Creating high‐frequency national accounts with state‐space modelling: a Monte Carlo experiment

A new technique for producing high-frequency data from lower frequency measurements subject to the full set of identities within the data all holding is assessed, demonstrating that the more simple models do almost as well as more complex ones and hence there may be little gain in return for the extra work of using a complex model.

Abstract

Abstract This paper assesses a new technique for producing high‐frequency data from lower frequency measurements subject to the full set of identities within the data all holding. The technique is assessed through a set of Monte Carlo experiments. The example used here is gross domestic product (GDP) which is observed at quarterly intervals in the United States and it is a flow economic variable rather than a stock. The problem of constructing an unobserved monthly GDP variable can be handled using state space modelling. The solution of the problem lies in finding a suitable state space representation. A Monte Carlo experiment is conducted to illustrate this concept and to identify which variant of the model gives the best monthly estimates. The results demonstrate that the more simple models do almost as well as more complex ones and hence there may be little gain in return for the extra work of using a complex model. Copyright © 2001 John Wiley & Sons, Ltd.