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We prove that a subtle but substantial bias exists in a common measure of the conditional dependence of present outcomes on streaks of past outcomes in sequential data. The magnitude of this streak selection bias generally decreases as the sequence gets longer, but increases in streak length, and remains substantial for a range of sequence lengths often used in empirical work. We observe that the canonical study in the influential hot hand fallacy literature, along with replications, are vulnerable to the bias. Upon correcting for the bias, we find that the longstanding conclusions of the canonical study are reversed.

Ecologists widely use the log response ratio for summarizing the outcomes of studies for meta-analysis. However, little is known about the sampling distribution of this effect size estimator. Here I show with a Monte Carlo simulation that the log response ratio is biased when quantifying the outcome of studies with small sample sizes, and can yield erroneous variance estimates when the scale of study parameters are near zero. Given these challenges, I derive and compare two new estimators that help correct this small-sample bias, and update guidelines and diagnostics for assessing when the response ratio is appropriate for ecological meta-analysis. These new bias-corrected estimators retain much of the original utility of the response ratio and are aimed to improve the quality and reliability of inferences with effect sizes based on the log ratio of two means.

The affine dynamic term structure model (DTSM) is the canonical empirical finance representation of the yield curve. However, the possibility that DTSM estimates may be distorted by small-sample bias has been largely ignored. We show that conventional estimates of DTSM coefficients are indeed severely biased, and this bias results in misleading estimates of expected future short-term interest rates and of long-maturity term premia. We provide a variety of bias-corrected estimates of affine DTSMs, for both maximally flexible and overidentified specifications. Our estimates imply interest rate expectations and term premia that are more plausible from a macrofinance perspective. This article has supplementary material online.

This paper extends the minimum-chi-square estimation for affine term structure models to a regime switching framework, and corrects the estimation bias in the regime switching dynamic term structure model. Biases arise as a result of highly persistent bond yields, and bias correction changes the decomposition of medium- and long-term forward rates. The bias-corrected expected short rate accounts for the pronounced moves in forward rates during the 1979–1982 monetary experiment and the financial crisis. The bias-corrected term premium becomes counter-cyclical and more negatively correlated with the short-term yield. Monte Carlo simulation shows that the decomposition of forward rates is more accurate after bias correction.

Grouping models are widely used in economics but are subject to finite sample bias. I show that the standard errors-in-variables estimator is exactly equivalent to the jackknife instrumental variables estimator and use this relationship to develop an estimator which, unlike the standard errors-in-variables estimator, is unbiased in finite samples. The theoretical results are demonstrated using Monte Carlo experiments. Finally, I implement a model of intertemporal male labor supply using microdata from the U.S. Census. There are sizable differences in the wage elasticity across estimators, showing the practical importance of the theoretical issues even when the sample size is quite large.

We investigate robustness in the logistic regression model. Copas has studied two forms of robust estimator: a robust-resistant estimate of Pregibon and an estimate based on a misclassification model. He concluded that robust-resistant estimates are much more biased in small samples than the usual logistic estimate is and recommends a bias-corrected version of the misclassification estimate. We show that there are other versions of robust-resistant estimates which have bias often approximately the same as and sometimes even less than the logistic estimate; these estimates belong to the Mallows class. In addition, the corrected misclassification estimate is inconsistent at the logistic model; we develop a simple consistent modification. The modified estimate is a member of the Mallows class but, unlike most robust estimates, it has an interpretable tuning constant. The results are illustrated on data sets featuring different kinds of outliers.

Accurately quantifying extreme rainfall is important for the design of hydraulic structures, for flood mapping and zoning and for disaster management. In order to produce maps of estimates of 25-year rainfall return levels in Brazil, we selected 893 shorter and 104 longer rainfall time series from the Agência Nacional de Águas (ANA), and applied the framework of extreme value theory. Care was needed to reduce the impact of poor data. Estimates of the shape parameter of the extreme-value model fitted to rainfall data are typically biased, so we discuss an empirical correction that takes into account not only the sample-size bias, but also a so-called penultimate approximation that we use to inform a Bayesian spatial latent variable model for the annual rainfall maxima. This model accounts for subtle patterns of spatial variation in the data and provides plausible return level estimates.

Prior research (Belsley, 1997) has established that the common tests for single orders of serial correlation (e.g., Durbin–Watson, artificial regression) are badly distorted and result in grossly misleading tests in small samples. A corrected t-statistic has been derived that removes these difficulties, but it cannot be applied to joint tests. This research provides the needed generalizations. First it shows, to no surprise, that the same distortions plague the F-statistic typically used for testing joint orders of serial correlation with artificial regressions. And second it derives a corrected F-statistic that provides acceptable tests for arbitrarily stipulated joint orders of serial correlation. The test procedure is detailed and exemplar code provided.