Økonometri

(4). ECONOMETRICS

Questions about the topics within the subject area of Econometrics should be directed to: Christian Møller Dahl, Department of Business and Economics, e-mail: cmd@sam.sdu.dk

All topics in this subject area can be written in English or Danish.

 
4.1 Inequality in life quality, health and health behavior – a geographical perspective
Denmark is known to be one of the most equal countries in the world when it comes to income. Therefore, it is quite intriguing to realize that the social inequality in health is very high in Denmark. While this topic has been investigated extensively in literature, geographical aspects alike potential geographical inequality has been much less investigated. To our knowledge, no such studies considered the Danish case.
The purpose of the suggestion could be to investigate inter alia:
1. To which extent is there geographic inequality in life quality, health or health behavior
2. To which extent may such inequality be ascribed to geographic variation in economy, demography, policy etc?
3. Are the present policy initiatives appropriate?
 The empirical investigation can be based on a rich source of data, aggregated to the municipal level.
The project can be written in Danish or English. However, a basic ability in reading Danish is highly recommendable, as some of the data sources and references are in Danish.
 Literature: 
Ulighed i sundhed – årsager og indsatser. Sundhedsstyrelsen 2011. Equity, Social Determinants and Public Health Programmes. WHO 2010
Michael Bech, Jørgen Lauridsen: Exploring spatial patterns in GP expenditure. European Journal of Health Economics, 10, 2009, 243-54.
Data bases: Indenrigsministeriets Kommunale Nøgletal, Statistikbanken
 
 4.2 Social inequality in health – can it be measured, and how?

 Denmark is known to be one of the most equal countries in the world when it comes to income. Therefore, it is quite intriguing to realize that the social inequality in health is very high in Denmark. While this topic has been investigated extensively in literature, the  empirical approaches have been quite rudimentary in certain dimensions: Income has generally been used as a measure for socioeconomic status, even though severe endogeneity may exist between income and health; health has been measured using simple and subjective measures like self-assessed health, the development over time is less investigated etc.
The purpose of the suggestion could be to investigate inter alia:
1. Can we suggest and apply improved measures of social status?
2. Can improved results be obtained by using objective health measures?
3. What happens over time with health inequality in DK?
4. Are the present policy initiatives appropriate?
The project may conveniently be carried out as an empirical investigation, based on the  Survey of Health, Ageing and Retirement in Europe (SHARE), which enables comparison across countries, but different Danish sources are also available.
The project can be written in Danish or English. However, a basic ability in reading Danish is helpful, as selected relevant references are in Danish.
 
Literature: Ulighed i sundhed – årsager og indsatser. Sundhedsstyrelsen 2011. Equity, Social Determinants and Public Health Programmes. WHO 2010
Terkel Christiansen and Jørgen Lauridsen: Determinants of inequality in health with focus on retired Danes. SDU 2009. Data bases: The SHARE survey

 

4.3 Socio-economic status (SES) and Health
Socio-economic status such as income, wealth, employment status, schooling etc. have been shown to be associated with health (Smith, 2004). Yet, on the other hand, measures of health such as a deficiency (Chong et al. 2016), onset of disease (Banks et al. 2010), birth weight (Black et al. 2007), height (Huang et al., 2013) etc. have been shown to feedback into labour supply, wealth, household income or schooling. Use a publicly available dataset that has health and SES information (see references for links to data) and disentangle the relationship between socio-economic variables and health through an empirical analysis with emphasis on the timing of health and socio-economic measures in the life-cycle. Students are free to pick a country context of their choice or to do a cross-country comparison. 
References:
1. Chong, Alberto, Isabelle Cohen, Erica Field, Eduardo Nakasone, and Maximo Torero. "Iron deficiency and schooling attainment in Peru." American Economic Journal: Applied Economics 8, no. 4 (2016): 222-55.
2. Smith, James P. "Unravelling the SES: Health Connection." Population and Development Review 30 (2004): 108-32. http://www.jstor.org/stable/3401465.
3. Banks, James, Alastair Muriel, and James P. Smith. "Disease prevalence, disease incidence, and mortality in the United States and in England." Demography 47, no. 1 (2010): S211-S231.
4. Black, Sandra E., Paul J. Devereux, and Kjell G. Salvanes. "From the cradle to the labour market? The effect of birth weight on adult outcomes." The Quarterly Journal of Economics 122, no. 1 (2007): 409-439.
5. Huang, Wei, Xiaoyan Lei, Geert Ridder, John Strauss, and Yaohui Zhao. "Health, height, height shrinkage, and SES at older ages: evidence from China." American Economic Journal: Applied Economics 5, no. 2 (2013): 86-121.
6. Johnston, David W., and Grace Lordan. "Discrimination makes me sick! An examination of the discrimination–health relationship." Journal of Health Economics 31, no. 1 (2012): 99-111.
7. https://g2aging.org/?section=page&pageid=18 – Longitudinal datasets from US, Mexico, EU, Costa Rica, Kora, Japan, Ireland, China and India with a focus on health and ageing. Also provides some harmonized datasets for cross country comparisons.
8. http://ibread.org/bread/data - Large resource of datasets (cross-sectional household surveys and longitudinal datasets) in developing countries.
 
