This course has three main goals, and one subsidiary component. These are:
- to write your capstone paper;
- to understand the empirical approaches to evaluating macroeconomic policy;
- to survey the core components of “modern” DSGE macro models used in theoretical analysis; and
- to delve into current data on the US economy.
The most important component of the course is your capstone paper and associated presentations / blogging. Your capstone should allow you to combine the theoretical, empirical and research tools you have developed in your economics major and apply them to a concrete problem. It might be a macroeconomic policy issue raised in the ongoing presidential primaries, but the range of possible topics is large. Papers in recent years included an analysis of reserve currencies, the impact of shale oil on regional economies in the US, the possible existence of a natural resource curse for a developing country (Nigeria) or a state (Louisiana), the impact of Ebola on West Africa, apprenticeships and unemployment, alternatives to funding national pensions (in the US, Social Security), or the influence of a unicameral (vs bicameral) legislature on national economic policy.
This term you must choose a general topic and carry out a quick survey of the literature and data. You must then refine your thesis – your statement of the problem – and do a “deep dive” into the literature. Having done that, you need to present the theoretical frameworks and empirical approaches in the literature. Then comes your analysis. What theoretical framework do you find most compelling, based on your evaluation of its strengths (and weaknesses) compared to other approaches? What data and empirical strategies have economists used, and which do you judge work best? If the literature offers no clear conclusion, why not? (You surely know President Truman’s wish for a one-handed economist!) What might done to strengthen the literature or reach a more compelling bottom line?
Empirical Issues in Macroeconomics
For many “micro” questions the empirical focus is on the value of a single crucial coefficient in a multiple regression framework. Of course we have no direct measures of “utility” and the data available are never ideal, lacking key variables and poorly measuring others. For policy questions, there are almost always winners and losers. Different data sets and regression structures may lead to contradictory results. Challenging, but familiar.
Macroeconometrics … well, first, macro data are time series in nature. As we’ll see, that causes severe problems for the standard linear regression framework. Those problems are not insuperable, but all too often empirical macro work uses jargon that (to put it kindly) is not transparent. You have to learn to read (or read around) that jargon. Second, data are limited. If we think that the financial deregulation of the 1980s changed the response of the US economy to interest rates and the interaction of other key macro variables, then we have only (say) 25 years of quarterly data available. Unfortunately 100 observations do not provide many degrees of freedom, and (even worse) we may have only a couple cases (or less) of inflations or recessions or presumed macro shocks in our datasets. (If you’re wanting to analyze a component of China’s macroeconomy, then the situation is worse: even 10 years makes for qualitative change.) Third, macro models are intrinsically multiequation in nature. Past (lagged) variables reasonably affect current and future variables; adjustment processes are not instantaneous. But if past values matter, then we need to track not just the impact of the previous quarters, but of multiple lags: unfortunately we can’t focus on a single coefficient. Oh, and lags quickly use up degrees of freedom (think 4 variables with 4 lags each). There’s no winning, only varying compromises and tradeoffs.
That leads to a crucial bottom line: you need familiarity with these issues and associated jargon to read the literature. So we will learn about “unit roots,” lagged variables, vector (multiequation) autoregressions (VARs) and impulse functions. Our goal is minimalist: what do you need to know to “read around” the technical empirical issues and understand the results?!
In “macro” everything affects everything else: interactions and tradeoffs are of the essence. In general, then, we are interested in simultaneously evaluating the impact of a policy on multiple variables. In Econ 211 – (intermediate) macro theory – you grapple with the IS-LM model and (perhaps) a few extensions. That leads to more than the implicit two-equation supply-and-demand model (or utility and decision variable model) of microeconomics. More realistically, even a minimalist model consists of a dozen equations in a dozen unknowns, plus control variables and parameters [the level of risk aversion, the labor share of income] and definitional “identities” [GDP = C + I + G + X – M]. Oh, and in these models most equations aren’t linear, and so the system can’t be “solved.” Welcome to the world of computer simulations!
What constitutes a “good” model is a subject of contention, in that a model that tracks one set of variables well may do poorly on other variables. How important is clarity? Adherence to known “micro” behavior? How do you incorporate expectations of the future (ignoring expectations was ruled out by Milton Friedman’s 1968 presidential address to the American Economic Association). As you will learn, macroeconomic theory is in disarray. Indeed, “modern” models implicitly (or explicitly) deny the relevance of the IS-LM analysis that is the core of Economics 211.
The core alternative to the basic IS-LM with which you are familiar is a DSGE model (DSGE = dynamic stochastic general equilibrium). We will develop the core components of this class of models in several steps. First, we will play with the 1957 Solow growth model that is at the core of DSGE models. We will then look at a variation of a very simple (and historically influential) macroeconomic model that incorporates “rational” expectations. Such models endogenize monetary policy (and potentially fiscal policy). As one example of how that’s done, we will look at the (1993) “Taylor Rule.” Third, we need to understand the multiperiod nature of macroeconomics models. To that end we introduce the “Life Cycle Model” (Ando-Modigliani 1954) and the comparable “Permanent Income Model” (Friedman 1957), and then combine those with the net present value” discounting approach of OLG (“overlapping generations”) model to incorporate internally consistent multiperiod optimization. (OLG models harken back to a 1958 model of Paul Samuelson.) Once we have done that we can pick apart the components of a DSGE model. [Samuelson, Friedman, Solow and Modigliani are all Nobel laureates; Taylor is still comparatively young.]
