LAUSR

Censoring, extreme values, and non-normal distribution are common features in microdata. These data features can compromise the statistical distributions of the estimators when an appropriate model is not used. We use an inverse hyperbolic sine (IHS) transformation in the double-hurdle (DH) model to accommodate extreme values and skewness in censored accommodation spending among Turkish households. The full-parameterized model nests many restricted specifications which do not accommodate dependence, heteroscedasticity, and non-normality in the error terms. Statistical test results support the use of the fully parameterized dependent IHS-DH specification. The statistically significant correlation between the binary and level decisions of accommodation precludes the use of a model with a two-step structure. Some of the findings in our study have a determining or driving force in expressing causality relationship in monthly accommodation probability and level decisions. The study made also prudent policy recommendations about what the results might mean to policymakers and stakeholders.