"A One-Covariate at a Time, Multiple Testing Approach to Variable Selection in High-Dimensional Linear Regression Models", by Alexander Chudik, George Kapetanios and M. Hashem Pesaran, forthcoming in Econometrica, February 2018.

Abstract: This paper provides an alternative approach to penalised regression for model selection in the context of high dimensional linear regressions where the number of covariates is large, often much larger than the number of available bbservations. We consider the statistical significance of individual covariates one at a time, whilst taking full account of the multiple testing nature of the inferential problem involved. We refer to the proposed method as One Covariate at a Time Multiple Testing (OCMT) procedure, and use ideas from the multiple testing literature to control the probability of selecting the approximating model, the false positive rate and the false discovery rate. OCMT is easy to interpret, relates to classical statistical analysis, is valid under general assumptions, is faster to compute, and performs well in small samples. The usefulness of OCMT is also illustrated by an empirical application to forecasting U.S. output growth and inflation.
JEL Classifications: C52, C55
Key Words: One covariate at a time, multiple testing, model selection, high dimensionality, penalised regressions, boosting, Monte Carlo experiments.
Full Text: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/fp18/OCMT_CPK_2018Feb23.pdf
MC Supplement: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/fp18/MC_Supplement_OCMT_CPK_2018Feb23.pdf

"Half-Panel Jackknife Fixed Effects Estimation of Linear Panels with Weakly Exogenous Regressors", by Alexander Chudik, M. Hashem Pesarann and Jui-Chung Yang, SSRN Working Paper No. 281, forthcoming in Journal of Applied Econometrics, January 2018

Abstract: This paper considers estimation and inference in fixed effects (FE) linear panel regression models with lagged dependent variables and/or other weakly exogenous (or predetermined) regressors when N (the cross section dimension) is large relative to T (the time series dimension). The paper first derives a general formula for the bias of the FE estimator which is a generalization of the Nickell type bias derived in the literature for the pure dynamic panel data models. It shows that in the presence of weakly exogenous regressors, inference based on the FE estimator will result in size distortions unless N/T is suffciently small. To deal with the bias and size distortion of FE estimator when N is large relative to T, the use of half-panel Jackknife FE estimator is proposed and its asymptotic distribution is derived. It is shown that the bias of the proposed estimator is of order [code], and for valid inference it is only required that N/T[code], as N, T [code] jointly. Extensions to panel data models with time effects (TE), for balanced as well as unbalanced panels, are also provided. The theoretical results are illustrated with Monte Carlo evidence. It is shown that the FE estimator can suffer from large size distortions when N > T, with the proposed estimator showing little size distortions. The use of half-panel jackknife FE-TE estimator is illustrated with two empirical applications from the literature.
JEL Classifications: C32, E17, E32, F44, F47, O51, Q43.
Key Words: Panel Data Models, Weakly Exogenous Regressors, Lagged Dependent Variable, Fixed Effects, Time Effects, Unbalanced Panels, Half-Panel Jackknife, Bias Correction
Full Text: https://doi.org/10.1002/jae.2623
Readme file: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/fp18/readme_file_for_data_and_codes.txt
Data for the Empirical Section: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/fp18/cpy-data.zip
Computer codes: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/fp18/cpy-progs.zip
 

"Tests of Policy Interventions in DSGE Models", by M. Hashem Pesaran and Ron P. Smith, forthcoming in Oxford Bulletin of Economics and Statistics, October 2017.

Abstract: This paper considers tests of the effectiveness of a policy intervention, de…ned as a change in the parameters of a policy rule, in the context of a macroeconometric dynamic stochastic general equilibrium (DSGE) model. We consider two types of intervention, fi…rst the standard case of a parameter change that does not alter the steady state, and second one that does alter the steady state, e.g. the target rate of infl‡ation. We consider two types of test, one a multi-horizon test, where the post-intervention policy horizon, H, is small and fi…xed, and a mean policy effect test where H is allowed to increase without bounds. The multi-horizon test requires Gaussian errors, but the mean policy effect test does not. It is shown that neither of these two tests are consistent, in the sense that the the power of the tests does not tend to unity as H → ∞, unless the intervention alters the steady state. This follows directly from the fact that DSGE variables are measured as deviations from the steady state, and the effects of policy change on target variables decay exponentially fast. We investigate the size and power of the proposed mean effect test by simulating a standard three equation New Keynesian DSGE model. The simulation results are in line with our theoretical fi…ndings and show that in all applications the tests have the correct size; but unless the intervention alters the steady state, their power does not go to unity with H.
JEL Classifications: C18, C54, E65.
Key Words: Counterfactuals, policy analysis, policy ineffectiveness test, macroeconomics.
Full Text: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/fp17/PS_on_PI_12_October_2017_main_paper.pdf
Supplement: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/fp17/PS_on_PI_12_October_2017_online_supplement.pdf