国民经济核算体系
对国民账户体系的批评 > Statisticians' criticisms

Statisticians have also criticized the validity of international statistical comparisons using national accounts data, on the ground that in the real world, the estimates are rarely compiled in a uniform way – despite appearances to the contrary.
For example, Jochen Hartwig provides evidence to show that "the divergence in growth rates [of real GDP] between the U.S. and the EU since 1997 can be explained almost entirely in terms of changes to deflation methods that have been introduced in the U.S. after 1997, but not – or only to a very limited extent – in Europe".
The "magic" of national accounts is that they provide an instant source of detailed international comparisons, but, critics argue, on closer inspection, the numbers are not really so comparable as they are made out to be. The effect is that all sorts of easy comparisons are tossed around by policy scientists which, if the technical story behind the numbers was told, would never be attempted because the comparisons are scientifically untenable (or at the very least rather dubious).
Both the strength and the weaknesses of national accounts are that they are based on an enormous variety of data sources. The strength consists in the fact that a lot of cross-checking between data sources and data sets can occur, to assess the credibility of the estimates. The weakness is that the sheer number of inferences made from different data sets used increases the possibility of data errors, and makes it more difficult to assess error margins.
The data quality has also often been criticized on the ground that what pretends to be "data" in reality often consists only of estimates extrapolated from mathematical models, not direct observations. These models are designed to predict what particular data values ought to be, based on sample data for "indicative trends". One can, for example, observe that if variables X, Y, and Z go up, then variable P will go up as well, in a specific proportionality. In that case, one may not need to survey P or its components directly, it is sufficient to get trend data for X, Y, and Z and feed them into a mathematical model which then predicts what the values for P will be at each interval of time.
Because statistical surveys are very costly or may be difficult to organize, or because the data has to be produced rapidly to meet a deadline, statisticians often try to find cheaper, quicker, and more efficient methods to produce the data, by means of inferences from data that they already have, or from selected data which they can get more easily.
But the objection to this approach - although it can sometimes be proved to provide accurate data successfully - is that there is a loss in data accuracy and data quality.
The extrapolated estimates may lack any solid empirical basis, and the tendency is for fluctuations in the magnitudes of variables to be "smoothed out" by the estimation or interpolation procedure.
Any unexpectedly large fluctuation in a variable is difficult to predict by a mathematical model since ultimately the model's descriptions assume the future trend will conform to the law of averages and the patterns of the past.
Without adequate, comprehensive observational data from direct surveys, many of the statistical inferences made are simply not truly verifiable. All one can then say about the estimates is, that they are "probably fairly accurate, given previous and other concurrent data."
A typical reply of statisticians to this kind of objection is that although it is preferable to have comprehensive survey data available as a basis for estimation, and although data errors and inaccuracies do occur, it is possible to find techniques that keep the margins of error within acceptable bounds.