IMBA Thesis Workshop Class 2. P. Schuhmann, Spring 2013 Lecture material based on the work of Steven Greenlaw : Doing Economics: A Guide to Understanding and Carrying Out Economic Research , Steven A. Greenlaw , 2006. Houghton Mifflin Co. Available for purchase here:
P. Schuhmann, Spring 2013
Lecture material based on the work of
Doing Economics: A Guide to Understanding
and Carrying Out Economic Research,
Steven A. Greenlaw, 2006.
Houghton Mifflin Co.
Available for purchase here:
You should attempt to answer the following for each paper you read (1-3 sentences for each):
Use the literature
Reference sources of factual information or opinion:
Reference facts, positions or arguments to help motivate your argument: :
Reference general methodology
Reference modeling particulars:
Reference source conclusions to support your conclusions:
Reference sources for additional information:
Which of these styles of writing seems more appropriate for your thesis (or professional research article)? A or B?
A. “We measure the importance of several factors influencing biotech valuation.”
B. “The importance of several factors influencing biotech valuation were measured”.
A. “I hypothesize that firms with greater drug approval rate will have higher valuations.”
B. “It is hypothesized that firms with greater drug approval rate will have higher valuations.”
The active voice is clearer and more honest.
Official exchange rates vs. PPP exchange rates
We need equality of purchasing power for these comparisons to be meaningful.
E.g. bond yield = 6%
inflation = 2%
Real interest rate = 4%
E.g price in (base) year 2000 = $50 (CPI = 100)
1. An explanation of what data you are using:
“The data in this study include daily closing price indices of Shanghai A share (SHA), Shenzhen A share (SZA), Shanghai B share (SHB), Shenzhen B share (SZB) and Hong Kong Hang Seng China Enterprises Index H- Shares from the first quarter of 1992 through the fourth quarter of 2012.”
“In order to test the hypotheses noted above, we use measures of monetary aggregates, price levels and real GDP from 23 European nations for the period 1998-2011. All data are quarterly, except personal income, which was converted to a quarterly average from monthly data. Variable names, definitions and sources are reported in Table 1.”
“Data on mergers and acquisitions between U.S. corporations were collected from Bloomberg using the following criteria: …”
2. A description of how you treated or modified the data.
“Monthly returns of international equity indices were averaged for the ten-year period 2000- 2010, and closing prices expressed in local currency were used to compute returns.
For countries with more than one equity index, we select the capitalization-weight index that best represents the country’s overall stock market. We estimate volatility as the standard deviation of the monthly stock index returns over the 2000-2010 sample period. We estimate the risk-adjusted return using the Sharpe ratio calculated as:
The numerator of the Sharpe ratio is the average excess return over a risk-free benchmark. We use the average risk-free rate per country from 2000 to 2009 which we retrieved from the United Nations database. Homogeneous data were difficult to gather, so for some countries, the money market rate was chosen as the risk-free rate while for others we use the three month government borrowing rate.”
3. A description of the distribution of important variables, including descriptive statistics.
Within the revealed preference literature, while there has been considerable research investigating various representations of expected catch (McConnell et al. 1995), there has been considerably less attention given to expected congestion. Furthermore, the effects of accounting for congestion on compensating variation measures for changes in site quality or access price within this framework have yet to be explored. To further illustrate some of these important issues within the RUM framework, consider the following explicit linear representation of the conditional indirect utility function
Vij t + εij t = βtcij+ δcejt+ λqejt+ εij(3)
where V is the conditional indirect utility for individual i, ceand qeare the expected catch and congestion, respectively, of visiting site j at time t, and the other variables are defined above.
Real estate investor sentiment surrounding periods of recurring hurricane landfalls is an attractive topic for research, especially in the area around Wilmington, NC, where four hurricanes made landfall between 1996 and 1999. Adjacent to discoveries of a real estate market “recovery” in this area of southeastern North Carolina since the unprecedented series of hurricane landfalls in the late 1990’s, we test a series of empirical expectations. First, we affirm the findings of Graham, Hall, and Schuhmann (2007) where home prices rebound in the years following Hurricane Floyd, the last major storm to hit the region in 1999. Second, we assemble metrics to proxy for investor sentiment, and use those metrics to illustrate the market’s improving sentiment since early this century.
The first metric we consider is the spread between listing and selling prices. Our premise is that spreads between listing and selling prices increase as home-buyer sentiment changes with perceptions of increased exposure to hurricanes and catastrophic risk. This expanding spread is affirmed by Graham and Hall (2002). Extending those findings, we expect the spread to narrow in the years following Floyd. Home buyers become less willing to purchase at current prices, ceteris paribus, due to expectations of increasing future hurricane losses. As a result, sellers are forced to provide some price concession to compensate buyers for the assumption of additional risk.
Based on results in the literature and economic theory of demand, we hypothesize that tourism demand is a function of the explicit and implicit costs of travel, individual demographics and destination quality.
More formally, travel demand can be estimated as:
Log (vi/pi) = β0 + β1TCi + β2 Qi + ∑ (βk∙Xk+ … + βj∙Xj) (1)
Where vi = total visits from zone i,
pi= population of zone i,
TCi= round trip travel cost from zone i (explicit + time cost) Qi= measure of coatal quality for respondents from zone i (response or instrument), and Xk… Xj = demographic characteristics of respondents from zone i.
In order to examine the relationship between student characteristics and economic knowledge acquisition, scores on the economics portion of the survey serve as the dependent variable. Note that the pre and post survey results can be examined individually or together by calculating the difference in correct answers between the pre- and post-course surveys. In the former case, the variable we wish to explain is constrained to be zero or a positive integer, hence a count data model will be appropriate for estimation. In the latter, the variable of interest (change in score) can be positive or negative; hence more traditional regression methods will suffice for estimation.
Poisson regression models provide a standard framework for the analysis of count data when a majority of the data falls in the lower end of the distribution (ie 0,1,2,..). The Poisson distribution determines the probability of a count.
(1) P(yi) = Prob[yi= j] = exp(-i) -ij/ j! , j = 0, 1, 2, …
Where the standard formulation for i is:
(2) i = exp( ΄xi )
In order to examine the relationship between student characteristics and pre- and post-course scores on the economics questions in our survey, we estimate the following equations using a Poisson specification for both the pre-course survey results and the post-course results (variable definitions are provided in Table 1):
(Model 1) Yi = 0 + 1(RSURVEYi)+ 2(busINESSi) + 3(Otheri) + 4(hseconi) + 5(macroi) + 6(mac had mici) + 7 (mic had maci) + 8(mic had surveyi) + 9(mac HAD mic AND surVEYi) + 10(tmATHpreior tmATHpOSTi) + 11(MWCi) + 12(UNCWi) + 13 (UNi)
(Model 2) Yi = 0 + 1(RSURVEYi)+ 2(busINESSi) + 3(Otheri) + 4(hseconi) + 5(macroi) + 6(mac had mici) + 7 (mic had maci) + 8(mic had surVEYi) + 9(mac HAD mic AND surVEYi) + 10(Q1i) + 11(q2i) + 12(q3i) + 13 (q4i) + 14(q5i) + 15(q6i) + 16(q7i) + 17(Q8i) + 18(MWCi) + 19(UNCWi) + 20(UNi).