Introduction Linear regression commonly assumes that the error terms of a model are independently and identically distributed (i.i.d). However, when datasets contain groups, the potential for correlated error terms within groups arises. Example: Weather shocks to apple orchards For example, consider a model of the supply of apples from various orchards across the United States. [...]
Introduction Though many standard econometric models assume that variance is constant, structural breaks in variance are well-documented, particularly in economic and finance data. If these changes are not accurately accounted for, they can hinder forecast inference measures, such as forecast variances and intervals. In this blog, we consider a tool that can be used to [...]
Introduction Time series data with inconsistently formatted dates and times can make your work frustrating. Dates and times are often stored as strings or text data and converting to a consistent, numeric format might seem like a daunting task. Fortunately, GAUSS includes an easy tool for loading and converting dates and times – the date [...]
Introduction The housing market has been on a roller coaster ride since the early 2000s. We have seen the rise and fall of one bubble and it is feared by some that we may be witnessing a second bubble today. If we want to study housing prices, an important first step is to check and [...]
Introduction While structural breaks are a widely examined topic in pure time series, their impacts on panel data models have garnished less attention. However, in their forthcoming paper Chowdhury and Russell (2018) demonstrate that structural breaks can cause bias in the instrumental variable panel estimation framework. This work highlights that structural breaks shouldn't be limited [...]
