Python implementation of the midasml approach
Companion code repository for the textbook Macroeconometrics by Alessia Paccagnini, with Python, R, MATLAB and Dynare examples
Code repository for "Multi-Task Encoder-Dual-Decoder Modeling Framework on Mixed Frequency Data", International Journal of Forecasting, 2023
This repository contains the code for the paper "Hybrid ARDL-MIDAS-Transformer Time-Series Regressions for Multi-Topic Crypto Market Sentiment Driven by Price and Technology Factors", by Ioannis Chalkiadakis, Prof. Gareth W. Peters and Dr. Matthew Ames.
Bridging Time Series Frequencies for Nowcasting
Forecasting GDP using MIDAS regressions with mixed-frequency macroeconomic indicators; includes data preparation, model estimation, and evaluation.
Replication package for Burri, Marc (2026): "Nowcasting Swiss GDP Growth from Public Lead Texts: Simple Methods Are Sufficient", Oxford Bulletin of Economics and Statistics, forthcoming.
Stata commands for Mixed-Frequency Data Sampling (MIDAS): bumidas and umidas for unrestricted selection and estimation, rmidas for restricted estimation, and mfcollapse for constructing mixed-frequency data. Includes examples, tests, simulations, and an empirical application to exchange rate forecasting.
An implementation of the Semiparametric Spatiotemporal Model with Mixed Frequencies
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