4.4 Big data visualization and predictive analytics:
This project is about big data visualization and predictive analytics applied to one of the ongoing competitions hosted by www.kaggle.com.
The amount of available data in organizations (private and public) is growing exponentially. But more data doesn’t automatically translate into information that is useful and facilitate better decisions. Unfortunately, the capacity of most organizations to analyze data has not increased at the same pace as the available data. To replace gut feeling based on experience with a data-driven approach we need to enhance this capacity by introducing visualization and predictive analytics.
Big data visualization and predictive analytics, that is based on the fundamental principles of statistics (econometrics), trains a computer model to automatically learn from large amounts of data to find the complex, hidden patterns that can optimize your investment in financial assets; your inventory; predict fraud, maintenance, or customer retention; recommend the products that customers actually need; or even diagnose Alzheimer’s disease. 
Big data visualization and predictive analytics has gotten a lot of attention recently through the success of Kaggle. Kaggle is a web platform where organizations like General Electric, Pfizer and Facebook host predictive modeling competitions (based on a wide range of different data sources) with prices up to $3M. 
Literature:
James, G., Witten, D., Hastie, T., Tibshirani, R (2013). An Introduction to Statistical Learning with Applications in R. Springer (can be downloaded free of charge from the SDU Library) 

4.5 Unravelling Economic Puzzles with Panel Data Models with Unobserved Factors
There are important economic problems well suited to panel data models. One example is the Feldstein-Horioka puzzle (FHP) defined in Feldstein and Horioka (1980). They run a set of cross-section regressions for 21 OECD countries over the period 1960-1974. They encounter that the correlation between the domestic investment and the domestic savings is very close to 1. They interpret this result as that there is hardly any capital mobility between OECD countries, i.e., investors choose lower returns from investments in their countries of residence than higher returns from foreign countries. This conclusion contradicts open-economy macroeconomic models that have claimed high mobility of capital, especially amongst the OECD countries which trading relationship aims at stimulating international trade. This problem has been studied by several authors and samples since without a satisfactory solution. The objective of the paper is to apply panel data models with unobserved factors and see if they resolved this puzzle. 

Students are encouraged to find their own puzzle or problem in which panel data models did not provide good results, or explore the FHP for different countries and periods. 

Literature for the project will be given from the instructor after topics have been discussed with the student
 
4.6 Realized Variance for Cryptocurrency Predictability
Exploring the predictive power of estimators of the equity variance risk premium and the conditional variance for cryptocurrency returns. The Realized Variance (RV) has been a major focus of research into accounting for uncertainty in financial investments. The objective of this project is to compare different RV forecasting models and then applied the best forecast to predict one week-ahead Bitcoin returns. In the absence of intra-day data from cryptocurrencies, the student is welcome to use daily prices from https://coinmarketcap.com/coins/. The RV will be calculated with a weekly frequency.
Literature for the project will be given from the instructor after topics have been discussed with the student
 

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