The Nested PIGLOG Model: An Application to U.S. Food Demand

A new demand system is introduced, the Nested PIGLOG model, nesting thirteen other demand systems including five that are also new. This new model and its nested special cases are applied to models of U.S. food demand that include food-at-home (FAH), food-away-from-home (FAFH), and alcoholic beverages. Although nested tests and out-of-sample forecasting performance favor generalizing models to a certain degree, statistically insignificant improvements to in-sample-fit and even poorer out-of-sample forecast accuracy undermine further generalizations. Based on a subset of preferred models, FAFH is found to be price and income elastic compared to FAH which is price and income inelastic.     

Robustness issues in multilevel regression analysis

A multilevel problem concerns a population with a hierarchical structure. A sample from such a population can be described as a multistage sample. First, a sample of higher level units is drawn (e.g. schools or organizations), and next a sample of the sub‐units from the available units (e.g. pupils in schools or employees in organizations). In such samples, the individual observations are in general not completely independent. Multilevel analysis software accounts for this dependence and in recent years these programs have been widely accepted. Two problems that occur in the practice of multilevel modeling will be discussed. The first problem is the choice of the sample sizes at the different levels. What are sufficient sample sizes for accurate estimation? The second problem is the normality assumption of the level‐2 error distribution. When one wants to conduct tests of significance, the errors need to be normally distributed. What happens when this is not the case? In this paper, simulation studies are used to answer both questions. With respect to the first question, the results show that a small sample size at level two (meaning a sample of 50 or less) leads to biased estimates of the second‐level standard errors. The answer to the second question is that only the standard errors for the random effects at the second level are highly inaccurate if the distributional assumptions concerning the level‐2 errors are not fulfilled. Robust standard errors turn out to be more reliable than the asymptotic standard errors based on maximum likelihood.     

Memory recall errors in retrospective surveys: A reverse record check study

This paper reports on a study to compare self-reports during an interview with staff who attended a University health centre in Turkey, with the records of visits to the same health centre over the previous 12 months. Design of the study reflects the effects of importance of the event, duration since the event, frequency of the occurrence of the event, measurement scale of the event, and bounded and unbounded recalling. In order to assess the extent of recall error, responses to retrospective questions on health centre visits are compared with administrative records. Statistical models are proposed for short and long term human memory recall error effects on responses.     

Blocked-Error-R 2: A Conceptually Improved Definition of the Proportion of Explained Variance in Models Containing Loops or Correlated Residuals

Bentler and Raykov (2000, Journal of Applied Psychology 85: 125–131), and Jöreskog (1999a, http://www.ssicentral.com/lisrel/column3.htm, 1999b http://www.ssicentral. com/lisrel/column5.htm) proposed procedures for calculating R ² for dependent variables involved in loops or possessing correlated errors. This article demonstrates that Bentler and Raykov’s procedure can not be routinely interpreted as a “proportion” of explained variance, while Jöreskog’s reduced-form calculation is unnecessarily restrictive. The new blocked-error-R ² (beR ²) uses a minimal hypothetical causal intervention to resolve the variance-partitioning ambiguities created by loops and correlated errors. Hayduk (1996) discussed how stabilising feedback models – models capable of counteracting external perturbations – can result in an acceptable error variance which exceeds the variance of the dependent variable to which that error is attached. For variables included within loops, whether stabilising or not, beR ² provides the same value as Hayduk’s (1996) loop-adjusted-R ². For variables not involved in loops and not displaying correlated residuals, beR ² reports the same value as the traditional regression R ². Thus, beR ² provides a conceptualisation of the proportion of explained variance that spans both recursive and nonrecursive structural equation models. A procedure for calculating beR ² in any SEM program is provided.     

Behavior in one-shot traveler’s dilemma games: model and experiments with advice

We report results of one-shot traveler’s dilemma game experiments to test the predictions of a model of introspection. The model describes a noisy out-of-equilibrium process by which players reach a decision of what to do in one-shot games. To test the robustness of the model and to compare it to other models of introspection without noise, we introduce non-binding advice. Advice has the effect of coordinating all players’ beliefs onto a common strategy. Experimentally, advice is implemented by asking subjects who participated in a repeated traveler’s dilemma game to recommend an action to subjects playing one-shot games with identical parameters. In contrast to observations, models based on best-response dynamics would predict lower claims than the advised. We show that our model’s predictions with and without advice are consistent with the data.      

