Towards Macroeconomic Portfolio Forecasting Model With Multi-source Mixing Data

DOI

10.2991/978-94-6463-270-5_13How to use a DOI?

Keywords

Mixing data; MIDAS model; Macroeconomic; Combined forecasting

Abstract

Using China’s daily financial data and monthly/quarterly macroeconomic data, this paper constructs a mixed frequency data sampling model (MIDAS) from the perspective of pseudo-out-of-sample forecasting, and adds financial and economic leading factors to compare the macroeconomic prediction accuracy of the four types of combined forecasting models. The results show that the combined forecasting model can reduce the systematic error of macroeconomic forecasting and improve the prediction accuracy. Among them, daily financial data can improve the prediction accuracy of univariate; Whether in MIDAS or traditional forecasting models, monthly/quarterly macroeconomic data can improve the accuracy of macroeconomic forecasting; The macroeconomic forecasting effect of monthly/quarterly macroeconomic data on the macroeconomy is comparable to that of daily financial data on the macroeconomy, or even better than the macroeconomic forecasting effect of daily financial data; The leading items of monthly/quarterly macroeconomic data have a good effect on China’s macro forecast.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xiaoxuan Su
PY  - 2023
DA  - 2023/10/29
TI  - Towards Macroeconomic Portfolio Forecasting Model With Multi-source Mixing Data
BT  - Proceedings of the 3rd International Conference on Internet Finance and Digital Economy (ICIFDE 2023)
PB  - Atlantis Press
SP  - 112
EP  - 123
SN  - 2667-1271
UR  - https://doi.org/10.2991/978-94-6463-270-5_13
DO  - 10.2991/978-94-6463-270-5_13
ID  - Su2023
ER  -