This is a guest post by "Dr. Phil of Economics", which is the pseudonym of an econ grad student whose identity is probably not that hard to figure out if you look hard enough.
The title is a reference to an internet meme popular on the support group website Econ Job Market Rumors. For those of you who don't speak meme, it's how you'd type "Time series econometrics no good :p" if you were typing quickly and carelessly. Thus, we can expect this post to be a critique of time-series econometrics, at least the way it is commonly employed in the economics literature. After reading it several times, I'm fairly sure that's what it actually is. Anyway, I'll let Phil take it from here:
Time Series Econometrics No Giod P
Thank you for joining us today. I’m Dr. Phil of Economics and I want you to take a deep breath, find a safe place, sit right down, and think hard about how you’ve been using your time series econometrics. I want you to take a good long look in the mirror and ask yourself if you’ve been responsible with your implicit assumptions and if you’ve been honest with yourself about the strength of your proxies.
The Lucas Critique: A Refresher
What’s the Lucas Critique? In 1976, Economics Nobel Laureate (1995) Robert Lucas, Jr. told the lot of us to hold our horses when it comes to drawing policy recommendations from econometric conclusions. The intuition behind the argument is easy enough: historical relationships are determined jointly by measured variables and institutions. It’s erroneous to assume that changing the institutions will have no effect on the relationship between the variables. The rise of microfoundations in macroeconomics was a response to, inter alia, Lucas’s warning.
It’s high time we faced facts here, people. The Lucas Critique is really just the beginning of the problem. With all due respect to the esteemed Robert Lucas, implicit policy parameters lurk in the left-hand variables just as much as in the right. It’s time to get real and consider that the act of measuring something might well inflate its importance, justly or unjustly.
What do I mean by this? It’s the econometric version of dietary gluten. Think back ten years ago. Did you have the first idea what gluten was in 2004? Did you count your carbs in 1994? Did you wring your hands over NGDP in 1984? Or how about stratospheric ozone in 1974? These things all existed, but the act of measuring them made them more than merely relevant—their measurement created categories. No longer is it just “trade” anymore, it’s either “domestic” or “international” trade. Right or wrong, national income accounting (to pick a handy example) encourages analysts to consider national borders as if they have some special economic significance. And if the pencil pushers quantify and measure it, you can bet someone’s career is eventually going to be on the line for it, one way or another.
Your Dependent Variable Matters
Responsibility is what separates adults from children. One of your duties as a parent is to let your kids know about the dangers of convenience sampling. You already know this, but what you may not know is that when you choose your dependent variable, you’re doing so for very similar reasons to pollsters who hang out on street corners, accosting passersby. The data are there and easy to get. Why not just go ahead and use ‘em?
“Why not” is because data are not “given” by an impartial spectator. Data are gathered by folks just like you or me or Stevie Nicks, folks who had to make the hard choices of what to record, of how frequently to record them, and of how to market them. Even something as seemingly foundational as GDP is still the result of deliberate choice. Some forgotten (just kidding: it was Simon Kuznets, a household name if you’re in the fold) economist wanted a nice shorthand way to measure national production, so BAM, we get national income accounting. A few years later, GDP is adopted as an official metric. Before long, political leaders are running contests with each other using the darn thing as a finishing line in a race that never ends.
I don’t mean to pick on GDP or how it’s metastasized from metric to target. We all know about this, and we all accept it without too much fuss. Endogeneity is a non-diversifiable risk. Have the integrity to accept that and the courage to continue in the face of adversity. This is a safe place—I won’t judge you, but you have to conquer your fears before your fears conquer you. But let’s face facts here, people. The more you pretend that the act of measuring something won’t alter the way it’s measured, the more you’re going to look like a fool. You have to respect you before anyone else will.
