Comments and Questions

We find these comments both relevant and important. 1. The econometrics in macro panels has advanced rapidly the last years, not least within the field of dealing with cross-sectional dependence. In our paper we use a panel of 20 OECD-countries to test the New Keynesian Phillips curve, NPC, against an alternative model of price-inflation. From earlier research on country data, our own and others, we have formulated the hypothesis that the dominance of the leading inflation term in particular may be due to omission of other explanatory variables and equilibrating mechanisms, which are in fact formulated and estimated in existing models of ‘price setting’, e.g., the ICM models that we refer to in the paper. If that view is supported by the evidence, macroeconomics is in the (somewhat strange) situation that a new and widely accepted model does in fact not encompass an existing one; hence there is loss of information and learning instead of the opposite. If we are on the right track, estimation of a NPC should give more ore less the same result regarding the main parameters of interest, the coefficient of the forward inflation rate in particular, on any country data set, and also on a panel of macro data. This is the motivation of the paper. Since we use a macro panel we do address the issue of cross-sectional dependence in Section 4. However, we do not model the cross sectional correlation explicitly. The reason is partly, as Sapra points out, that our approach is more convenient to implement, but also that we want to keep the estimation and the presentation of results close to the format that readers familiar with the literature on the NPC (and the earlier literature on the ICM) can recognize. Moreover, our ambition at this stage has not been to estimate "the best" possible price-inflation model on this dataset. Our more limited aim has been to test one model against another, and since our estimates fairly well corroborates the findings forin several single-country analyseis, we take it as a strong indications that the treatment of the two estimated models are reasonably balanced, and that the assumption of the statistical model we use for inference are not rejected by the misspecification test (see 2). That said, the more refined estimation methods that Sapra mentions are cleary relevant to us in future work, on this or other macro panels. 2. We have conducted several estimators, i.e. correcting for cross-section specific heteroskedasticity, period specific heteroskedasticity, contemporaneous covariances and between period covariances. The various estimators show very similar standard errors in our panel. The standard errors we have chosen to report are robust to arbitrary serial correlation and time varying variances in the disturbances. 3. Thank’s for reminding us of this issue. Although there are three endogenous ‘regressors’ in Table 1, all the results are robust to treating Δulc and Δ(pi- p) as exogenous with the inflation lead as the only endogenous explanatory variable. In that setting, the strength of the instruments can be tested by their predictive power in the first stage regression. Note that we estimate two different specifications, namely M1 versus M2, and it is M2 which is most likely to be subject to weak instrument problems, since there are two instruments less in M2 than in M1 (and M1’), i.e. the NPCs. Hence we test the strength of the other 12 instruments. The result is F=57, which is much higher than the rule of thumb of 10. By implication, it is therefore even less evidence for weak instrument problems for M1, since the ‘first stage regression’ for that model includes two more significant predictors, namely the lagged values of (ulc-pi) and (ulc-p), which are jointly significant with Χ2 (2) =22.36. In line with this, the test statistic for instrument irrelevance is F=76 for M1 and M1'. Incidentally, Bårdsen et al (2004) report F=71 for their re-estimation of the euro area NPC.