Forecast Analysis of Probability Distribution of China's GDP Growth Rate Based on Quantile Factor Models
This study delves into the probability distribution of GDP growth rate through the utilization of quantile factor models,which facilitate the evaluation of a broad spectrum of economic growth scenarios and uncertainties.By extracting quantile factors from macroeconomic variables,the models establish the probability distribution of GDP growth rate.The results indicate that the inclusion of quantile factors improves the ability of economic forecasts to explain outcomes and also enhances their accuracy.Furthermore,short-term forecasts,grounded in conditional probability density fitting,exhibit notably improved precision.Through an examination of the GDP growth rate probability distribution using quantile factor models,this study underscores the advantages of conditional probability density forecasts in offering more precise insights into economic volatility and downside risks when compared to conventional mean forecasts.These discoveries carry substantial implications for policymaking and contribute significantly to a deeper comprehension of the mechanisms underlying economic volatility and uncertainty.
GDP growth rateProbability densityForecastingQuantile factor models
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