statsmodels.tsa.interp.denton.dentonm(indicator, benchmark, freq='aq', **kwargs)
Modified Denton’s method to convert low-frequency to high-frequency data.
Uses proportionate first-differences as the penalty function. See notes.
>>> indicator = [50,100,150,100] * 5 >>> benchmark = [500,400,300,400,500] >>> benchmarked = dentonm(indicator, benchmark, freq="aq")
Denton’s method minimizes the distance given by the penalty function, in a least squares sense, between the unknown benchmarked series and the indicator series subject to the condition that the sum of the benchmarked series is equal to the benchmark. The modification allows that the first value not be pre-determined as is the case with Denton’s original method. If the there is no benchmark provided for the last few indicator observations, then extrapolation is performed using the last benchmark-indicator ratio of the previous period.
Minimizes sum((X[t]/I[t] - X[t-1]/I[t-1])**2)
sum(X) = A, for each period. Where X is the benchmarked series, I is the indicator, and A is the benchmark.
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.