On Biases of the Price-Deflator

Ryotaro Iochi
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Abstract

With the increasing role that price-deflators play in econometric analysis, the delicate numerical effects caused by using deflator-series must be closely scrutinized. In some cases another model-setting of analysis might be preferred at seeing some unexpected results of one model due to the biases of deflator. The ipiases of price-deflatorsl can be classified into three types : the model bias, the formula bias and the data bias. By the model bias is meant the sort of biasing effect caused by a model in which the deflator and the deflatand2 are related in some function. In ordinary uses the deflator-series are taken as denominators for the deflatand series under the assumption that the factor of pricelevel is included in the deflatand money value as a product-factor. This assumption, however, is not due where, for instance, the price-level acts as one of the regressors explaining the money value of the regressand. If we apply the denominator form of the dailator to this regression analysis, a sort of model bias is found in this misapplication. Sceondly, the formula type of bias has a connection to the formula used for the construction of the price-deflator. The Laspeyres formula is well-known to have a limitation in its power of expressing the change of the price-1evel, to the effect that its use as deflator includes this second type of bias. We can, how-ever, neglect these first two types of bias when possible. But the third type of bias can we never neglect in any case. That is the data-bias which stems inevitably from the discrepancies on the nature of data between the deflatand and the deflator. As a matter of course we try to select appropriate deflator-series so as to fit well to the nature of the deflatand-series. Nevertheless there are apt to remain some discrepancies between the two series-e.g. the total expenditure of a family as the deflatant and the Consumers' Price Indices as deflator, m the pomt of therr "coverage", This sort of discrepancies will be ever deepened so long as the Laspeyres formula will hold without any revision of the weightsystem. Among these three the third type, the data bias type, will be treated in this paper, the other two beinb" Put aside for the time being.
论价格平减指数的偏差
随着价格平减指数在计量经济分析中的作用越来越大,使用平减指数序列引起的微妙数值效应必须仔细审查。在某些情况下,由于平减指数的偏差,在看到一个模型的一些意想不到的结果时,可能更倾向于另一个模型的分析设置。物价平减指数的偏差可分为三种类型:模型偏差、公式偏差和数据偏差。所谓模型偏差,是指由平减指数和平减指数在某种功能上相关联的模型所引起的那种偏倚效应。在通常使用中,假设物价水平因素包括在通货紧缩和货币价值作为产品因素的情况下,将通货紧缩系列作为通货紧缩和系列的分母。然而,这种假设并不适用于,例如,价格水平作为解释回归的货币价值的回归因子之一。如果我们将分母形式的累加式应用到回归分析中,在这种错误应用中会发现一种模型偏差。其次,偏差的公式类型与用于构建价格平减指数的公式有关。众所周知,拉斯佩尔公式在表达价格水平变化的能力上有一个局限性,即它作为平减指数的使用包含了第二种偏差。然而,我们可以在可能的情况下忽略前两种偏见。但第三种偏见在任何情况下都不能忽视。这就是数据偏差,它不可避免地源于通货紧缩指数和通货紧缩指数在数据性质上的差异。当然,我们尽量选择合适的平胀级数,以便很好地适应平胀级数的性质。然而,这两个系列之间往往仍然存在一些差异。把一个家庭的总支出作为通货紧缩指标,把消费者价格指数作为通货紧缩指标,作为他们“覆盖”的重点。只要拉斯佩尔公式在不修改权重体系的情况下仍然成立,这种差异就会越来越大。在这三种类型中,本文将处理第三种类型,即数据偏差类型,其他两种类型暂不考虑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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