We consider inference about a scalar coefficient in a linear regression with spatially correlated errors. Recent suggestions for more robust inference require stationarity of both regressors and dependent variables for their large sample validity. This rules out many empirically relevant applications, such as difference-in-difference designs. We develop a robustified version of the SCPC method of Müller and Watson (2021) that addresses this challenge. We find that the method has good size properties in a wide range of Monte Carlo designs that are calibrated to real world applications, both in a pure cross sectional setting, but also for spatially correlated panel data. We provide numerically effcient methods for computing the associated spatial-correlation robust test statistics, critical values and confidence intervals.Spatial Correlation Robust Inference. (Joint with MARK WATSON.) Link to Stata implementation. Link to Matlab implementation. Slides.
We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar `estimator plus and minus a standard error times a critical value' form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given `worst-case' spatial covariance model. The critical value is chosen to ensure coverage in a benchmark parametric model for the spatial correlations. The method is shown to control coverage in large samples whenever the spatial correlation is weak, i.e., with average pairwise correlations that vanish as the sample size gets large. We also provide results on correct coverage in a restricted but nonparametric class of strong spatial correlations, as well as on the efficiency of the method. In a design calibrated to match economic activity in U.S. states the method outperforms previous suggestions for spatially robust inference about the population mean.A More Robust t-Test. (Previously presented under the title "Inference for the Mean".) Replication files. Link to Stata implementation. Slides.
Standard inference about a scalar parameter estimated via GMM amounts to applying a t-test to a particular set of observations. If the number of observations is not very large, then moderately heavy tails can lead to poor behavior of the t-test. This is a particular problem under clustering, since the number of observations then corresponds to the number of clusters, and heterogeneity in cluster sizes induces a form of heavy tails. This paper combines extreme value theory for the smallest and largest observations with a normal approximation for the average of the remaining observations to construct a more robust alternative to the t-test. The new test is found to control size much more successfully in small samples compared to existing methods. Analytical results in the canonical inference for the mean problem demonstrate that the new test provides a refinement over the full sample t-test under more than two but less than three moments, while the bootstrapped t-test does not.Low-Frequency Analysis of Economic Time Series. (Joint with MARK WATSON.) Draft chapter for Handbook of Econometrics, Volume 7, edited by S. Durlauf, L.P. Hansen, J.J. Heckman, and R. Matzkin.
We propose a Bayesian procedure for exploiting small, possibly long-lag linear predictability in the innovations of a finite order autoregression. We model the innovations as having a log-spectral density that is a continuous mean-zero Gaussian process of order 1/sqrt(T). This local embedding makes the problem asymptotically a normal-normal Bayes problem, resulting in closed-form solutions for the best forecast. When applied to data on 132 U.S. monthly macroeconomic time series, the method is found to improve upon autoregressive forecasts by an amount consistent with the theoretical and Monte Carlo calculations.Forthcoming and Published Papers
Refining the Central Limit Theorem Approximation via Extreme Value Theory, Statistics & Probability Letters 155 (2019), 1 – 7.
Nearly Weighted Risk Minimal Unbiased Estimation, Journal of Econometrics, 209 (2019), 18 – 34. (Joint with YULONG WANG.) Replication files. Slides.
Long-Run Covariability, Econometrica 86 (2018), 775 – 804. Mark Watson’s Fisher-Schultz lecture 2016. (Joint with MARK WATSON.) Appendix and Replication files. Slides.
Low-Frequency Econometrics. In Advances in Economics and Econometrics: Eleventh World Congress of the Econometric Society, Volume II, ed. by B. Honoré, and L. Samuelson, Cambridge University Press (2017), 53 – 94. (Joint with MARK WATSON.) Replication files. Slides.
Fixed-k Asymptotic Inference about Tail Properties, Journal of the American Statistical Association, 112 (2017), 1334 – 1343. (Joint with YULONG WANG.) Replication files. Slides.
Credibility of Confidence Sets in Nonstandard Econometric Problems, Econometrica 84 (2016), 2183 – 2213. (Joint with ANDRIY NORETS.) Supplementary Appendix. Slides.
Measuring Uncertainty about Long-Run Predictions. Review of Economic Studies 83 (2016), 1711 – 1740. (Joint with MARK WATSON.) Supplementary Appendix. Replication files. Slides.
Coverage Inducing Priors in Nonstandard Inference Problems. Journal of the American Statistical Association 111 (2016), 1233 – 1241. (Joint with ANDRIY NORETS.) Supplementary Appendix.
