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on Econometric Time Series |
| By: | Rafal Kulik; Philippe Soulier |
| Abstract: | We consider strictly stationary heavy tailed time series whose finite-dimensional exponent measures are concentrated on axes, and hence their extremal properties cannot be tackled using classical multivariate regular variation that is suitable for time series with extremal dependence. We recover relevant information about limiting behavior of time series with extremal independence by introducing a sequence of scaling functions and conditional scaling exponent. Both quantities provide more information about joint extremes than a widely used tail dependence coefficient. We calculate the scaling functions and the scaling exponent for variety of models, including Markov chains, exponential autoregressive model, stochastic volatility with heavy tailed innovations or volatility. Theory is illustrated by numerical studies and data analysis. |
| Date: | 2013–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1307.1501&r=ets |
| By: | Guillén, Osmani Teixeira de Carvalho; Hecq, Alain; Issler, João Victor; Saraiva, Diogo |
| Abstract: | It is well known that cointegration between the level of two variables (e.g.prices and dividends) is a necessary condition to assess the empirical validityof a present-value model (PVM) linking them. The work on cointegration,namelyon long-run co-movements, has been so prevalent that it is often over-looked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. This amounts toinvestigate whether short-run co-movememts steming from common cyclicalfeature restrictions are also present in such a system.In this paper we test for the presence of such co-movement on long- andshort-term interest rates and on price and dividend for the U.S. economy. Wefocuss on the potential improvement in forecasting accuracies when imposingthose two types of restrictions coming from economic theory. |
| Date: | 2013–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:fgv:epgewp:742&r=ets |
| By: | Kihwan Kim (Rutgers University); Norman Swanson (Rutgers University) |
| Abstract: | In this chapter, we discuss the use of mixed frequency models and diffusion index approximation methods in the context of prediction. In particular, select recent specification and estimation methods are outlined, and an empirical illustration is provided wherein U.S. unemployment forecasts are constructed using both classical principal components based diffusion indexes as well as using a combination of diffusion indexes and factors formed using small mixed frequency datasets. Preliminary evidence that mixed frequency based forecasting models yield improvements over standard fixed frequency models is presented. |
| Keywords: | forecasting, diffusion index, mixed frequency, recursive estimation, Kalman filter |
| JEL: | C22 C51 |
| Date: | 2013–07–16 |
| URL: | http://d.repec.org/n?u=RePEc:rut:rutres:201315&r=ets |