A few suggestions:1) Duplicate the lower-frequency variable across time.2) Interpolate the lower-frequency variable across values.3) Use a Brownian bridge to synthesize random intermediate values of the lower-frequency variable.4) Develop a model of higher-frequency proxies for the lower-frequency variable.Methods 2, 3, and 4 help account for the effects of unmeasured expectations of the intermediate values of the lower-frequency variable. #2 and # 3 are dangerous because they leak information about the future into the model and can't be used in forecasting the dependent variable in a live context (e.g., you don't know the future quarter's value of GDP in order to interpolate or bridge it with the previous value of GDP.)You might also want to analyze the residuals of your regression resorted by time-since-last-measurement both to scope the magnitude of unexplained variance from using the old value and to look at covariance of those residuals with respect to variance of the NEXT observed value of the slow variable.
Last edited by Traden4Alpha
on January 28th, 2014, 11:00 pm, edited 1 time in total.