Weather and temperatures vary in ways that are difficult to explain and predict precisely. In this article we review data on temperature variations in the past as well possible reasons for these variations. Subsequently, we review key properties of global climate models and statistical analyses...

The logistic function is one of the most useful mathematical models that can be utilised for various practical, real-life data, such as forecasting sea ice extent. This study aims to determine what logistic function is and how it can help forecast a time series problem, as in our case, a sea ice...

Outlying observations can bias regression estimates, requiring the use of robust estimators. Comparing robust estimates to those obtained using OLS is a common robustness check, however, such comparisons have been mostly informal due to the lack of available tests. Here we introduce a formal...

Econometric studies for global heating have typically used regional or global temperature averages to study its long memory properties. One typical explanation behind the long memory properties of temperature averages is cross-sectional aggregation. Nonetheless, formal analysis regarding the...

Evidence-based policy of global warming is best relying on a relevant sample of data. We choose a sample of annual data from 1959 to-date to provide some statistically robust stylized facts about the relationships between actual CO2 and temperature. Visually, there is a clear upward trend in...

This paper proposes strategies to detect time reversibility in stationary stochastic processes by using the properties of mixed causal and noncausal models. It shows that they can also be used for non-stationary processes when the trend component is computed with the Hodrick-Prescott filter...