Your Sample Frequency Matters
Measurement frequency is a choice too, and it’s a choice that can unfortunately make irrelevant results look meaningful. Let me tell you a story of something I saw once. I won’t mention the name of the article or the journal so that I don’t embarrass the author, but let’s just say that the primary focus of the journal is one part law, and two parts economics and the piece made a sad attempt to demonstrate price fixing in retail gasoline markets in some Canadian backwater (forgive the pleonasm). After some questionable data composition, the authors concluded that the local cartel was a rousing success to the tune of 2 cents (Canadian) a gallon. Now, I’m a curious guy. So I asked myself, “Phil, is two cents a gallon really all that much?” Like a curious cat on a hot tin roof, I pulled up a website that lets you watch real-time updates to retail gasoline prices. Can you guess what I found? Those prices were jumping around like a one-legged cat trying to bury turds on a waffle iron. Now it’s true that in the peer-reviewed study I looked at, the authors adequately defended the decision to take weekly price snapshots (it was for practical restrictions), but think about what you’re doing to your family and your profession when your treatment yields an effect that gets snowed in by ordinary noise at a quick, casual glance.
Even if you don’t follow the empirical economics literature, you’ve seen this yourself every dang time you turn on a report about the stock market. Those Dow Jones Industrial Average numbers you hear? If the closing bell has already rung, that’s just the index as it happens to be once trading stops for the day. When a reporter tells you the Dow is up 100 points at the end of the day, how do you interpret it? Is that a lot? Should you believe the tag line that follows? It’s almost never “the stock market moved today”, it’s always “the stock market moved today on the release of some report you didn’t think to care about until we just now mentioned it.”
Settle down now. Collect your thoughts before you do something that will hurt somebody. It’s bad, but it’s not that bad.
Your Model Assumptions Really Matter
Even modest little ARIMA requires that you get your dummy right. But how do you know for sure if it’s the consumer durables report or the U3 numbers or the forward guidance or whatever that’s actually driving the change in your output variable that might just be plain old statistical noise anyway? Just because someone writes down “this is the treatment effect” don’t make it so. Just because some loud television personality barks it at you doubly don’t make it so (take it from me). So what do we do? We look to theory to guide us. We think there’s some good reason for interest rates to be linked to the amount of money in circulation (to pick something totally non-controversial…) and there you go: M1 is our big independent variable of interest. We’ve got a model that tells us what to look for.
What else does the model do? The model gives you something to compare your results to. In a laboratory setting, researchers take great pains to set up a control group that is statistically identical to the treatment group with the single exception of the one element under study. The world is not a laboratory, so to answer one of the cardinal rules of econometrics “compared to what?,” you better darn well make yourself a convincing counterfactual. Once you futz past all the gassy accusations, this is one of the big reasons Macro No Giod P: the underlying Platonic capital-T Truth alternative state of the world is forever a flickering shadow on the wall of our cave. But hey, that’s the best we’ve got, so just go with it, okay? Integrity means admitting your shortcomings and still making the best of a tough situation.
That’s a whole bushel of low-hanging fruit right there, maybe more than you want to chow down on if you haven’t already thought about it before. Just try to remember that there are a few questions you need to answer for your econometrics to be worth a tinker’s damn. First and foremost: who cares? Why is the thing you measure interesting? Second: are your results salient? That price fixing paper I reviewed had “statistically significant” results, but that term of art is very different from its colloquial use. I assure you that if you want the cold shoulder on Valentine’s Day, all you have to tell your statistician sweetie is that he’s your statistically significant other. The two cent price fixing scandal was statistically significant (enough to get past a few friendly referees anyway), but it was hardly salient. Third: please reject alternative hypotheses. I’d venture a weak guess that the reason my kind host here has no ahpinion is because this last one is spectacularly difficult, even with the finest GARCH specifications and discontinuity analyses available.
Honesty is a virtue. Have the honesty to admit the limits of your time series methods. You owe it to the people who love you, and that includes me.
You can follow Dr. Phil of Economics on Twitter at @DrPhilofEconomi
No relation to Dr. Phil McGraw or Peteski Productions.