Another look at long memory in common stock returns

We apply the modified rescaled range test to the return series of 1,952 common stocks. The results indicate that long memory is not a widespread characteristic of these stocks. But logit models of the event of a test rejection reveal that rejections are linked to firms with large risk-adjusted average returns. The maximal moment of a return distribution is also found to influence the event of a rejection, but not in a way suggestive of moment-condition failure. Evidence suggestive of survivorship bias is also uncovered. We conclude that there is some evidence consistent with persistent long memory in the returns of a small proportion of stocks.     

Communicating risk for issues that involve ‘uncertainty bias’: what can the Israeli case of water fluoridation teach us?

Water fluoridation is a controversial issue in public health. Despite the uncertainty regarding its efficacy and safety, health officials continue to communicate it as ‘unequivocally’ safe and effective. Our focus is on how health officials and policy-makers in Israel frame the issue of water fluoridation in terms of certainty while promoting a mandatory fluoridation policy. According to van Asselt and Vos, the uncertainty paradox describes situations in which uncertainty is acknowledged, but the role of science is framed as providing certainty. Our study is an analysis of documents and media articles emphasizing the paradoxical language used by official representatives on the controversial topic of fluoridation. A central contribution of this study is that we coin the term ‘uncertainty bias,’ in which policy-makers do exactly what they accuse laypeople of doing, framing uncertainty in biased terms. We found that in order to establish mandatory regulation, health ministry officials expressed information in an unbalanced format, promoting the topic of fluoridation by framing it in exclusively positive terms. This study does not focus on the practice of water fluoridation per se, and is not intended to decide for or against it, but rather, to explore how the debate regarding it is communicated. Understanding this particular case can shed light on how other controversial topics are transformed into health policy that is characterized in equivocal terms.     

Sample attrition in the RLMS, 2001–10

The data of the Russian Longitudinal Monitoring Survey (RLMS) – Higher School of Economics represents one of the few nationally representative sources of household and individual data for Russia. These data have been collected since 1992 and in recent years, thanks to more secure financial and logistical support, have become a resource increasingly drawn upon by scholars and students for national and cross-national studies. In this paper, we examine the extent of non-random attrition in the RLMS and discuss the circumstances under which this might give rise to biases in econometric analysis. We illustrate this with an example drawn from the health sphere.     

Properties of Census Dual System Population Size Estimators

We study parametric and non‐parametric approaches for assessing the accuracy and coverage of a population census based on dual system surveys. The two parametric approaches being considered are post‐stratification and logistic regression, which have been or will be implemented for the US Census dual system surveys. We show that the parametric model‐based approaches are generally biased unless the model is correctly specified. We then study a local post‐stratification approach based on a non‐parametric kernel estimate of the Census enumeration functions. We illustrate that the non‐parametric approach avoids the risk of model mis‐specification and is consistent under relatively weak conditions. The performances of these estimators are evaluated numerically via simulation studies and an empirical analysis based on the 2000 US Census post‐enumeration survey data.     

On estimating long-run effects in models with lagged dependent variables

A common procedure in economics is to estimate long-run effects from models with lagged dependent variables. For example, macro panel studies frequently are concerned with estimating the long-run impacts of fiscal policy, international aid, or foreign investment.Our analysis points out the hazards of this practice. We use Monte Carlo experiments to demonstrate that estimating long-run impacts from dynamic models produces unreliable results.Biases can be substantial, sample ranges very wide, and hypothesis tests can be rendered useless in realistic data environments. There are three reasons for this poor performance. First, OLS estimates of the coefficient of a lagged dependent variable are downwardly biased in finite samples. Second, small biases in the estimate of the lagged, dependent variable coefficient are magnified in the calculation of long-run effects. And third, and perhaps most importantly, the statistical distribution associated with estimates of the LRP is complicated, heavy-tailed, and difficult to use for hypothesis testing. While many of the underlying problems have been long-known in the literature, the continued widespread use of the associated empirical procedures suggests that researchers are unaware of the extent and severity of the estimation problems. This study aims to illustrate their practical importance for applied research.     

Students' persistent preconceptions and learning economic principles

Economic views held by the general public tend to differ significantly from those of economic experts. Would these differences fade away if people were exposed to some economic instruction? In this article, the authors identify college students' preconceptions about economic issues at the beginning of the semester, verify their persistence throughout the semester, and test whether their beliefs are correlated to course performance. The authors conduct a survey at the beginning and end of the semester on a sample of first-year students taking an economic principles course. They find evidence of preconception persistence and reasoning inconsistencies, pointing to some cognitive biases as a plausible cause. Most students do not integrate the newly learned tools into their thinking process, even if they perform well in tests.     

Wheat variety yield data: Do commercial and public performance tests provide the same information?

Wheat yields from reported performance test results are of economic importance to wheat producers, since their profits depend on selecting the optimal variety for their location. However, our data shows differences in absolute and relative wheat yields between commercial and public wheat breeding program's performance test data in Kansas. Newly available data are used to test if the difference in yields arose from potential selectivity bias, and to determine the contribution of private and public wheat breeding programs to varietal yield improvement during 2007–2012. Both Heckman selection models and multiple regression showed no statistical evidence of the potential presence of selectivity bias rather, managerial practices, agronomic conditions, field location, and inherent genetic traits of the seed variety were identify as the source of yield differences.     

Information and Investor Behavior Surrounding Earnings Announcements

The goal of this paper is to analyze the impact of annual earnings announcements on the market through the order flow data in addition to the usual transaction data. In this respect, examining order flow data can potentially reveal valuable information that is not available from transaction data. In fact, the data allow us to test hypotheses about asymmetric information and investor behavior and to test if the behavior varies with investor sophistication. In addition, the paper tries to identify the determinants of the impact on a firm's value using assumptions about investor behavior.     

Trading volume and stock returns: A meta-analysis

I examine 468 estimates on the relationship between trading volume and stock returns reported in 44 studies. I study publication bias together with Bayesian and frequentist model averaging to explain the heterogeneity in the estimates. The results yield three key conclusions. First, publication bias distorts the findings of the primary studies. Second, the predictability of stock returns varies with different markets and stock types. Third, different data characteristics, structural variations and methodologies used drive the heterogeneity in the results of the primary articles. In particular, one should be cautious when using monthly data or VAR models.