The Broadening Scope Of Economic Jobs

It used to be that being an economist, you’d end up working for the government, in academia, or in the financial sector. Nowadays, the market for an economist is much, much wider.

Take for example the following:

1. Google and Youtube are looking for quants, to help analyse “large, complex datasets”;
2. Amazon is looking for economists to, “apply the frontier of economic thinking to market design, pricing, forecasting, online advertising and other areas.”
3. Twitter is looking for help to, “analyze data and answer complex questions related to users, advertisers, and revenue.”
4. Qualcomm, who’s processors are at the heart of many smartphones today, are looking for someone to join their intellectual property team, to help advise on IP policy issues.

It’s a much wider job market than it used to be, and there’s never been a better time to be armed with an economics degree.

However, there are a couple of common threads running through all these “uncommon” jobs – a statistics/econometrics background is absolutely required, and a Masters degree is the minimum entry level qualification (PhD preferred).

Time to start hitting those books again, guys and gals.

Mark Thoma On Economists and The Future Of Economic Models

He’s responding to a Roger Martin essay (which you can read here) (excerpt):

Should economists be “imagineers” of our future?
By Mark Thoma

...I agree that macroeconomists need to fix their models. But I don’t think that predicting the future based upon “a straight-line projection of the past” is the problem...

...This year’s Nobel prize award to Thomas Sargent and the previous award to Robert Lucas were partly in recognition of their development of the tools and techniques that economists need to go beyond simply trying to extrapolate the future from the past, a procedure that can lead forecasters astray…

…people change their behavior in response to changes in the conditions they face. And this is one of the things that separate what researchers in the hard sciences do from the work of economists…

Brad DeLong Explains IS-LM

No much commentary on this one, just a good read for anyone interested in the subject (excerpt):

The Tribal Dislike of John Hicks and IS-LM: History of Economic Thought Edition

When you do economics and apply it to the real world, you start with the simplest possible model. Does that help you understand enough of the real world to satisfy you? If not, you complicate it by adding the most important thing that you had left out. Does that help you understand enough of the real world to satisfy you? If so, you use that model--and then when you want to go further you complicate it in its turn.

But at each stage in the process, you absorb the valid insights from your current model before you go on to complicate things further.

If you’re not familiar with the subject, or didn’t take macroeconomics at university, then the IS-LM model is Sir John Hicks attempt at explaining Keynes. Unfortunately, the underlying basis that he used for constructing the model was partially faulty (Keynes rejected the notion of a unique equilibrium), and Hick’s in fact repudiated IS-LM later in his career. By then it was too late, as a full generation of American economists had taken it up and hijacked the intellectual leadership of Keynesian economics, even if what they actually practiced was actually a bastardised amalgam of Keynes and neo-classical thought, commonly called the neo-classical synthesis.

Nevertheless, as DeLong points out, it doesn’t invalidate the potential usefulness of the model to examine policy issues, as long as it isn’t that badly wrong.

Uncertainty, Complexity, And The Problem With Economic Models

Nick Rowe has a headache (excerpt):

The Lucasian map is not the Hayekian territory

In defence of Lucas '72.

Take any macroeconomic model of a market economy with inefficient aggregate fluctuations. In fact, take any economic model where something bad might happen.

Assume that model is literally true.

The people in that model are idiots.

This conclusion follows immediately. If they weren't idiots, the people in the model would appoint the economist modelling the economy as central planner, who would tell them all what to do, and make them all better off.

The people in Lucas' '72 model are complete idiots for producing less because they don't realise there's a recession on.

The people in New Keynesian models are complete idiots for waiting for the Calvo fairy to give them permission to cut prices in a recession.

All models suffer this same problem. If the world really were as simple as the economic model of that world, people would figure it out, and wouldn't let bad things happen.

Of course, the discussion doesn’t go so far as to say that all economic analysis is useless – we know more about how people interact individually and in aggregate than we did before. But it’s always useful to keep in mind the inherent tension between a highly complex real world teeming with sometimes irrational individuals, and the oversimplified world of economic modelling. And you should always also take into account the biases of the modeller.

But, to quote George Box, “Essentially, all models are wrong, but some are useful."

The Determinants of FDI

Hot of the press at the NBER (abstract; emphasis added):

Determinants of Foreign Direct Investment

Empirical studies of bilateral foreign direct investment (FDI) activity show substantial differences in specifications with little agreement on the set of covariates that are (or should be) included. We use Bayesian statistical techniques that allow one to select from a large set of candidates those variables most likely to be determinants of FDI activity. The variables with consistently high inclusion probabilities are traditional gravity variables, cultural distance factors, parent-country per capita GDP, relative labor endowments, and regional trade agreements. Variables with little support for inclusion are multilateral trade openness, host country business costs, host-country infrastructure (including credit markets), and host-country institutions. Of particular note, our results suggest that many covariates found significant by previous studies are not robust.

