I have always been fascinated by distribution-free non-parametric tests, or randomization tests, or Monte Carlo tests -- whatever you want to call them. (For example, I used some in ancient work like Diebold-Rudebusch 1992.) They seem almost too good to be true: exact finite-sample tests without distributional assumptions! They also still seem curiously underutilized in econometrics, notwithstanding, for example, the path-breaking and well-known contributions over many decades by Jean-Marie Dufour, Marc Hallin, and others.

For the latest, see the fascinating new contribution by Jean-Marie Dufour and Richard Luger. They show how to use randomization to perform simple tests of the null of linearity against the alternative of Markov switching in dynamic environments. That's a very hard problem (nuisance parameters not identified under the null, singular information matrix under the null), and several top researchers have wrestled with it (e.g., Garcia, Hansen, Carasco-Hu-Ploberger). Randomization delivers tests that are exact, distribution-free, and *simple*. And power looks pretty good too.
Some twenty years ago, a leading Bayesian econometrician startled me during an office visit at Penn. We were discussing Bayesian vs. frequentist approaches to a few things, when all of a sudden he declared that "There must be something about Bayesian analysis that stifles creativity. It seems that frequentists invent all the great stuff, and Bayesians just trail behind, telling them how to do it right".

His characterization rings true in certain significant respects, which is why it's so funny. But the intellectually interesting thing is that it doesn't have to be that way. As Chris Sims notes in a recent communication:
... frequentists are in the habit of inventing easily computed, intuitively appealing estimators and then deriving their properties without insisting that the method whose properties they derive is optimal. ... Bayesians are more likely to go from model to optimal inference, [but] they don't have to, and [they] ought to work more on Bayesian analysis of methods based on conveniently calculated statistics.

See Chris' thought-provoking unpublished paper draft, "Understanding Non-Bayesians".

[As noted on Chris' web site, he wrote that paper for the Oxford University Press *Handbook of Bayesian Econometrics*, but he "withheld [it] from publication there because of the Draconian copyright agreement that OUP insisted on --- forbidding posting even a late draft like this one on a personal web site."]
Check out Barnichon-Brownlees (2017) (BB). As proposed and developed in Jorda (2005), they estimate impulse-response functions (IRF's) directly by projecting outcomes on estimates of structural shocks at various horizons, as opposed to inverting a fitted autoregression. The BB enhancement relative to Jorda is the effective incorporation of a smoothness prior in IRF estimation. (Notice that the traditional approach of inverting a low-ordered autoregression automatically promotes IRF smoothness.) In my view, smoothness is a natural IRF shrinkage direction, and BB convincingly show that it's likely to enhance estimation efficiency relative to Jorda's original approach. I always liked the idea of attempting to go after IRF's directly, and Jorda/BB seems appealing.
Sendhil Mullainathan gave an entertaining plenary talk on machine learning (ML) in finance, in Chicago last Saturday at the annual American Finance Association (AFA) meeting. (Many hundreds of people, standing room only -- great to see.) Not much new relative to the posts here, for example, but he wasn't trying to deliver new results. Rather he was trying to introduce mainstream AFA financial economists to the ML perspective.

[Of course ML perspective and methods have featured prominently in time-series econometrics for many decades, but many of the recent econometric converts to ML (and audience members at the AFA talk) are cross-section types, not used to thinking much about things like out-of-sample predictive accuracy, etc.]

Anyway, one cute and memorable thing -- good for teaching -- was Sendhil's suggestion that one can use the canonical penalized estimation problem as a taxonomy for much of ML. Here's my quick attempt at fleshing out that suggestion.

Consider estimating a parameter vector \( \theta \) by solving the penalized estimation problem,

\( \hat{\theta} = argmin_{\theta} \sum_{i} L (y_i - f(x_i, \theta) ) ~~s.t.~~ \gamma(\theta) \le c , \)

or equivalently in Lagrange multiplier form,

\( \hat{\theta} = argmin_{\theta} \sum_{i} L (y_i - f(x_i, \theta) ) + \lambda \gamma(\theta) . \)

Then:

(1) \( f(x_i, \theta) \) is about the modeling strategy (linear, parametric non-linear, non-parametric non-linear (series, trees, nearest-neighbor, kernel, ...)).

(2) \( \gamma(\theta) \) is about the type of regularization. (Concave penalty functions non-differentiable at the origin produce selection to zero, smooth convex penalties produce shrinkage toward 0, the LASSO penalty is both concave and convex, so it both selects and shrinks, ...)

(3) \( \lambda \) is about the strength of regularization.

(4) \( L(y_i - f(x_i, \theta) ) \) is about predictive loss (quadratic, absolute, asymmetric, ...).

Many ML schemes emerge as special cases. To take just one well-known example, linear regression with regularization by LASSO and regularization strength chosen to optimize out-of-sample predictive MSE corresponds to (1) \( f(x_i, \theta)\) linear, (2) \( \gamma(\theta) = \sum_j |\theta_j| \), (3) \( \lambda \) cross-validated, and (4) \( L(y_i - f(x_i, \theta) ) = (y_i - f(x_i, \theta) )^2 \).
A very Happy New Year to all!

I get no pleasure from torpedoing anything, and "torpedoing" is likely exaggerated, but nevertheless take a look at "A Torpedo Aimed Straight at HMS Randomista". It argues that many econometric randomized controlled trials (RCT's) are seriously flawed -- not even *internally *valid -- due to their failure to use double-blind randomization. At first the non-double-blind critique may sound cheap and obvious, inviting you to roll your eyes and say "get over it". But ultimately it's not.

Note the interesting situation. Everyone these days is worried about *external *validity (extensibility), under the implicit *assumption *that internal validity has been achieved (e.g., see this earlier post). But the non-double-blind critique makes clear that even internal validity may be dubious in econometric RCT's as typically implemented.

The underlying research paper, "Behavioural Responses and the Impact of New Agricultural Technologies: Evidence from a Double-Blind Field Experiment in Tanzania", by Bulte *et al*., was published in 2014 in the *American Journal of Agricultural Economics*. Quite an eye-opener.

Here's the abstract:

Randomized controlled trials in the social sciences are typically not double-blind, so participants know they are “treated” and will adjust their behavior accordingly. Such effort responses complicate the assessment of impact. To gauge the potential magnitude of effort responses we implement an open RCT and double-blind trial in rural Tanzania, and randomly allocate modern and traditional cowpea seed-varieties to a sample of farmers. Effort responses can be quantitatively important––for our case they explain the entire “treatment effect on the treated” as measured in a conventional economic RCT. Specifically, harvests are the same for people who know they received the modern seeds and for people who did not know what type of seeds they got, but people who knew they received the traditional seeds did much worse. We also find that most of the behavioral response is unobserved by the analyst, or at least not readily captured using coarse, standard controls.