US Macro Data
You surely encounter the “headline” unemployment rate (currently 5.1%) and other macroeconomic metrics. Policy evaluation requires digging beyond the headlines. What does (and does not) that 5.1% number tell us? How are the data collected, and what (if any) alternative conceptual measures and data sources are available? I will focus on data for the US, though you can find similar [though seldom identical] metrics for all the OECD (developed countries’ club) economies. Thanks to the internet these data (and much more) are readily accessible. By the end of the term turning to “FRED” should be an instinct. I will present analyses that make more sophisticated use of the extraordinary wealth of publicly available data. Following such examples, you (collectively) will be responsible for tracking a variety of key metrics that track (among others) one or more concept of “(un)employment,” “inflation,” and “GDP”. As you will see, a spreadsheet can let you do a lot.
I have now observed 4 “bubbles”. The first was while I worked on Wall Street in the late 1970s as the “gofer” of a team making what ultimately proved to be $2 billion in bad Eurodollar loans to Latin American borrowers. (Back then US$1 billion was a lot of money.) A resume overflowing with red ink was one impetus to leave banking for a PhD at Yale. My second experience was Japan’s real estate bubble, which broke while I was on sabbatical in Tokyo in 1991-92; A “lost decade” (actually closer to 15 years) followed. (The US peak was in early 2007, so we’re at the 8+ year point and not yet back to “normal”.) Then came the dot.com bubble; an alumnus who was CEO of a then high-flying online banking venture frequently visited campus, speaking repeatedly in Econ 398. I bought stock in that company twice in 2000, once after the price fell 40% and again after it had fallen 60%. (It fell 93%…ouch!…but several at W&L bought at peak.) Then there’s our recent bubble, which has left me as the unintended “owner” of two houses, er, mortgages. Despite the passage of years, local Rockbridge County real estate markets remain moribund, with prices still 20% or so below peak…
Yes, macro impacts our daily lives. Macroeconomics is also fascinating intellectually. But it’s more that a game: bad analysis contributed to past disasters and impeded recoveries. We’re talking tens of millions of people who lost jobs, savings, houses. I’ve been lucky – most of my losses are on paper, and only once – in 1974 – has a recession cost me a job. However, I know people on 4 continents whose lives were upended by bursting bubbles, and not for the better. Microeconomics is interesting and relevant, but macro is really important. Getting “micro” policy wrong hurts; getting “macro” wrong is tragic.
None. Readings – all downloadable at no cost as a W&L student – will be posted on the course schedule on this WordPress site. If you would like to purchase a book that takes you beyond the intermediate level, David Romer’s Advanced Macroeconomics is the place to go. It is very challenging, but nevertheless more accessible than other texts. The old (3rd) edition can be had used for about $25. See the blogs linked on the right side of this page for current [and archived] discussion.
Modern macroeconomics is extraordinarily technical; I view my job as in part to find ways for you to avoid the need to grapple with math. I think I’ve found ways to do that. So by the end of the term you should:
- understand the basic building blocks of the “new” macro: growth models, lifecycle optimization, overlapping generations models, and the rational expectations approach, all in a “general equilibrium” setting;
- have practice in finding and presenting basic U.S. macroeconomic data, and understand some of the empirical limitations, econometric and otherwise, in using that data;
- read and be familiar with contemporary debates, including what constitutes a good model (no consensus!), and how models might permit evaluating the size of “the” multiplier;
- through your paper project, learn how to research a (macro)economic topic, starting with how to locate and filter the academic literature, knowing data sources, being cognizant of empirical issues, and being able to present your findings to others in several modes of communication.
The course is a small seminar, which will rely upon class discussion rather than lectures. You will thus need to go through readings in advance. Be warned: an individual paper can readily take a full hour to read – don’t wait until the last minute!
We devote a portion of each week to the current status of the US economy – U, π, g, i. At least once during the term you will need to locate, analyze, graph and present the relevant data. I also expect you to read a variety of blogs, to keep abreast of current debates. Tied to that, you yourself will need to contribute to the course blog and commenting on the posts of your classmates. That too will facilitate class discussion.
All of you must undertake a Capstone project, submitting your results in writing and presenting your topic orally to the class.
I will not impose a final exam upon you.
Blogging and commenting on the blogs of your peers is a course requirement. Based on experiments the past several years, I will do things differently this term. In particular, I will (i) mandate specific blog topics and (ii) limit the number of posts per week. This should (iii) make it easier for you to do a post, (iv) make it easier for me to hold you responsible for quality, (v) better tie blogging to the syllabus and by limiting the number and focus of posts (vi) make it easier for you all to engage with your peers (and me) in give-and-take via blog comments.
