Self-study PhD at the London School of Economics?

Here’s how you would do it…

Notorious for it’s rigorous workload, well respected faculty, and award-winning alumni, the prestigious London School of Economics (LSE) is consistently ranked in the top 5 PhD programs worldwide. This top London university is known for it’s high standards, high starting salaries, and high tuition. While still dramatically cheaper than a top 5 US school, one year at LSE will cost about 16K pounds (25K USD) and another 10K to 15K pounds in cost of living. However, if your goal is to gain a PhD level understanding of economics and better position yourself for top banking jobs without a $100K degree, this is how you would do it:

LSE PhD is composed of 2 key parts: masters level course work, and PhD level research.

In year 1, everyone begins with as a candidate for a Masters of Research (MRes) in economics. This is a 2 year program before even being able to apply to the PhD program. During year one, the MRes candidate takes the bulk of their course work including a full year of macro, micro, and econometrics. As shown below, these courses equate to about 12–15 credit hours of class per semester, but get into the nitty-gritty guts of these subjects. As far as self-study, this portion is the most easily replicable by doing the following:

Course Work

Take a shot at a self-study version of the following 3 courses based on the actual course descriptions from LSE. These will be the building blocks for later research work, and will develop your economics skills to a masters level. Micro and Macro economics will build on a lot of the basics you have picked up in college, but dive in past theory to get into more of the statistics/calculus behind the models. you may want to quickly brush up on your college level math to prepare. Finally, the econometrics course is the centerpiece to economic research and will be the most important of the 3 courses. PhD level economic research is mostly based on these econometric theories and measurement techniques. A strong understanding of this topic will allow you to solve nearly any business problem with an economic/statistical model.

Take a look at the summary of the course descriptions found on the LSE site:

Course 1: Microeconomics — analytics tools, theory, mathematics (stats)

Topics include: Consumer theory, producer theory, general equilibrium, welfare, choice under uncertainty, game theory, economics of information, agency theory, contracts, topics in mechanism design.

Text: The main text is Mas-Collel, Whinston & Green, Microeconomic Theory, OUP.

Other suggestions: D Fudenberg & J Tirole, Game Theory, MIT Press; D M Kreps, A Course in Microeconomic Theory, Harvester Wheatsheaf; H R Varian, Microeconomic Analysis (3rd edn), Norton; M J Osbourne & A Rubinstein, A Course in Game Theory, MIT Press; G A Jehle & P J Reny, Advanced Microeconomic Theory, Longman.

Course 2: Macroeconomics — economic growth modeling, equilibrium, monitary policy

Topics include: Neoclassical Growth Model, Optimizing Behaviour in dynamic models under certainty, Endogenous Technological Change, Imitation and Convergence, Growth and Development Accounting, Efficiency Wages, Growth and Unemployment, Dynamic Stochastic General Equilibrium Models, Fiscal policy analysis, Monetary Economics, credit frictions, sticky prices,

Text: D Romer, Advanced Macroeconomics, McGraw-Hill Advanced Series in Economics, New York, 1996.

Other suggestions: D. Acemoglu, Introduction to Modern Economic Growth, Princeton University Press, 2009; R J Barro & X Sala-i-Martin, Economic Growth, McGraw-Hill, 1997. L Ljungqvist & T Sargent, Recursive Macroeconomic Theory, MIT Press, 2000; N Stokey & R E Lucas, Recursive Methods in Economic Dynamics, Harvard University Press, 1989.

Course 3: Econometrics — Estimation techniques, regression, time-series modeling, and drawing inferences

Topics: estimation and optimality, asymptotic theory, statistical inference, classical testing (Wald, Likelihood Ratio, and Lagrange Multiplier), linear regression model, errors in regression(heteroskedasticity, autocorrelation, measurement error, omitted variables, simultaneity, missing data), non-linear regression models, quantile regression, bootstrapping, time-series theory, unit roots, simultaneous equations for non-stationary variables, co-integration, ARCH and GARCH models, panel data methods, limited dependent variables, nonlinear panel data, duration models, program evaluation, nonparametrics, kernel estimation, and differences in differences.

Recommended books are: W H Greene, Econometric Analysis, 6th edn, Pearson Education; R Davidson & J MacKinnon, Estimation and Inference in Econometrics, Oxford University Press, 1993; P. Ruud, An Introduction to Classical Econometric Theory, Oxford University Press, 2000; T Amemiya, Advanced Econometrics, Harvard University Press, 1985; J Johnston, Econometric Methods, 3rd edn, McGraw Hill; G Judge et al, A Course in Econometrics, Wiley, 1988; G Maddala, Econometrics, McGraw Hill, 1977.

Online Courses: Supplement your studying with open courses

Nothing can fully replace the immersive learning experience from a classroom lecture, but online courses and tutorials have started cropping up as part of a MOOC movement (massive online open courses). To compliment your self-study and texts, take some time to watch relevant lectures from LSE, Yale, and Stanford who have embraced open courses and posted some economics courses.

Additionally, you can view actual LSE lectures on their youtube channel. Even if you are not a quant heavy economaniac, you should check out the open lecture series for some TED-esque speeches from noted economists on current events and strategy.

Research

Year 2 is dominated by research work to validate your MRes degree and apply for PhD acceptance. The research is based on a lot of the econometric statistics and methodology from the required reading in course 3. To simulate self-study research, I would break the process into 3 key parts: defining an interesting topic, developing in-depth research, and communicating actionable conclusions.

Based on your studies from course work, or personal passions, select a topic that will be meaningful and interesting to you. For some inspiration on economic topics, check out the link to some past LSE student’s research available on their website.

As you develop your research and format conclusions, reference LSE’s “research style guide”. This will be useful for those developing a portfolio of professionally appealing economic analysis.

Finally, the PhD candidate begins field work and research to develop a thesis and dissertation after they have been accepted based on their MRes research paper. Typically, a PhD involves 2–3 years of challenging research guided by LSE professors and research assistants. For self-study this would involve a good amount of research and analysis over the course of a year. You will lack the resources of a full-fledged PhD student, but you will still be able to solve a local business problem and develop a good network using your masters level knowledge of economics. As far as formatting your PhD level work, I would do the following:

  1. Define a problem to solve — try to stay focused on a local and solvable problem. This will help you with networking and availability of data.
  2. Develop a research plan and timeline
  3. Involve local businesses — most businesses are happy to help with the educational process and most lack economics expertise that you would have.
  4. Extensively use surveys — as a social science, Economics relies heavily on surveys and representative samples to make inferences.
  5. Consolidate learnings and publish broadly

After completing these tasks, you will have completed a PhD level of analysis and learning, solved a local problem, and developed a network of business and academics. You may not be able to sit for the PhD boards, but you will be well versed in a relatively exclusive field with high demand.

Clearly, the task of simulating a full PhD may be overly audacious given the level of intensity and time commitment required at LSE. However, nobody can deny the value of building your economics repertoire before applying to banks or other financial businesses. Afterward, you wont be able to put an LSE PhD on your resume, but you will be able to better compete with them in the business world.

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