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on Econometric Time Series |
| By: | Helmut Luetkepohl |
| Abstract: | Vector autoregressive (VAR) models for stationary and integrated variables are reviewed. Model specification and parameter estimation are discussed and various uses of these models for forecasting and economic analysis are considered. For integrated and cointegrated variables it is argued that vector error correction models offer a particularly convenient parameterization both for model specification and for using the models for economic analysis. |
| Keywords: | VAR, vector autoregressive models |
| JEL: | C32 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:eui:euiwps:eco2007/11&r=ets |
| By: | Christian Kascha |
| Abstract: | Classical Gaussian maximum likelihood estimation of mixed vector autoregressive moving-average models is plagued with various numerical problems and has been considered di±cult by many applied researchers. These disadvantages could have led to the dominant use of vector autoregressive models in macroeconomic research. Therefore, several other, simpler estimation methods have been proposed in the literature. In this paper these methods are compared by means of a Monte Carlo study. Different evaluation criteria are used to judge the relative performances of the algorithms. |
| Keywords: | VARMA Models, Estimation Algorithms, Forecasting |
| JEL: | C32 C15 C63 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:eui:euiwps:eco2007/12&r=ets |
| By: | Christian Conrad (KOF Swiss Economic Institute, ETH Zurich Switzerland) |
| Abstract: | In this article we derive conditions which ensure the non-negativity of the conditional variance in the Hyperbolic GARCH(p; d; q) (HYGARCH) model of Davidson (2004). The conditions are necessary and suffcient for p < 2 and suffcient for p > 2 and emerge as natural extensions of the inequality constraints derived in Nelson and Cao (1992) for the GARCH model and in Conrad and Haag (2006) for the FIGARCH model. As a by-product we obtain a representation of the ARCH(1) coeffcients which allows computationally effcient multi-step-ahead forecasting of the conditional variance of a HYGARCH process. We also relate the necessary and suffcient parameter set of the HYGARCH to the necessary and su±cient parameter sets of its GARCH and FIGARCH components. Finally, we analyze the effects of erroneously fitting a FIGARCH model to a data sample which was truly generated by a HYGARCH process. An empirical application of the HYGARCH(1; d; 1) model to daily NYSE data illustrates the importance of our results. |
| Keywords: | Inequality constraints, fractional integration, long memory GARCH processes |
| JEL: | C22 C52 C53 |
| Date: | 2007–04 |
| URL: | http://d.repec.org/n?u=RePEc:kof:wpskof:07-162&r=ets |
| By: | Figueiredo, Annibal; Matsushita, Raul; Da Silva, Sergio; Serva, Maurizio; Viswanathan, Gandhi; Nascimento, Cesar; Gleria, Iram |
| Abstract: | We employ the Levy sections theorem in the analysis of selected dollar exchange rate time series. The theorem is an extension of the classical central limit theorem and offers an alternative to the most usual analysis of the sum variable. We find that the presence of fat tails can be related to the local volatility pattern of the series. |
| JEL: | C49 |
| Date: | 2007–07–03 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:3810&r=ets |