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How efficient are China's macroeconomic forecasts? Evidences from a new forecasting evaluation approach

机译:中国的宏观经济预测效率如何?一种新的预测评估方法的证据

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Surveys, which are designed to collect data and include professional forecasts of macroeconomic variables, are of great interest to central banks and various institutions. The formal evaluation of such forecasts has attracted considerable attention in literature. However, empirical studies focusing on China are limited. This paper firstly proposes a new approach to test forecasts' accuracy and efficiency under asymmetric loss function by using a unique dataset from surveys conducted by the State Administration of Foreign Exchange in China. It is found that forecasters of the four macroeconomic variables (i.e., GDP growth rate, CPI, exports and imports) partially utilize new information and publicly available information under LINEX loss function when they update the forecasts, which are similar to those under quadratic loss function. Besides, a new finding in this paper is that over-smoothing hardly exists in the forecasts of GDP growth rate and CPI in China, which is different from the results in developed countries. Our findings suggest clear areas of opportunity to improve the accuracy of the forecasts, such as considering the negative autocorrelation found in forecast revisions of CPI.
机译:旨在收集数据并包括对宏观经济变量的专业预测的调查,对于中央银行和各种机构都非常感兴趣。对此类预测的正式评估在文献中引起了相当大的关注。但是,针对中国的实证研究有限。本文首先提出了一种新方法,该方法使用来自中国国家外汇管理局的调查中的独特数据集来测试不对称损失函数下的预测准确性和效率。结果发现,四个宏观经济变量(即GDP增长率,CPI,出口和进口)的预测者在更新预测时会部分利用LINEX损失函数下的新信息和公开可用信息,这与二次损失函数下的相似。 。此外,本文的新发现是,在中国的GDP增长率和CPI预测中几乎不存在过度平滑的现象,这与发达国家的结果不同。我们的发现表明,有明显的机会领域可以提高预测的准确性,例如考虑CPI预测修订中发现的负自相关。

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