"Financial econometrics has become an increasingly popular research field. In this paper we review a few parametric and nonparametric models and methods used in this area. After introducing several widely used continuous-time and discrete-time models, we study in detail dependence structures of discrete samples, including Markovian property, hidden Markovian structure, contaminated observations, and random samples. We then discuss several popular parametric and nonparametric estimation methods. To avoid model mis-specification, model validation plays a key role in financial modeling. We discuss several model validation techniques, including pseudo-likelihood ratio test, nonparametric curve regression based test, residuals based test, generalized likelihood ratio test, simultaneous confidence band construction, and density based test. Finally, we briefly touch on tools for studying large sample properties."

"Haavelmo was the first to recognize the capacity of economic models to guide poli- cies. This paper describes some of the barriers that Haavelmo’s ideas have had (and still have) to overcome, and lays out a logical framework for capturing the relationships between theory, data and policy questions. The mathematical tools that emerge from this framework now enable investigators to answer complex policy and counterfactual questions using embarrassingly simple routines, some by mere inspection of the model’s structure. Several such problems are illustrated by examples, including misspecification tests, identification, mediation and introspection."

"We propose methods for inference on the average effect of a treatment on a scalar outcome in the presence of very many controls. Our setting is a partially linear regression model containing the treatment/policy variable and a large number $p$ of controls or series terms, with $p$ that is possibly much larger than the sample size $n$, but where only $s < n$ unknown controls or series terms are needed to approximate the regression function accurately. The latter sparsity condition makes it possible to estimate the entire regression function as well as the average treatment effect by selecting an approximately the right set of controls using Lasso and related methods. We develop estimation and inference methods for the average treatment effect in this setting, proposing a novel "post double selection" method that provides attractive inferential and estimation properties. In our analysis, in order to cover realistic applications, we expressly allow for imperfect selection of the controls and account for the impact of selection errors on estimation and inference. In order to cover typical applications in economics, we employ the selection methods designed to deal with non-Gaussian and heteroscedastic disturbances. We illustrate the use of new methods with numerical simulations and an application to the effect of abortion on crime rates."

"This article is about estimation and inference methods for high dimensional sparse (HDS) regression models in econometrics. High dimensional sparse models arise in situations where many regressors (or series terms) are available and the regression function is well-approximated by a parsimonious, yet unknown set of regressors. The latter condition makes it possible to estimate the entire regression function effectively by searching for approximately the right set of regressors. We discuss methods for identifying this set of regressors and estimating their coefficients based on $ell_1$-penalization and describe key theoretical results. In order to capture realistic practical situations, we expressly allow for imperfect selection of regressors and study the impact of this imperfect selection on estimation and inference results. We focus the main part of the article on the use of HDS models and methods in the instrumental variables model and the partially linear model. We present a set of novel inference results for these models and illustrate their use with applications to returns to schooling and growth regression."

"Measurement errors in economic data are pervasive and nontrivial in size. The presence of measurement errors causes biased and inconsistent parameter estimates and leads to erroneous conclusions to various degrees in economic analysis. While linear errors-in-variables models are usually handled with well-known instrumental variable methods, this article provides an overview of recent research papers that derive estimation methods that provide consistent estimates for nonlinear models with measurement errors. We review models with both classical and nonclassical measurement errors, and with misclassification of discrete variables. For each of the methods surveyed, we describe the key ideas for identification and estimation, and discuss its application whenever it is currently available." (Not read, reconsider to_teach tag later.)

DeLong is missing a trick. The rational-expectations dogmatist could simply insist that the true probability of an event like 2008 in 2008 _was_ 0.02%, and we were just unlucky.

I love how they just flat-out identify "econometrics" with "linear regression with Gaussian noise"; but it looks like a clean exposition with proofs.

The title is intriguing, but I can't find anything more about this...

I can rarely remember such _enthusiasm_ in a statistical paper.

As I was saying.

Of course, saying "we assume that correlated effects are absent" is, in this context at least, very much a "we assume we have a can opener" move.

I will be fascinated to see what of this is "Bayesian".

Exclusive focus on kernel methods, using Hayfield and Racine's np package for R.

I think you mis-spelled "smoothing spline". HTH. HAND.

My reaction to the first half is "preach it, brothers and sisters!" Perhaps inevitably, the constructive proposals of the 2nd half are less compelling.

Review of Hendry's _Econometrics: Alchemy or Science?_

Note: the Hansen of the abstract is not the author!

"Standard econometric model selection methods are based on four fundamental errors in approach: parametric vision, the assumption of a true DGP, evaluation based on fit, and ignoring the impact of model uncertainty on inference. Instead, econometric model selection methods should be based on a semiparametric vision, models should be viewed as approximations, models should be evaluated based on their purpose, and model uncertainty should be incorporated into inference methods. These problems have been examined individually, but not jointly, and my view is that future research into econometric model selection should attempt to address all four issues. "

"This is a draft of an incomplete first-year Ph.D. econometrics textbook. This manuscript may be printed and reproduced for individual or instructional use, but may not be printed for commercial purposes."

I very much like the approach of treating the likelihood ratio as an empirical process; why haven't I seen it before? (Also, the state-of-the-art in simulating Gaussian processes must be much better now than what Hansen was doing in '92, which would make this even more practical.)

I don't suppose anyone has an electronic copy they'd be willing to share?

Looks cool, though it depends on finding instrumental variables, which I always find sketchy.

Soon to be a book from Princeton. On first scan, it doesn't look wrong, exactly, so much as a completely ad hoc parametric form, with no reason to think it will generally be adequate, or evades the fundamental problem that observed correlations do not reflect underlying economic linkages (i.e., regression isn't causal inference). But Engle has a prize and I don't. To be shot after a fair trial.

(The published version has infinitely better typography, but $$$: http://dx.doi.org/10.1198/073500102288618487)

I am dubious.

This looks great, and sounds like what econometrics should be, but too rarely is. --- Nine months later: meh. Review: http://bactra.org/reviews/christensen-kiefer/

Beware your data.

The only good thing about this is that it might get some people to read Heckman's (truly impressive) work on the causes and amelioration of inequality.

The locus classicus.

Shorter Simon & Levy (1963): I am sickened by the weakness of your model's goodness-of-fit test. (Does make me reconsider the many papers I still see using Cobb-Douglas...)

Color me boggled.

This is a joke, right? Right? Somebody please tell me this is a joke...

ETA: It's not a joke. It's now a negative example in ADA.

Ungated version: http://forum.johnson.cornell.edu/faculty/waldman/autism-waldman-nicholson-adilov.pdf

1992 paper on abuses of hypothesis testing --- specifically evidence that even the unrejected null hypotheses in most economics papers are in fact false.

Comparison across surveys, and robustness of psychometric (item-response) models used in the data processing