RDP 2018-08: Econometric Perspectives on Economic Measurement Appendix F: The Method of Moments Makes Sensible Multilateral Index Functions

Consider a model consisting of assumptions A1** to A4**. It is a special case of The Diewert Model.

A1**

where: f (·), p_tuv, α_t, ε_tuv and U_tv are understood already; and λ_v is a fixed effect for varieties.

A2** Across varieties the observations are independently and identically distributed.

A3** The errors follow strict exogeneity of the form. So

A4** There is conditional heteroskedasticity of the form

where σ is some strictly positive constant.

Now consider a case that sets f (x) = x, U_tv = q_tvλ_v and . As the model is in levels, the heteroskedasticity assumption is natural.

The model corresponds to the conditional moment restriction

where δ and x_tv is vector shorthand for the full set of coefficients and regressors (the time and product dummies) that are implicit in the model, for a given (t, v) pair.

Following Wooldridge (2010, p 542), the efficient method of moments estimators for α_t and λ_v solve

But note that

and

the last of which contains the unknown parameters α and λ. The feasible method of moments estimators use and instead.

The resulting system of equations is

and

This is the same as the system of Geary (1958) and Khamis (1972). The result differs somewhat from the original method of moments derivation of Rao and Hajargasht (2016), who argue that an inefficient weighting system is necessary to generate the index. Although our set-ups are different, ultimately it is the introduction of units of equal interest that resolves the discrepancy.

Table F1 provides the settings needed to generate the other multilateral indices considered in Rao and Hajargasht (2016), using the method of moments. I have not explored whether there are further multilateral indices that fit the method of moments interpretation.