Core Inflation Rate for China and the ASEAN-10 Countries: Smoothed Signal for Score-Driven Local Level Plus Scale Models

Szabolcs Blazsek, Adrián Licht, A. Ayala, Su-Ping Liu
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Abstract

Abstract We use a score-driven minimum mean-squared error (MSE) signal extraction method and perform inflation smoothing for China and the ASEAN-10 countries. Our focus on China and ASEAN-10 countries is motivated by the significant historical variation in inflation rates, e.g. during the 1997 Asian Financial Crisis, the 2007–2008 Financial Crisis, the COVID-19 Pandemic, and the Russian Invasion of Ukraine. Some advantages of the score-driven signal extraction method are that it uses dynamic mean and volatility filters, it considers stationary or non-stationary mean dynamics, it is computationally fast, it is robust to extreme observations, it uses information-theoretically optimal updating mechanisms for both mean and volatility, it uses closed-form formulas for smoothed signals, and parameters are estimated by using the maximum likelihood (ML) method for which the asymptotic properties of estimates are known. In the empirical application, we present the political and economic conditions for each country and analyze the evolution and determinants of the core inflation rate.
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中国和东盟十国的核心通货膨胀率:分数驱动的地方水平加规模模型的平滑信号
摘要 我们采用得分驱动的最小均方误差(MSE)信号提取方法,对中国和东盟十国的通货膨胀率进行平滑处理。我们之所以将重点放在中国和东盟十国,是因为通胀率在历史上存在显著变化,例如在 1997 年亚洲金融危机、2007-2008 年金融危机、COVID-19 大流行病和俄罗斯入侵乌克兰期间。得分驱动信号提取方法的一些优点是:它使用动态均值和波动率滤波器,考虑了静态或非静态均值动态,计算速度快,对极端观测结果具有鲁棒性,对均值和波动率都使用了信息理论上的最优更新机制,对平滑信号使用了闭式公式,使用最大似然法(ML)估计参数,其估计值的渐近特性是已知的。在实证应用中,我们介绍了每个国家的政治和经济状况,并分析了核心通货膨胀率的演变和决定因素。
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