I am happy to announce that after several months of getting to understand the language better, I have finally published my first Julia registered package: LongMemory.jl. This package is the result of my research on long memory time series analysis, which is a fascinating topic in econometrics and statistics. Long memory models are useful for capturing the persistence and dependence of many real-world phenomena, such as inflation, interest rates, volatility, network traffic, and environmental data.
LongMemory.jl makes it easy to generate, estimate, and forecast long memory models in Julia. It supports various types of models, such as fractional differencing, cross-sectional aggregation, and stochastic duration shocks. It also provides functions for testing the presence of long memory, computing the Hurst exponent, and simulating long memory processes. The package is fully documented and includes classical data examples, such as the Nile River minima.
I hope you find LongMemory.jl useful and practical. I welcome any feedback, suggestions, or contributions to improve the package. You can contact me or open an issue on GitHub. Thank you for your interest and feedback!
This is a very interesting package. Thankyou.
Veering off topic, can you comment on the similarties of long term economic modelling and climate modelling?
Indded in the anthropocene era they may be related, as humans started to burn fossil fuels.
Cool! I’ve been wanting to play with some long memory models in Julia for a couple of years, but never got around to it myself, and admittedly, it isn’t my area of specialty, so I’m very glad you’ve done it
I would say “it depends”, as trained-economist.
There are certainly some models that can be used for both. But it would depend on the scope of the modelling, on whether we want to explain the phenomena or we want to predict future developments.
For the former, the models come from theory and data is used to validate them.
Whilst for the latter, we may be more interested in the dynamical/statistical properties of the phenomena. This is, I would say, where long memory models are a better fit.
The idea is to accommodate the fact that previous events have long-lasting effects. Hence, predictions should account for this fact. Climate variables, financial volatility measures, among other sources of data have been shown to have this property.
This package is the result of my research on long memory time series analysis, which is a fascinating topic in econometrics and statistics. Long memory models are useful for capturing the persistence and dependence of many real-world phenomena, such as inflation, interest rates, volatility, network traffic, and environmental data.
