We give an appraisal of the New Keynesian Phillips curve (NPCM) as an empirical model of European inflation. The favourable evidence for NPCMs on euro-area data reported in earlier studies is shown to depend on specific choices made about estimation methodology. The NPCM can be re-interpreted as a highly restricted equilibrium correction model. We also report the outcome of tests based on variable addition and encompassing of existing models. The results show that economists should not accept the NPCM too readily.
Inflation targeting requires inflation forecasts, yet most models in the literature are either theoretical or calibrated. The motivation for this paper is therefore threefold: We seek to test and implement an econometric model for forecasting inflation in Norway-one economy recently opting for formal inflation targeting rather than a managed nominal exchange rate. We also seek to quantify the relative importance of the different transmission mechanismswith basis in empirical estimates rather than calibrated values. Finally, we want to focus on and exploit econometric issues required in the design and estimation of econometric models used for inflation forecasting and policy analysis.
This paper proposes a new evaluation approach of the class of small-scale 'hybrid' New Keynesian Dynamic Stochastic General Equilibrium (NK-DSGE) models typically used in monetary policy and business cycle analysis. The novelty of our method is that the empirical assessment of the NK-DSGE model is based on a conditional sequence of likelihood-based tests conducted in a Vector Autoregressive (VAR) system in which both the low and high frequency implications of the model are addressed in a coherent framework. The idea is that if the low frequency behaviour of the original time series of the model can be approximated by unit roots, stationarity must be imposed by removing the stochastic trends. This means that with respect to the original variables, the solution of the NK-DSGE model is a VAR that embodies a set of recoverable unit roots/cointegration restrictions, in addition to the cross-equation restrictions implied by the rational expectations hypothesis. The procedure is based on the sequence 'LR1→LR2 →LR3', where LR1 is the cointegration rank test, LR2 the cointegration matrix test and LR3 the cross-equation restrictions test: LR2 is computed conditional on LR1 and LR3 is computed conditional on LR2. The type-I errors of the three tests are set consistently with a prefixed overall nominal significance level and the NK-DSGE model is not rejected if no rejection occurs. We investigate the empirical size properties of the proposed testing strategy by a Monte Carlo experiment and illustrate the usefulness of our approach by estimating a monetary business cycle NK-DSGE model using U.S. quarterly data.
The objective of this paper is to ease the planning of new toll projects by providing estimates of operating costs, and to help us make better informed decisions about the design of the toll collection system. To do so we use panel data for Norwegian toll companies to estimate average cost functions.The main results can be summarised as follows. We provide evidence of very important unexploited economies of scale. The estimates cost curves are very steep for traffic levels below the sample mean, and becomes almost entirely flat over a wide range above the sample mean. A higher share of vehicles using on board units will significantly reduce average costs. Competitive tendering will significantly reduce average operating costs by as much as 25 %. Our results also suggest that increased number of lanes, higher debt and passenger charging will increase average operating costs whereas average operating costs are lower for toll cordons compared with other projects.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.