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I am using the tempdisagg R package for benchmarking quarterly time series to annual time series from different (more trusted) sources (by temporally disaggragating the annual data using the quarterly data as indicator series).

The time series are sub series and sum series, and these identities should hold after benchmarking, too. I.e. if

S = A + B - C,

then

predict(td(S,...)) = predict(td(A, ...)) + predict(td(B, ...)) - predict(td(C,...)).

I have tried the Denton-Cholette and the Chow-Lin-maxlog methods.

This is to be carried out regularly, so ideally I would like a disaggregation method that minimises revisions. I have tried removing up to ten years' worth a data from various time series to see if any method outperforms the other in terms of minimising revisions, but it seems that it depends on a combination of time series volatility and method and I can't reach a conclusion.

It would be possible to use a combination of different methods on the sub series, I guess.

Is there any comprehensive knowledge on benchhmarking and revisions?

I have attached some graphs in an attempt to illustrate the problem. Ideally, we would like to see one line that just changes colour according to the various years of data, as in the first two graphs until about 2015. The black lines in the graphs are the raw data.

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SiKiHe
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    The pb with your wondering is that "minimizing revisions" doesn't mean a lot. Dividing the annual value by 4 has 0 revisions and it's not a good benchmark ^^. IMO, it's better to wonder if your benchmark fits the data well. That is : is the model able to predict the next year ? Does the smoothed part of the model contributes to its result a lot ? – Arnaud Feldmann Jun 30 '22 at 14:37

1 Answers1

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Your question seems to consist of two independent parts.

  1. You mention that the identity S = A + B - C can be achieved with our tempdisagg R-library by predict(td(S,...)) = predict(td(A, ...)) + predict(td(B, ...)) - predict(td(C,...)). This is usually not the case. You will have to apply td() to three of the four series and compute the fourth series implicitly (e.g. S = predict(td(A, ...)) + predict(td(B, ...)) - predict(td(C,...))).

  2. To answer your question about the revisions, a reproducible example would be handy. You could create such an example with the example time series in our tempdisagg library, which are accessible by data(tempdisagg).

    Since the Chow-Lin method is based on a regression (in your case of the involved annual time series), the regression parameters are gonna change with every new or revised annual value. As a consequence, all values of the resulting quarterly series are gonna be revised. When applying the Denton method, no parameters have to be estimated. Thus only the most recent years of the resulting quarterly series are prone to revision. If your focus is on the whole resulting quarterly time series, a lot less quarters are prone to revisions when using the Denton method compared to the Chow-Lin method. If your focus is on the revisions of the most recent quarters/years, it's a different story and I doubt that there is a clear cut answer.

I hope this helps. Our paper Temporal Disaggregation of Time Series contains an overview of the different temporal disaggregation methods implemented in the tempdisagg library.

petersteiner
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