Inference with Few Heterogenous Clusters, Review of Economics and Statistics 98 (2016), 83 – 96. (Joint with RUSTAM IBRAGIMOV.) Supplementary Appendix. Replication files. Slides.
Nearly Optimal Tests when a Nuisance Parameter is Present Under the Null Hypothesis, Econometrica 83 (2015), 771 – 811. (Joint with GRAHAM ELLIOTT and MARK WATSON.) Supplementary Appendix. Replication files. Slides.
HAC Corrections for Strongly Autocorrelated Time Series, Journal of Business & Economic Statistics 32 (2014), 311 – 322. Comments and Rejoinder. Slides.
Pre and Post Break Parameter Inference, Journal of Econometrics 180 (2014), 141 – 157. (Joint with GRAHAM ELLIOTT.) 2012 working paper version. Slides.
Risk of Bayesian Inference in Misspecified Models, and the Sandwich Covariance Matrix, Econometrica 81 (2013), 1805 – 1849. Slides.
Low-Frequency Robust Cointegration Testing, Journal of Econometrics 174 (2013), 66 – 81. (Joint with MARK WATSON.) Slides.
Measuring Prior Sensitivity and Prior Informativeness in Large Bayesian Models, Journal of Monetary Economics 59 (2012), 581 – 597. Slides.
Efficient Tests under a Weak Convergence Assumption, Econometrica 79 (2011), 395 – 435. (Formerly circulated under the title "An Alternative Sense of Asymptotic Efficiency".) Slides.
Estimation of the Parameter Path in Unstable Time Series Models, Review
Economic Studies 77 (2010), 1508 – 1539. (Joint with PHILIPPE-EMMANUEL
PETALAS.) Supplement. Correction. Slides.
t-statistic Based Correlation and Heterogeneity Robust Inference, Journal of Business & Economic Statistics 28 (2010), 453 – 468. (Joint with RUSTAM IBRAGIMOV.) Supplement. Slides.
Valid Inference in Partially Unstable GMM Models, Review of Economic Studies 76 (2009), 343 – 365. (Joint with HONG LI.) Slides.
Testing Models of Low-Frequency Variability, Econometrica 76 (2008), 979 – 1016. (Joint with MARK WATSON.) Slides.
The Impossibility of Consistent Discrimination between I(0) and I(1) Processes, Econometric Theory 24 (2008), 616 – 630. Slides.
A Theory of Robust Long-Run Variance Estimation, Journal of Econometrics 141 (2007), 1331 – 1352. (Substantially different 2004 working paper).
Confidence Sets for the Date of a Single Break in Linear Time Series Regressions, Journal of Econometrics 141 (2007), 1196 – 1218. (Joint with GRAHAM ELLIOTT.)Minimizing the Impact of the Initial Condition on Testing for Unit Roots, Journal of Econometrics 135 (2006), 285 – 310. (Joint with GRAHAM ELLIOTT.)
Efficient Tests for General Persistent Time Variation in Regression Coefficients, Review of Economic Studies 73 (2006), 907 – 940. Formerly circulated under the title “Optimally Testing General Breaking Processes in Linear Time Series Models”. (Joint with GRAHAM ELLIOTT.)
Are Forecasters Reluctant to Revise their Predictions? Some German Evidence, Journal of Forecasting 25 (2006), 401 – 413. (Joint with GEBHARD KIRCHGÄSSNER.)
Size and Power of Tests for Stationarity in Highly Autocorrelated Time Series, Journal of Econometrics 128 (2005), 195 – 213.
Tests for Unit Roots and the Initial Condition, Econometrica 71 (2003), 1269 – 1286. (Joint with GRAHAM ELLIOTT.)Ecological Tax Reform and Involuntary Unemployment: Simulation Results for Switzerland, Schweizerische Zeitschrift für Volkswirtschaft und Statistik 134 (1998), 329 – 359. (Joint with GEBHARD KIRCHGÄSSNER and MARCEL SAVIOZ.)
Comment on “HAR Inference: Recommendations for Practice” by E. Lazarus, D. J. Lewis and J. H. Stock, Journal of Business & Economic Statistics 36 (2018), 563 – 564.
Comment on “Unit Root Testing in Practice: Dealing with Uncertainty over the Trend and Initial Condition” by D. I. Harvey, S. J. Leybourne and A. M. R. Taylor, Econometric Theory 25 (2009), 643 – 648.