Yes, Folks, You Too Can Do This At Home!

I’ve had a couple of questions about what software I’ve used in the analyses in this blog. I’m a big fan of EViews, so much so I actually bought a license for it – it’s been around a long time, is comparatively user-friendly, and has 99% of the functions most econometricians need.

Unfortunately it’s also priced to kill. Luckily I was still doing my Masters at the time so I qualified for a student discount, which takes 60% off the retail price. If anybody’s interested in a copy, you can contact Statworks in PJ – they’re the local distributors.

So what happens if you need to do some forecasting or econometric work, and don’t want to lose an arm and a leg? Excel just doesn’t cut it, even with some of the advanced plug-ins available – you might be able to do multivariate regressions, but diagnostics will get you stuck. And forget about more advanced estimation techniques such as VAR or ARCH.

The best alternative I’ve found, if you don’t want to deal with scripting or programming, is Gretl. Gretl is open-source, supports lots of platforms, and is fairly feature complete – in some ways it’s more powerful than EViews. It’s definitely not as user friendly (you can’t for instance just paste in a series from an external source), and graphs are very basic, but all the important bits are there and then some. The Windows version is available here.

But how do you use this thing? I’ll cover some of the basics in a series of posts, using real Malaysian data to illustrate. Stay tuned.

The Pitfalls of Econometrics

de minimis has an interesting post advocating using more econometric models to guide policy making in Malaysia. While I tend to lean that way myself (there's too much unsubstantiated rhetoric flung around the news and blogosphere for my taste), I don't want to be blind to the potential pitfalls and shortcomings of an applied econometric approach to policy. So this post is both to clarify some of the issues, as well as serve as a reminder to myself not to be too "assertive", as my wife puts it.

First is that econometric modeling (as etheorist remarked the other day) is really an art, not a science. There are many, many ways of looking at an economy and generating forecasts, from simple time series techniques to hideously complex dynamic general equilibrium models. So model choice and specification (as well as accompanying underlying assumptions), and not to mention the ideological bent of the modelers, can lead to very different conclusions about the state of the economy at any given time. The issue is compounded by Malaysia being such an open economy, which means that ideally, you'd have to incorporate all the major trade partners into your model as well.

Secondly, the evolving economic structure within a developing country means that even if you do come up with a model close to reality at some point in time, it might be out of date very quickly later on and you won’t know it until something goes wrong. This is one point where I would be critical of DOS: the Malaysian input-output tables haven’t been updated in years, and you need this to model intra-industry dynamics.

Thirdly, any econometric model necessarily uses historical data, which means there will always be an unobservable error component in any forecast in the presence of a current shock. A corollary of this is that, almost by definition, a trade shock such as we just suffered cannot be predicted on the basis of concurrent data. Models are more useful as a predictive guide to inventory driven recessions and business cycle downturns. You can of course use models to predict what happens when a shock occurs, but not when or if a shock will occur.

Fourth, data accuracy is inversely proportionate to the speed at which data is published. In other words, the faster you publish it, the larger the error rate. Where I think DOS can improve on that score is to follow the general practice in the EU, US and yes, Singapore, i.e. issue advanced, preliminary, and final estimates of major statistical series. The current practice of a 6-week to 8-week lag and quietly revising the historical series, isn't transparent (the loose hair around my workplace is testament to that). Data revisions should always be made clear, especially for national accounts data, which has to be revised even 2-3 years down the road.

One exception to this observation is financial sector data, which is available very quickly. (Side note: I've visited BNM to study their data gathering process, and I was a member of one of the teams responsible for implementing CCRIS in one of our banks - I am very impressed with BNM's operation in this instance. The disaggregated trial balance of the entire banking system is available at about t+4 after every month end – in other words, don’t be fooled by the monthly publishing schedule). (Side note to the side note: this is one reason why monetary policy is generally the first recourse in any crisis – you have better data much faster than real economy data).

However, I should point out that a 2-month to 3-month lag hits the sweet spot between accuracy and timeliness, and is fairly typical worldwide. China for instance tends to issue data on a 1 month lag, but subsequent revisions tend to be very large. Some Canadian series have no revisions at all, but you have to wait 6 months(!) to get them. I cannot fault DOS on that score, though they have made some absolute boo-boos before (pay attention to 2004-2005 trade data before and after revision, for instance).

Fourth: some of the most critical variables required for a predictive model are unobservable. For example, consumer and investor expectations have a big impact on private consumption and investment, but can’t be quantified. It’s possible to use proxies, such as consumer confidence or business expectations surveys, but these are subject to error as well.

Take all the factors above, and you shouldn’t be surprised that most whole economy econometric models have very little predictive power more than a quarter or two ahead, out of sample. I’d note that I’d be very surprised if the government doesn’t have on hand some whole economy econometric models, especially for trade and tax policies. Could more use be made of modeling? Absolutely! Just don’t fall for the promise that they’ll be a panacea and perfect guide to policy.