Estimating the evolution of elasticities of natural gas demand: the case of Istanbul, Turkey

Much of the existing literature on demand for natural gas assumes constant and single-value elasticities, overlooking the possibility of dynamic responses to the changing conditions. We aim to fill this gap by providing individual time series of short-run elasticity estimates based on maximum entropy resampling in a fixed-width rolling window framework. This approach does not only enable taking the variability of the elasticities into account, but also helps obtain more efficient and robust results in small samples in comparison with conventional inferences based on asymptotic distribution theory. To illustrate the methodology, we employ monthly time-series data between 2004 and 2012 and analyze the dynamics of residential natural gas demand in Istanbul, the largest metropolitan area in Turkey. Our findings reveal that the elasticities of the demand model do not remain constant and they are sensitive to the economic situation as well as weather fluctuations

Bootstrap Inference of Level Relationships in the Presence of Serially Correlated Errors: A Large Scale Simulation Study and an Application in Energy Demand

By undertaking a large scale simulation study, we demonstrate that the maximum entropy bootstrap (meboot) data generation process can provide accurate and narrow parameter confidence intervals in models with combinations of stationary and nonstationary variables, under both low and high degrees of autocorrelation. The relatively small sample sizes in which meboot performs particularly well make it a useful tool for rolling window estimation. As a case study, we analyze the evolution of the price and income elasticities of import demand for crude oil in Turkey by using quarterly data between 1996-2011. Our approach can be employed to tackle a wide range of macroeconometric estimation problems where small sample sizes are a common issue

Towards a framework for the design of quantitative experiments: Human-computer interaction and accessibility research

Many students and researchers struggle with the design and analysis of empirical experiments. Such issue may be caused by lack of knowledge about inferential statistics and suitable software tools. Often, students and researchers conduct experiments without having a complete plan for the entire lifecycle of the process. Difficulties associated with the statistical analysis are often ignored. Consequently, one may end up with data that cannot be easily analyzed. This paper discusses the concept sketch of a framework that intends to help students and researchers to design correct empirical experiments by making sound design decisions early in the research process. The framework consists of an IDE, i.e., Integrated (statistical experiment) Development Environment. This IDE helps the user structures an experiment by giving continuous feedback drawing the experimenter’s attention towards potential problems. The output of the IDE is an experimental structure and data format that can be imported to common statistical packages such as JASP in addition to providing guidance about what tests to use

Hostage of the Software: Experiences in Teaching Inferential Statistics to Undergraduate Human-Computer Interaction Students and a Survey of the Literature

Students’ knowledge of inferential statistics is lacking in many computer science study programs. Yet, the needs for inferential statistical skills have emerged with new fields of study such as human-computer interaction involving observation of human activity. This paper presents experiences teaching inferential statistics to undergraduate computer science students with a focus on the actual goals of the investigations and not the mechanisms and mathematics of statistics. The teaching framework involves teaching statistics as a set of systematic black-box tools