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on Econometric Time Series |
| By: | Joshua C.C. Chan; Eric Eisenstat |
| Abstract: | Empirical work in macroeconometrics has mostly restricted to using VARs, even though there are strong theoretical reasons to consider general VARMAs. This is perhaps because estimation of VARMAs is perceived to be challenging. In this article, we develop a Gibbs sampler for the basic VARMA, and demonstrate how it can be extended to models with stochastic volatility and time-varying parameters. We illustrate the methodology through a macroeconomic forecasting exercise. We show that VARMAs produce better density forecasts than VARs, particularly for short forecast horizons. |
| JEL: | C11 C32 C53 |
| Date: | 2013–02 |
| URL: | http://d.repec.org/n?u=RePEc:acb:cbeeco:2013-604&r=ets |
| By: | Juan Carlos Cuestas (University of Sheffield, UK); Luis A. Gil-Alana (University of Navarra, Pamplona, Spain) |
| Abstract: | This paper examines the interaction between non-linear deterministic trends and long run dependence by means of employing Chebyshev time polynomials and assuming that the detrended series displays long memory with the pole or singularity in the spectrum occurring at one or more possibly non-zero frequencies. The combination of the non-linear structure with the long memory framework produces a model which is linear in parameters and therefore it permits the estimation of the deterministic terms by standard OLS-GLS methods. Moreover, the orthogonality property of Chebyshev’s polynomials makes them especially attractive to approximate non-linear structures of data. We present a procedure which allows us to test (possibly fractional) orders of integration at various frequencies in the presence of the Chebyshev trends with no effect on the standard limit distribution of the method. Several Monte Carlo experiments are conducted and the results indicate that the method performs well, and an empirical application, using data of real exchange rates is also carried out at the end of the article. |
| Keywords: | Chebyshev polynomials; long run dependence; fractional integration |
| JEL: | C22 |
| Date: | 2013–02 |
| URL: | http://d.repec.org/n?u=RePEc:aee:wpaper:1301&r=ets |
| By: | R\'emy Chicheportiche; Anirban Chakraborti |
| Abstract: | Study of recurrences in earthquakes, climate, financial time-series, etc. is crucial to better forecast disasters and limit their consequences. However, almost all the previous phenomenological studies involved only a long-ranged autocorrelation function, or disregarded the multi-scaling properties induced by potential higher order dependencies. Consequently, they missed the facts that non-linear dependences do impact both the statistics and dynamics of recurrence times, and that scaling arguments for the unconditional distribution may not be applicable. We argue that copulas is the correct model-free framework to study non-linear dependencies in time series and related concepts like recurrences. Fitting and/or simulating the intertemporal distribution of recurrence intervals is very much system specific, and cannot actually benefit from universal features, in contrast to the previous claims. This has important implications in epilepsy prognosis and financial risk management applications. |
| Date: | 2013–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1302.3704&r=ets |
| By: | Henri Audigé |
| Abstract: | The objective of this paper is to gauge how and to which extent the surge in Greek sovereign bond rates in 2010 and 2011 has spilled over the rest of the Euro-area. To this end, we rely on a new class of contagion tests based on Smooth Transition Conditional Correlation GARCH models (STCC-GARCH). Our results highlight the existence of contagion and “wake-up call†effects from Greece to Ireland and Portugal in 2010, and a decoupling in the correlations between Greece and other peripheral countries in 2011. Regarding the core countries, our findings suggest flight-to-quality effects from Greece to Germany and the Netherlands. |
| Keywords: | Bond market, contagion, European crisis, multivariate GARCH models |
| JEL: | C32 C58 G01 G12 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:drm:wpaper:2013-02&r=ets |
| By: | Adam Clements (QUT); Yin Liao (QUT) |
| Abstract: | Understanding the dynamics of volatility and correlation is a crucially important issue. The literature has developed rapidly in recent years with more sophisticated estimates of volatility, and its associated jump and diffusion components. Previous work has found that jumps at an index level are not related to future volatility. Here we examine the links between co-jumps within a group of large stocks, the volatility of, and correlation between their returns. It is found that the occurrence of common, or co-jumps between the stocks are unrelated to the level of volatility or correlation. On the other hand, both volatility and correlation are lower subsequent to a co-jump. This indicates that co-jumps are a transient event but in contrast to earlier research have a greater impact that jumps at an index level. |
| Keywords: | Realized volatility, correlation, jumps, co-jumps, point process |
| JEL: | C22 G00 |
| Date: | 2013–02–06 |
| URL: | http://d.repec.org/n?u=RePEc:qut:auncer:2013_03&r=ets |
| By: | Miguel Belmonte (Department of Economics, University of Strathclyde); Gary Koop (Department of Economics, University of Strathclyde) |
| Abstract: | This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA)in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in‡ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results. |
| Keywords: | Model switching, forecast combination, switching state space model, infl‡ation forecasting |
| JEL: | C11 C52 E37 E47 |
| Date: | 2013–01 |
| URL: | http://d.repec.org/n?u=RePEc:str:wpaper:1302&r=ets |
| By: | Gary Koop (Department of Economics, University of Strathclyde) |
| Abstract: | This paper discusses the challenges faced by the empirical macroeconomist and methods for surmounting them. These challenges arise due to the fact that macroeconometric models potentially include a large number of variables and allow for time variation in parameters. These considerations lead to models which have a large number of parameters to estimate relative to the number of observations. A wide range of approaches are surveyed which aim to overcome the resulting problems. We stress the related themes of prior shrinkage, model averaging and model selection. Subsequently, we consider a particular modelling approach in detail. This involves the use of dynamic model selection methods with large TVP-VARs. A forecasting exercise involving a large US macroeconomic data set illustrates the practicality and empirical success of our approach. |
| Keywords: | Bayesian VAR; forecasting; time-varying coefficients; state-space model |
| JEL: | C11 C52 E27 E37 |
| Date: | 2013–01 |
| URL: | http://d.repec.org/n?u=RePEc:str:wpaper:1303&r=ets |