Spatial effects are endemic in models based on spatially referenced data. The increased awareness of the relevance of spatial interactions, spatial externalities and networking effects among actors, evoked the area of spatial econometrics. Spatial econometrics focuses on the specification and estimation of regression models explicitly incorporating such spatial effects. The multidimensionality ...

A generative model is, of necessity, a vast simplification of the deeply complex real-world phenomena that govern any observed data set. It is only via this simplification that we can arrive at a tractable data analysis and discover meaningful and actionable patterns in data. In this sense, typically any model of a real-world data set is misspecified, and misspecification is unavoidable. But wh...

The misspecification of gravity spatial interaction models has recently been described by the author. The bias in parameter estimates that results from such misspecification appears to produce the 'map pattern effect' or 'spatial structure bias' in estimated distance-decay parameters. A further aspect of the misspecification bias in gravity parameter estimates is explored here. The severity of ...

b a c k g r o u n d & aim: multi-state models can help better understand the process of chronic diseases such as cancers.  these models  are influenced  by assumptions  like individual  homogeneity.  this study aimed to investigate the effect of lack of individual homogeneity  assumption  in multi-state models. methods & materials: to investigate the effect of lack of individual homogeneity ass...

Abstract We explore decision-making under uncertainty using a framework that decomposes into three distinct layers: (1) risk, which entails inherent randomness within given probability model; (2) model ambiguity, about the to be used; and (3) misspecification, presence of correct among set models considered. Using new experimental design, we isolate measure attitudes toward each layer separatel...

The growing availability of computer power and statistical software has greatly increased the ease with which practitioners apply statistical methods, but this has not been accompanied by attention to checking the assumptions on which these methods are based. At the same time, disagreements about inferences based on statistical research frequently revolve around whether the assumptions are actu...

We develop a misspecification test for the multiplicative two-component GARCHMIDAS model suggested in Engle et al. (2013). In the GARCH-MIDAS model a short-term unit variance GARCH component fluctuates around a smoothly timevarying long-term component which is driven by the dynamics of a macroeconomic explanatory variable. We suggest a Lagrange Multiplier statistic for testing the null hypothes...

Tam Bang Vu Department of Economics, University of Hawaii at Manoa 2424 Maile Way, 542 Saunders Hall, Honolulu, HI 96822; [email protected] Abstract Mankiw (1982) shows that consumer durables expenditures should follow a linear ARMA(1,1) process, but the data analyzed supports an AR(1) process instead; thus, a puzzle. In this paper, we employ a more general utility function than Mankiw's quadrati...

We address function misspecification and model heterogeneity, two critical issues in empirical work. A nonparametric approach is proposed for single-index, binary-choice models when parametric models such as Probit and Logit are potentially misspecified. The new approach involves two steps: first, we estimate index coefficients using sliced inverse regression without knowing the conditional pro...

Sensitivity analysis stands in contrast to diagnostic testing in that sensitivity analysis aims to answer the question of whether it matters that a nuisance parameter is non-zero, whereas a diagnostic test ascertains explicitly if the nuisance parameter is different from zero. In this paper, we introduce and derive the finite sample properties of a sensitivity statistic measuring the sensitivit...