Observable Consequences of Trading Structure Differences: On the Use of Variance Ratios in Microstructure Studies

This paper examines the variance ratio tests in studies of transitory volatility and concludes that the variance ratio is an appropriate test of trading structure differences only under certain assumptions regarding the evolution of underlying stock prices and the autocorrelation structure of returns. This result raises caution as to the interpretation of results bases upon the 24-hour variance ratio methodologies in studies of transitory volatility and trading structure effects. A numerical example indicates that errors in inferences can be severe.     

Capitalized intangibles and financial analysts

We examine whether firms that capitalize a higher proportion of their underlying intangible assets have higher analyst following, lower dispersion of analysts’ earnings forecasts and more accurate earnings forecasts relative to firms that capitalize a lower proportion. Under Australian generally accepted accounting principles, capitalization of intangible assets has become increasingly ‘routine’ since the late 1980s. It is predicted that this experience leads Australian analysts to expect firms with relatively more certain intangible investments to signal this fact by capitalizing intangible assets. Our results are consistent with this. We find that capitalization of intangible assets is associated with higher analyst following and lower absolute earnings forecast error for firms with a stock of underlying intangible assets. Our tests suggest a weaker association between capitalization and lower earnings forecast dispersion. We conclude that there are benefits for analysts, for management to have the option to capitalize intangible assets. These findings suggest that IAS 38 Intangible Assets and AASB 138 Intangible Assets reduce the usefulness of financial statements.     

Food safety recall effects across meat products and regions

Food safety is one of the key issues for the international meat market. As a major meat exporter, few things facing the U.S. meat industry in recent years have garnered more attention than food safety events and policies. The impacts of Food Safety Inspection Services (FSIS) recalls on United States consumer meat demand are estimated using monthly grocery-scanner data identifying effects across products, geographic regions, and recall type. Results suggest beef E. coli recalls significantly reduce the demand for recalled ground beef contemporaneously among most, but not all, regions in the United States. Evidence of heterogeneity in demand impacts across regions and products is provided for the first time. Domestic and international implications for policy makers, industry leaders, and researchers are discussed.     

Consumer preferences before and after a food safety scare: An experimental analysis of the 2010 egg recall

In August 2010, more than half a billion eggs were recalled in the U.S. because of a Salmonella outbreak. This study examines the effect of the recall with a unique pair of auction experiments investigating willingness to pay (WTP) for conventional and organic eggs, one conducted shortly before and one right after the recall with the same participants. In addition to the before and after bids, participants bid again after a negative information or balanced information treatment about the event. Accompanying surveys showed consumers had a high level of awareness of the recall but less knowledge of specific details, and viewed information on egg farm conditions as very important in their WTP. While there were no significant before and after differences, WTP for organic eggs significantly increased in the negative information treatment, and balanced information had a positive effect on consumer WTP for conventional eggs.     

Conditional Persistence of Earnings Components and Accounting Anomalies

We suggest that the failure of investors to distinguish between an earnings component's autocorrelation coefficient (unconditional persistence) and the marginal contribution of that component's persistence to the persistence of earnings (conditional persistence) provides a partial explanation of post‐earnings‐announcement drift, post‐revenue‐announcement drift, and the accrual anomaly. When the conditional persistence of revenue surprises is high (low) relative to its unconditional persistence, both the post‐earnings‐announcement drift and the post‐revenue‐announcement drift are high (low), because investors’ under‐reaction to revenues and earnings is stronger when the persistence of revenue surprises is more strongly associated with the persistence of earnings surprises. Also, the mispricing of accruals decreases substantially when the conditional persistence of accruals is high relative to its unconditional persistence, because investors’ over‐reaction to accruals is mitigated when the persistence of accruals is indeed more strongly associated with the persistence of earnings. Our findings also suggest that financial analysts’ failure to distinguish between unconditional and conditional persistence of revenues and accruals results in more biased revenue and earnings predictions.