To date the literature on quantile regression and least absolute deviation regression has assumed either explicitly or implicitly that the conditional quantile regression model is correctly specified. When the model is misspecified, confidence intervals and hypothesis tests based on the conventional covariance matrix are invalid. Although misspecification is a generic phenomenon and correct spe...

No retail store choice model, no matter how many relevant variables it might include, can realistically expect to model all the variation in store choice. There are always some variables that are left out, because they are difficult to measure, they have not yet been conceptualized in theory, or their estimated parameter stability suffers when an excessive number of predictors are included. Bec...

This paper identifies two channels through which the economy can generate endogenous inflation and output volatility, an empirical regularity, by introducing model uncertainty into a Lucas-type monetary model. The equilibrium path of inflation depends on agents’ expectations and a vector of exogenous random variables. Following Branch and Evans (2006a) agents are assumed to underparameterize th...

Logistic regression models are commonly used to study the association between a binary response variable and an exposure variable. Besides the exposure of interest, other covariates are frequently included in the fitted model in order to control for their effects on outcome. Unfortunately, misspecification of the main exposure variable and the other covariates is not uncommon, and this can adve...

We describe a test, based on the correlation integral, for the independence of a variable and a vector that can be used to detect model misspecification in serially dependent data. In Monte Carlo simulations this test performs nearly as well or better than the BDS test in univariate time series and complements the BDS test in distributed lag models. Finally, we apply our test to detect misspeci...

In previous research (Hu & Bentler, 1998, 1999), 2 conclusions were drawn: standardized root mean squared residual (SRMR) was the most sensitive to misspecified factor covariances, and a group of other fit indexes were most sensitive to misspecified factor loadings. Based on these findings, a 2-index strategy—that is, SRMR coupled with another index—was proposed in model fit assessment to detec...

The cumulative logit or the proportional odds regression model is commonly used to study covariate effects on ordinal responses. This paper provides some graphical and numerical methods for checking the adequacy of the proportional odds regression model. The methods focus on evaluating functional misspecification for specific covariate effects, but misspecification of the link function can also...