forecast error (RMSE). A measure of “absolute” forecast accuracy, the RMSE indicates by how many units (e.g., percentage points of GDP growth) the forecast differed from the outcome on average over the sample period. It is given by the square root of the average squared error. For a sample [ t 0 : t 1 ] and a forecast horizon of h , the RMSE for country i is computed as: R M S E i , h = ( t 1 − t 0 + 1 ) − 1 ∑ t = t 0 t 1 e i t | t − h . 2 ( 2 ) Figure 1 displays the inter-quartile ranges, medians, and GDP-weighted means of the RMSE values for each of the
Front Matter Page Research Department Table of Contents Summary I. Introduction II. Instruments, Indicators, Targets, and Goals III. Nominal Income and Price Level Targets IV. The Role of Indicator Variables V. An Alternative Instrument VI. Concluding Remarks Appendix Tables 1. RMSE Values for Nominal Income Target Simulation Results, 1954.1-1985.4 2. RMSE Values for Price Level Target Simulation Results, 1954.1-1985.4 3. RMSE Values for Price Level Target Simulation Results, 1954.1-1985.4 4. RMSE Values for Real GNP
x * t are logarithms, these RMSE values can be interpreted as percentage deviations, with (e.g.) 0.02 corresponding to 2.0 percent. From the reported figures it can be seen that the rule (1) performs satisfactorily for intermediate values of A, that is, values between 0.1 and 0.25. Despite the variety of models, x t values are kept close to the x t target path, thereby implying inflation rates close to zero for the period. Targeting errors are smaller than with λ = 0 in all cases, and with the VAR model they are much smaller than when λ = 0.5. This last