A Direct Method Based on Seasonally Adjusted Growth Rate
- 王亚南院－已发表论文 
传统季节调整方法在提取环比增长率时需要先剔除原始数据中的季节成分,这会带来原始数据信息的失真。鉴于此,本文提出了一种直接拟合原始数据增长率的季节增长率(SGR)模型,该模型不仅可以直接提取环比增长率,还可以对原始数据的增长率进行预测。蒙特卡洛模拟结果表明,本文给出的针对SGR模型的MLE估计方法具有良好的有限样本表现。通过对我国GDP和CPI数据进行实证,本文发现利用SGR模型直接提取的环比增长率的稳定性要高于其他一些季节调整方法。不仅如此,SGR模型的拟合和预测表现相比BSM模型和SARIMA模型均有显著提高。此外,SGR模型还具有容易拓展为非线性、多元情形的优势。Using the traditional seasonal adjustment method to obtain the seasonally adjusted growth rate, in which need to remove its seasonal component first, hut it maybe lead to distortions on the raw data information. According to that reason, this paper presents a seasonal growth rate （SGR） model that can not only fit and forecast the original seasonal growth rate, but also extract the seasonally adjusted growth rate from the unadjusted growth rate data directly. Monte Carlo simulation results show that the proposed MLE estimation method for SGR model has a good finite sample performance. Through the empirical applications to Chinas real GDP and CPI data, we find that the seasonally adjusted growth rate extracted by the SGR model is smoother than by the other seasonally adjustment methods. Moreover, the fitting and forecasting performance of the SGR model is significantly improved compared to that of the BSM model and the SARIMA model. In addition, the SGR model also has the advantage of being easily extended to the nonlinear or multivariate cases.