Edited by Lawrence R. Klein
Chapter 11: Using Data and Models at Mixed Frequencies in Computation and Forecasting
11. Using data and models at mixed frequencies in computation and forecasting1 Fyodor I. Kushnirsky 1. INTRODUCTION This chapter considers the use of statistical information at mixed frequencies for estimating economic variables and forecasting. There are two types of problems involved. The first type is interpolation, i.e. the estimation of unknown variables based on related data of higher or lower frequency; this problem has always been of interest in numerical analysis, statistics and econometrics. Depending on the number and type of specified data points, some interpolation procedures are algebraic and others are statistical, resulting in obtaining an average path as an approximation. In other words, econometric estimation can be viewed as an interpolation process in a broader sense than algebraic interpolation; the generalization is caused by the fact that it is not possible to satisfy the interpolation conditions exactly at every point, and a least-squares approximation is performed by minimizing the sum of squared errors. The second type of problems, the construction of macroeconometric models at mixed frequencies and their joint use for medium- and longterm forecasting, is novel. The methodology of constructing and combining such models – high frequency, with monthly or even more frequent observations, and low frequency, with annual observations – was suggested by Klein and Kushnirsky (2005). This methodology differs from conventional forecasting techniques. To illustrate, suppose that two models at different frequencies are available in conventional medium-term forecasting. Their use will likely be arranged so that the high-frequency model is applied to the first year or two,...
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