By P. Arestis, G. Zezza
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Extra resources for Advances in Monetary Policy and Macroeconomics
Quarterly data show the same feature but allow for a richer specification because we can use the 6-VAR. 499 points of the quarterly inflation rate (about 2 points of annual rate) using a random walk. Sub-period analysis provides some interesting results. Whatever the forecast model is, forecast errors become smaller. Results are more precise compared to those with annual data, and several differences between models appear. 34). 34). 24 for the univariate forecasts. Then, as Ball (2000) has shown, if a simple adaptive behaviour produces much more important errors than an optimal multivariate forecast, the utilization of an univariate forecast produces only few additional errors.
Inflation dynamics are then seen as an AR(p) process: πt+1 = ρ1 πt + ρ2 πt−1 + · · · + ρp πt−p+1 + with t t+1 ∼ N(0, 1). Expected inflation is given by: Et (πt+1 ) = πt + ρ1 πt + ρ2 πt−1 + · · · + ρp πt−p+1 We use the minimization of the Akaike Information Criteria for computing the optimal number of lags. It ensures that the introduction of supplementary lags does not provide better forecasts. We select 2 lags in univariate and multivariate models when using annual data. With quarterly data the optimal number of lags is 5 for the 6-VAR specification but only 3 for other multivariate specifications (3-VAR and 2-VAR), and 4 lags for the optimal univariate specification.
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