Meet the Flintstones - a critical essay on the perpetuation of the 'caveman' stereotype, from the late 1800's to today.

Images can convey a lot in a little space, and communicate in a way that words cannot. For this reason, studying visual communication is important in order to ensure that users are conveying the right message. This thesis aims to better understand paleolithic reconstructions. and the stereotypes that are associated with them with the help of the follow theoretical perspectives: gender, visual perspective, agency, and critical theory. Images are taken from three different periods in order to track the progression: the late 1800's, the 1960's, and today. These images are analyzed to extract elements of the stereotype. These elements are discussed in association with their appropriate time periods and paradigms in order to grasp various aspects associated with perpetuation. We see that certain aspects of reconstructions have remained stagnate since the birth of the stereotype in the late 1800's, and that these aspects are the result of various socio-political factors as well as conscious and unconscious motives. In addition to researching the reasons behind stereotype perpetuation it was necessary to conduct a survey to show how images affect those who are exposed to them. The survey shows that images affect both archaeologically and non-archaeologically trained students to varying degrees

Identification-robust simulation-based inference in joint discrete/continuous models for energy markets

In the analysis of energy use models, a common problem consists in correcting for endogenous discrete-choice variables. Indeed, energy demand equations often include endogenous dummies which reflect the underlying discrete-choice for e.g. energy equipment. The latter lead to discrete/continuous (D/C) statistical models where the discrete and continuous components are statistically dependent, so weak-identification problems may occur which stem from the "quality" of the first stage instrumental model. These problems are studied in the context of energy demand analysis. A wide mixed-logit-based class of models is considered which allow for dependent choices, heteroskedasticity and multi-dimensionality. The severity of weak-identification problems and relevance for empirical practice are documented, even with very large data sets. Tractable and reliable (in the sense of type I error control) solutions are proposed which combine generalized Anderson-Rubin (GAR) procedures and maximum simulated likelihood (MSL) methods for models commonly used in practice. Results are illustrated via Monte-Carlo examples and an empirical study on electricity demand.