This individual project is a replacement for the final exam and it accounts for 85% of your module mark. Because this coursework assesses all learning outcomes, and because of its heavy weight, you are expected to conduct two independent empirical studies: one is based on panel data analysis and the other one is based on time series analysis. Thus, your report shall be divided into two sections, with each section being a stand-alone “article” in the sense that it has its own title, introduction-literature review, data description-empirical model, analysis-inference,discussion-conclusion, and references (see more of the recommended structure below). For each section, please choose a socioeconomic phenomenon or relationship and conduct your investigation using real-world data.
The source of the topics can be your own experience/knowledge (as an economist), textbook examples (with proper modification), or academic literature. You are free to choose your topics,but please bear in mind that (1) they must facilitate analyses using panel data and time series data, and (2) they must be properly motivated (i.e., why is it important/useful to study the problem). The two sections will be marked separately using the same set of criteria (more on these later). With this coursework assignment you are expected to develop a good understanding of the learning outcomes, enhance your software skills, and familiarize yourself with the literature and popular datasets. Please also note that even though the statistical methods and models presented in ECO214 are sufficient to produce many interesting results,you are free to use more advanced panel data or time series methods if they provide additional information or fit your purpose.
Guidelines on choosing topics
Potentially, you may find research topics from the following sources.
- The first source is your textbooks in other fields of studies (micro/macroeconomics, labor economics, international economics, finance, etc.). Usually these textbooks cover a wide range of economic or financial theories which you can test with real-world data. For example, you learned the concept of productivity function in micro/macroeconomics and you may want to estimate a parametric form using province- or city-level data in capital stock, labor input, and output. This will be a typical panel-data application because there are unobserved idiosyncrasies. As another example, you learned in international economics the principle of purchasing power parity (PPP). Thus, you can test whether the price levels in any two countries is cointegrated with the cointegration coefficient equal to unity. This will be a typical time-series application.
- A second source is the academic literature. Google Scholar is the best place to search the academic literature. Type a key word and it will return hundreds of articles. You may read an article estimating the Philips curve. The model might be complicated and the statistical method might be highly sophisticated. You can implement a simplified version of the model/method based on your knowledge and data availability.
- A third source is textbooks in econometrics. Most econometric textbooks emphasize empirical examples and exercises. Thus, they provide a large pool of potential topics. The easiest approach is to take one of the problems and apply the empirical model to your own data.
- In addition, I encourage you to find your own topics by means of deep thinking. Deep thinking produces interesting research questions. To give an example, we all know that money may not be neutral in the short run but “should” be so in the long run. There are many real factors in the economy, including real GDP, unemployment rate, the real interest rate, etc. It is then your job to narrow down to one or two real variables and choose the appropriate statistical framework in testing the neutrality of money. These cannot be done without deep thinking.Even if you adopt a research question raised by others, deep thinking will help you refine the question and generate new insights. For instance, after you have estimated a production function using province- or city-level data, you may want to use the Solow residual to construct measures of productivity and make comparisons across regions and over time. The result may be informative on the nation’s regional disparity and overall productivity growth.
Below I give a few sample topics by data type.
- Estimate aggregate production function using regional (province- or city-level) data.
- Estimate determinants of pollutants emission using regional data.
- Estimate determinants of housing price using regional data.
- Estimate β-convergence using national data.
- Estimate the effect of training program using population survey data.
- Estimate the effect of having children on labor supply using population survey data.
Time series data
- Develop a forecast model for China’s major economic indicators.
- Explore the potential structural breaks during China’s economic transition.
- Test the neutrality of money using Chinese or U.S. data.
- Test the PPP principle using the price levels in two countries and the corresponding exchange rate.
- Estimate the monetary policy of the Federal Reserve, i.e., Taylor’s rule.
- Estimate a dynamic Okun’s law or Philips curve using U.S. data.
Although there is no restriction to the scope of topics you may try, to ensure that you obtain meaningful results from the analysis, please adhere to the following principles.
- Please make sure you test an economic model, rather than an accounting identity. An economic model is a hypothetical functional form that describes how one variable is determined by other variables. The exact form of this function is unknown and must be estimated using real-world data. For instance, we often assume a Cobb-Douglas production function 𝑌 = 𝐴𝐾𝛼𝐿𝛽, where 𝑌 stands for output (GDP or value added), 𝐾 for capital stock,𝐿 for labor input, and 𝐴 is called the total factor productivity (TFP). In this formulation, the parameters 𝛼 and 𝛽 are unknown, which can be estimated using real-world data.
Accounting identities, on the other hand, are known formulas that must be universally true. This statement has two implications. One, the parameters of the formula are all known, which means there is no need to estimate them. Second, the relationship must be always true for any data set,except for measurement error or statistical discrepancy. To illustrate, you all learned in Principless of Macroeconomic that 𝑌 = 𝐶 + 𝐼 + 𝐺 + 𝑁𝑁, where 𝑌 stands for GDP, 𝐶 for personal consumption expenditures, 𝐼 for private investment, 𝐺 for government spending, and 𝑁𝑁 for net export. This is an accounting identity because the use of outputs must be one of the four types. Here we have a linear function in 𝐶, 𝐼, 𝐺, and 𝑁𝑁, but their coefficients are known to be unity. Hence, it is meaningless for you to estimate this equation.
- Data must be available for all the variables in your model. Data availability is usually a major challenge for empirical studies. Using the Cobb-Douglas production function as an example, usually data on GDP (or value added) and labor input (employment) are relatively easy to obtain, but data on capital stock are seldom provided by the statistic bureau. If data on capital stock is unavailable, in principle the estimation cannot be done. In this very case, a common strategy is to estimate the capital stock using data on investment and the perpetual inventory method.
If your study employs country-level, province-level, or city-level aggregate data, please keep in mind that government agencies or international organizations are your only data sources. Please check their websites or publications (statistical yearbooks) to verify that the data you need are available. If you plan to collect data by a survey, please think carefully about implementation issues.
If data availability is a problem, you have three options: First, you can change your measurement. For instance, if you need data on the number of permanent residents in cities, but such information is not provided, you can use the number of registered residents instead.Second, you can modify your topic by using a different variable. As an example, you may want to estimate the aggregate production function. In that situation you need production capital stock of the city or province. Suppose that these data are unavailable but the statistical yearbooks do provide data on the capital stock of the secondary industry, then you can narrow down your topic to the production function of the secondary industry. In what follows you need to use industrial value added as the dependent variable. If both options fail, you had better think about a different topic for which data are available.