Testing for Additive Outliers in Seasonally Integrated Time Series

The detection of additive outliers in integrated variables has attracted some attention recently, see e.g. Shin et al. (1996), Vogelsang (1999) and Perron and Rodriguez (2003). This paper serves several purposes. We prove the inconsistency of the test proposed by Vogelsang, we extend the tests proposed by Shin et al. and Perron and Rodriguez to the seasonal case, and we consider alternative ways of computing their tests. We also study the effects of periodically varying variances on the previous tests and demonstrate that these can be seriously size distorted. Subsequently, some new tests that allow for periodic heteroskedasticity are proposed.Additive outliers, outlier detection, integrated processes, periodic heteroscedasticity, seasonality.

Multicointegration and present value relations

It is well-known that if the forcing variable of a present value (PV) model is an integrated process, then the model will give rise to a particular cointegrating restriction. In this paper we demostrate that if the PV relation is exact, such that no additive error term appears in the specification, then te variables will be multicointegrated such that the cumlation of cointegration errors at one level of cointegration will cointegrate with the forcing variable. Multicointegration thus delivers a statistical property of the data that is necessary, though not sufficient, for this class of models to be valido Estimation and inference of the model are discussed and it is shown that, provided me PV relation is exact, the discount factor of the model can be estimated with arate of convergence that is faster than the usual super-consistent rate characterising estimators in the cointegration literature. Finally, the paper is completed with two empirical analyses of PV models using term structure data and farmland data, respectively

On Financing of Urban Transition viewed from the Oresund Area:When the Political Agenda of Urban Transition meets the Market

The “urban transition” agenda is as a conglomerate of ambitions derived from international policy documents and as applied in the Oresund area. Encompassing locally set goals for (i) climate change mitigation; (ii) energy efficiency; and (iii) human wellbeing in the built environment. Its implementation is largely dependent on private sector joining in, since transitioning the building stock is to be financed by the market. This paper explores strategies to meet this agenda in the Oresund area. A particular focus is on the refurbishment of multi-family housing relative to these set goals. The paper finds meaningful differences between Denmark and Sweden. In general, exceptionally high energy standards come at an additional cost that is likely to be incompatible with rational economic behavior. Furthermore, actions appropriate for one goal are likely to have modest effects on ancillary goals. The paper concludes by suggesting to revisit current strategies in the Oresund area to reflect market constraints and to promote more coherent ways to achieve the set goals

A vector autoregressive model for electricity prices subject to long memory and regime switching

A regime dependent VAR model is suggested that allows long memory (fractional integration) in each of the observed regime states as well as the possibility of fractional cointegration. The model is motivated by the dynamics of electricity prices where the transmission of power is subject to occasional congestion periods. For a system of bilateral prices non-congestion means that electricity prices are identical whereas congestion makes prices depart. Hence, the joint price dynamics implies switching between a univariate price process under non-congestion and a bivariate price process under congestion. At the same time, it is an empirical regularity that electricity prices tend to show a high degree of long memory, and thus that prices may be fractionally cointegrated. Analysis of Nord Pool data shows that even though the prices are identical under non-congestion, the prices are not, in general, fractionally cointegrated in the congestion state. Hence, in most cases price convergence is a property following from regime switching rather than a conventional error correction mechanism. Finally, the suggested model is shown to deliver forecasts that are more precise compared to competing models.Cointegration, electricity prices, fractional integration, long memory, regime switching

Long-run forecasting in multicointegrated systems

We extend the analysis of Christoffersen and Diebold (1998) on long-run forecasting in cointegrated systems to multicointegrated systems. For the forecast evaluation we consider several loss functions, each of which has a particular interpretation in the context of stock-flow models where multicointegration typically occurs. A loss function based on a standard mean square forecast error (MSFE) criterion focuses on the forecast errors of the flow variables alone. Likewise, a loss function based on the triangular representation of cointegrated systems (suggested by Christoffersen and Diebold) considers forecast errors associated with changes in both stock (modelled through the cointegrating restrictions) and flow variables. We suggest a new loss function which is based on the triangular representation of multicointegrated systems which further penalizes deviations from the long-run relationship between the levels of stock and flow variables as well as changes in the flow variables. Among other things, we show that if one is concerned with all possible long-run relations between stock and flow variables, this new loss function entails high and increasing forecasting gains compared to both the standard MSFE criterion and Christoffersen and Diebold?s criterion. The paper demonstrates the importance of carefully selecting loss functions in forecast evaluation of models involving stock and flow variables

Multiple unit roots in periodic autoregression

In this paper we propose a model selection strategy for a univariate periodic autoregressive time series which involves tests for one or more unit roots and for parameter restrictions corresponding to seasonal unit roots and multiple unit roots at the zero frequency. Examples of models that are considered are variants of the seasonal unit roots model and the periodic integration model. We show that the asymptotic distributions of various test statistics are the same as well-known distributions which are already tabulated. We apply our strategy to three empirical series to illustrate its ease of use. We find that evidence for seasonal unit roots based on nonperiodic models disappears when periodic representations are considered

Multiple unit roots in periodic autoregression

In this paper we propose a model selection strategy for a univariate periodic autoregressive time series which involves tests for one or more unit roots and for parameter restrictions corresponding to seasonal unit roots and multiple unit roots at the zero frequency. Examples of models that are considered are variants of the seasonal unit roots model and the periodic integration model. We show that the asymptotic distributions of various test statistics are the same as well-known distributions which are already tabulated. We apply our strategy to three empirical series to illustrate its ease of use. We find that evidence for seasonal unit roots based on nonperiodic models disappears when periodic representations are considered