Macroeconomic Forecasting Based on Mixed Frequency Vector Autoregression and Neural Network Models

Abstract

Macroeconomic indicators include gross domestic product (GDP), consumer price index (CPI), and retail price index (RPI). These indicators are important for understanding the macroeconomic situation and controlling the macroeconomic trend, as they provide a macroscopic view of a country or region's economic performance. If macroeconomic indicators can be predicted accurately in advance, the government and relevant macroeconomic control departments can propose more forward-looking and targeted macroeconomic control policies and deploy the necessary control measures. In addition, individuals can make more reasonable decisions on their investments and savings if they know the macroeconomic indexes in advance. From these two aspects, prediction of macroeconomic indexes is of great research significance. In this paper, we propose a prediction model based on chaotic vector autoregression and neural networks for macroeconomic forecasting, and we model and test the prediction with GDP and inflation as the main concerns, and we find that the improvement of GDP forecasting shows an increase of expected inflation rate, indicating the usefulness of using expected GDP data.