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- Daunfeldt, Sven-Olov, et al.
(författare)
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Efficacy and Cost of Regime Shifts in Inflation Policies : Evidence from New Zealand and Sweden
- 2001
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Ingår i: Applied Economics. - 0003-6846 .- 1466-4283. ; 33:2, s. 217-224
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Tidskriftsartikel (refereegranskat)abstract
- In this paper a comparative study of the regime shift in inflation policies in New Zealand and Sweden is performed. A nonparametric regression method is used to decompose the inflation time series into three components of variation: a long-term trend, a medium-term (cyclical and transient variations) trend and a short-term shocks component. This allows study of the transition process from the high inflation characterizing the end of the 1970s and the 1980s to the low inflation observed during the 1990s. It is found that in New Zealand, although it is initially delayed, the decrease in inflation happens at a faster pace than in Sweden. This may indicate that reforms were more efficient in New Zealand. A clear link is also shown between the rising unemployment and the transition from high to low inflation. Furthermore, while in New Zealand a downward adjustment of the unemployment rate happens directly after the transition period, in Sweden there seems to be persistence in high unemployment.
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- de Luna, Xavier, et al.
(författare)
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Choosing a Model Selection Strategy
- 2003
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Ingår i: Scandinavian Journal of Statistics. ; 30, s. 113-128
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Tidskriftsartikel (refereegranskat)
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- de Luna, Xavier, et al.
(författare)
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Robust Simulation-Based Estimation of ARMA Models
- 2001
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Ingår i: Journal of Computational and Graphical Statistics. ; 10, s. 370-387
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Tidskriftsartikel (refereegranskat)abstract
- This article proposes a new approach to the robust estimation of a mixed autoregressive and moving average (ARMA) model. It is based on the indirect inference method that originally was proposed for models with an intractable likelihood function. The estimation algorithm proposed is based on an auxiliary autoregressive representation whose parameters are first estimated on the observed time series and then on data simulated from the ARMA model. To simulate data the parameters of the ARMA model have to be set. By varying these we can minimize a distance between the simulation-based and the observation-based auxiliary estimate. The argument of the minimum yields then an estimator for the parameterization of the ARMA model. This simulation-based estimation procedure inherits the properties of the auxiliary model estimator. For instance, robustness is achieved with GM estimators. An essential feature of the introduced estimator, compared to existing robust estimators for ARMA models, is its theoretical tractability that allows us to show consistency and asymptotic normality. Moreover, it is possible to characterize the influence function and the breakdown point of the estimator. In a small sample Monte Carlo study it is found that the new estimator performs fairly well when compared with existing procedures. Furthermore, with two real examples, we also compare the proposed inferential method with two different approaches based on outliers detection.
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