuss the data you plan to use. The source of your data must be publicly available, and you must include the most recent values available. I should be able to reproduce your data.
4. Explain any modifications made to the data. For example, are the values real or nominal variables? Are the values measured in levels or in rates of change?
5. Describe how you plan to use the data to test the theory. Examination of graphs and tables? Regression analysis?
6. Explain what the tests show. Are your results consistent with your hypotheses?
7. What might explain discrepancies between your findings and the theory’s predictions?
.
The following suggestions may also be helpful:
1. You should clearly distinguish between real and nominal variables. You need to think about which is more appropriate for your model. If you use or generate real data, explain how this was done.
2. You need to distinguish clearly between levels and rates of change. Again, you need to think about which is more appropriate for your model. For instance, most macroeconomic aggregates show an upward trend thus a time-series analysis may show a significant correlation even when the variables are actually unrelated. This problem can be reduced (but not always eliminated) by using rates of change rather than levels.
3. You should clearly discuss the issue of causality. If two variables are statistically related discuss what causes that relationship.
4. Please be aware of the identification problem. For instance, the correlation between price and quantity will depend on whether the market is impacted by demand shifts or supply shifts.

Suggested topics:
1. Fisher Effect
2. Yield curves as a predictor of inflation
3. TIPS, expected inflation, actual inflation, and rational expectations
4. Asset-price bubbles and efficient markets
5. Quantity Theory of Money (price levels, inflation)
6. Budget deficits and inflation
7. Money demand (interest rates, stability)
8. Liquidity Trap

Key variables:
1. Interest rates (real, nominal, long term, short term)
2. Inflation and the price level
3. The money supply and its growth rate (MB, M1, M2, MZM, etc.)
4. Real and nominal GDP, levels and growth rates, unemployment rate

Sources of data:
1. Trading Room
2. Web sites such as:
St. Louis Fed – https://research.stlouisfed.org/fred2/
World Bank – http://databank.worldbank.org/data/databases.aspx
OECD – https://data.oecd.org/
Yahoo Finance – http://finance.yahoo.com/
3. Library

Types of data sets:
1. Time series (watch out for serial correlation—use rates of change where possible)
2. Cross-sectional (try to get at least 25 observations)

Types of statistical analysis (don’t test tautologies like MV=PY or i = r + p):
1. Regression analysis (simple, multiple) if you feel comfortable interpreting the output.
2. Correlation.
3. Graphical analysis (label completely).
4. Tables and summary statistics (mean, median, standard deviation, etc.)
5. Event studies (a study of the immediate market reaction to news events.