Disaggregation of very small time series with multiple endogenous partial structural breaks

In this paper, we propose a method to disaggregate very small time series by fitting them with higher frequency related series using a cointegration regression with multiple partial endogenous structural breaks. We allow any coecient to change at up to two dates of structural break and three related series and provide critical values for the test of cointegration corrected for the very small sample size. We find that increasing the num- ber of related series drastically improves the power of the test by allowing for increased flexibility in the cointegration model. The simulated power of the test is shown to be very high even in very small sample sizes such as fifteen observations. This flexibility also mildly improves the accuracy of the disaggregation method when the sample size is as small as thirty-five observations. An application to the Chinese national accounts data is provided and allows the study of the Chinese business cycles stylized facts. We find that household consumption, public spending, and trade surpluses are the main driver of the business cycle.