My aim this term is to help you write short, focused pieces that you will be proud to have others read. It’s useful practice: short memos (and in marketing, actual blogs) are a form of output pervasive in the business world. I will provide each of you access to this blog (which includes posts and comments from previous terms), and show you how to format for (visual) clarity, how to incorporate graphs from Excel and FRED, and otherwise provide guidance on what might make for a good post.
During this term there will be a “release” of most (if not all) of the key macroeconomic indicators for the US economy. Powerpoint (graphs and bullet points) is a good way to convey such information. You should each have a chance to do one initial presentation, and then to follow up with additional detail and sophistication [e.g., graphs reflecting spreadsheet analysis of the data] later in the term. I will also ask you to present your capstone project to your peers in several iterations, from (i) your initial topic to (ii) a key paper to (iii) your final results. As should be obvious, these will begin as very brief presentations (5 slides including an initial title slide and final bibliography slide) and expand by the end of the term to reflect your deeper, better researched understanding of your topic.
Weights will vary depending on the evolution of the class. Tentatively:
- short papers / homework exercises / a globalization essay will account for 15%,
- reading notes / discussion leadership / data presentations for 15%,
- the capstone paper and its components 50%, and
- blogging for 20%, all adding up to 100%.
Because the Capstone is a discussion-oriented class, I expect you to attend and be an active participant in all classes.
The Economics Department requires all capstone students to write a 30 minute essay on globalization as part of our self-assessment for SACS/AACSB reaccreditation. I will provide the prompt, and grade and summarize your essays on the basis of “exceeds/meets/does not meet” expectations. I make it a small part of your course grade to encourage you to take it seriously.
I do not hold fixed office hours. Instead, I will will suggest time slots but leave the onus on you to make appointments. My office is HU125B (next to the wheelchair entrance on the lower level of Huntley), but I often prefer to meet at Lexington Coffee on Washington Street (in which case your beverage is on me). I check email frequently, and prefer that as a means of communication. My cell is 460-6288, a local call; please don’t text me, except (for example) to note that you’re running late (or to check whether I’m running late) to a scheduled appointment.
As necessary (a function of enrollments) I will devote one or two class periods during the term to office hours. In addition, I urge you to make regular appointments with the staff of the Williams School Communication Center, whose offices are in the basement of Huntley. Note this requires planning, as you need to book an appointment in advance, and need to submit a draft from which they can work.
You probably have written at least one research paper in an economics class. Nevertheless, we will work in class and one-on-one in office hours to select a topic, find relevant academic research (journal articles and working papers), and locate data. My input does not guarantee a good paper. It will be up to you to develop a strong topic that interests you sufficiently to put in lots of hours. You will need to discern when sources are strong, whether the models you are considering are appropriate to your question, and which empirical approaches make sense.
I ask you to:
- submit a formal topic statement
- follow it up with a working bibliography and then proper literature survey;
- add a section on the theory that undergirds your analysis; and
- indicate how you would support it empirically, including locating relevant data.
I will fix the due dates once the term commences; all will be duly reflected on the Schedule on this web site. The easiest way to earn a low grade in the class is to fail to meet these deadlines.
I treat it as your responsibility to print out and convey to me a physical copy of all your work. Please also submit digital copies. Those serve as a backup, and also mean that I will have access to your latest draft if I am out of town for a meeting. (It also happens to provide a time stamp for when you submitted work.)
I put links to readings on the schedule on this WordPress site; we also blog here. I thus make each of you an “author” so that you can post and edit content. (I maintain my personal blog at http://autosandeconomics.blogspot.com, and with your permission will edit and repost select class posts there.
I take it as a given that you will uphold the Honor Code. Even though you’re seniors, and know that all coursework is covered, it’s still a good reminder to pledge things you submit. I will go through citation standards and paper formats as the term progresses. (See also the relevant Leyburn Library pages.)
Note that in the business world you don’t send an important memo to your boss (to hand to his/her boss, sometimes without acknowledging you!) without input from colleagues (and in all likelihood your boss). Let me reiterate: you will benefit from the editing skills of the staff of the writing center, and you should cultivate a network of peers, relatives, even other faculty to help you refine your work. You have hopefully learned from your classmates, faculty, summer co-workers and relatives these past four years. Feel free to incorporate all that!! The best work stands on the shoulders of giants (Google’s motto).Note
As per university policy, if you have been approved for special accommodations, please speak to me in private at the start of the term.
Source: Robert K. Merton, On the Shoulders of Giants: A Shandean Postscript. Free Press (1965). Leyburn QC16.N7 M38 1985 [This is also a cautionary internet tale: I spent time tracking down this sequence in Japan in 2007, and found that many seemingly independent sources in fact cited Merton: if he got any of this wrong, lots of other people are then also wrong!]