Statement of Purpose এর নমুনা-12 ( Economics)
I am applying to Harvard’s doctoral program in economics in pursuit of a career
in academic research. I entered economics research because I enjoy modeling realworld
situations with math. This interest was confirmed by my research work, which
included projects on mortgages, optimal surveys, and consumer savings. These
research experiences also led me to discover that I enjoy theory work, especially theory
fields that see wide use in empirical research, fields like game theory and mechanism
design. My interest in game theory and behavioral economics recently led me to
explore the subfield of learning in games, in which I have a few research ideas.
I entered economics research because I enjoy the process of modeling social
situations, the process of looking at an economic phenomenon, thinking about the key
empirical factors, and making the correct variable and structural choices to generate a
tractable model that explains the situation. For example, one project I worked on for
Professor John Jonson involved finding a formula that gave the best time to refinance
mortgages. In this project, I enjoyed contemplating the various tradeoffs between
simplicity and richness that went into the model design. Should we model interest rates
as mean reverting, or is a simple random walk sufficiently approximate? Should we be
precise and model mortgage amortization time, or should we avoid an extra state
variable and instead just use a time-stationary hazard rate? These tradeoffs were
interesting to think about, and existed in all projects: for example, my work on optimal
surveys required careful consideration of response interaction complexity. Overall, my
research work confirmed my interest in economic model building.
In doing research work, I also began to discover a new interest in economic
theory, especially in theory work that is heavily used by empirical economics. For the
mortgage-refinancing project, my major personal contribution was finding a closed-form
solution for the refinancing formula. I discovered that I enjoy carefully thinking about
the highly mathematical parts of the problem, like the existence conditions for the
formula’s solutions or the analytic details of the bellman equations. Similarly, I enjoyed
the process of finding mathematical insights in my optimal survey project. One insight
involved using a multidimensional envelope theorem; another insight involved pushing a
standard delta-method technique in statistics to infinite cases. In both project, I was
especially satisfied to know that these theoretical results advanced practical goals in
empirical research. For mortgage research, a closed-form solution significantly
advances the paper’s goal of providing a simple formula homeowners can use. For
optimal survey research, the math insights led to a method of construction of the best
survey possible. The method was put to actual use for a separate journal article on
empirical intertemporal discount rates. Through all these projects, I both enjoyed.
generating mathematical insights and knowing that these theoretical advances have
real empirical benefits.
My revealed interest in economic theory led me towards theoretical fields with
wide applications, fields like game theory and mechanism design where advances in
theory increase the power and scope of all of economics. For example, in game theory,
sequential equilibrium in extensive form games allows richer dynamic models. In
mechanism design, the revelation principle simplifies mechanism
calculations. Implementation theory allows economists to design novel institutions to
meet an objective that was previously untenable. The applicability of such theory work
appeals greatly to me.
In addition to game theory and mechanism design, behavioural economics also
interests me because of my recent exposure to the field in research assistance
work. Behavioral economics is appealing because it question the basic assumptions of
rationality in an attempt to generate more accurate predictions about human
behavior. However, work in behavioural economics often lacks unity. Instead of a
central model that explains a wide set of phenomena, oftentimes, there are numerous
models that each explain a specific phenomenon without the ability to generalize
further. For example in the subfield of learning in games, reinforcement models like
Roth and Erev (1995) explain trends in learning, but predicts convergence much too
slowly in coordination games (Boylan and El-Gamal 1992). In contrast, belief learning
models like Fudenberg and Levine (1998) allow hypothetical reinforcement and hence
faster learning, but performs slightly worse on zero-sum games (Battalio, Samuelson,
and Van Huyck 1997; Mookerjee and Sopher 1997). Camerer (1999) synthesizes these
two models in an Experience Weighted Attraction (EWA) model, but EWA has a high
number of parameters that vary widely for different games, and still exhibits poor
performance in zero-sum games. These models predict zero sum games poorly
because they fail to consider a fraction of players who overpredict reinforcement
learning in opponents. The missing component then is having players who are
heterogeneous in level of sophistication, a structure in the style of Nagel (1995) or Stahl
and Wilson (1995). However, instead of nth order reasoning, the correct concept
seems to be nth order sophistication, an idea that Camerer (2007) broaches with the
Cognitive Hierarchy (CH) model. CH is a static model however and needs to be
extended to a dynamic setting, perhaps by allowing player sophistication to rise over
time, or by basing the actions of level zero player on historic outcomes as in Stahl
(1996). This model would explain quick convergence in median action games –
sophisticates jump to the median very rapidly. This model also explains reinforcement
overprediction in zero-sum games: level-one players number higher than level-zero
players. If such a theory is confirmed through experiments, it would advance the goal
of having more general models for behavioral economics.
কিভাবে ইউনিভার্সিটির রিক্যোয়ারমেন্ট খুজবেন তা দেখানো হলো নিচের ভিডিওতে:
কিভাবে ইউনিভার্সিটির রিক্যোয়ারমেন্ট খুজবেন তা দেখানো হলো নিচের ভিডিওতে:
In addition to giving me ideas, my past work has also given me the skills needed
for graduate school. To build a technical toolbox, I have taken theoretical math,
graduate statistics, and graduate economics classes, culminating in earning an A on the
graduate micro generals last year. To experience working with real research, I have
done research in behavioral economics and consumer finance with John Jonson and in
auction theory work Barbara Babson. I have been exposed to many parts of the
research process: I have solved mathematical models in mortgage refinancing work; I
have advanced theoretical proofs in my optimal survey research; and I have analyzed
large data sets including US Census for behavioral research. These experiences have
given me skills for graduate work and have confirmed that research work is something I
enjoy.
Overall, I am fascinated with economics and very much enjoy research. I
especially enjoy building models and doing theory work with empirical impact. I am
interested game theory, mechanism design, and behavioral economics, and would like
to explore these and other economic fields in graduate school. My fascination with
research will provide me with the necessary ambition to succeed in Harvard’s program,
while my extensive coursework and field preparation will provide me with the necessary
skills to succeed in Harvard’s program.
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