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on Econometric Time Series |
| By: | Abdou Kâ Diongue (UFR SAT - Université Gaston Berger - Université Gaston Berger de Saint-Louis, School of Economics and Finance - Queensland University of Technology); Dominique Guegan (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, Ecole d'économie de Paris - Paris School of Economics - Université Panthéon-Sorbonne - Paris I) |
| Abstract: | In this paper, we discuss the parameter estimation for a k-factor generalized long memory process with conditionally heteroskedastic noise. Two estimation methods are proposed. The first method is based on the conditional distribution of the process and the second is obtained as an extension of Whittle's estimation approach. For comparison purposes, Monte Carlo simulations are used to evaluate the finite sample performance of these estimation techniques. |
| Keywords: | Long memory, Gegenbauer polynomial, heteeroskedasticity, conditional sum of squares, Whittle estimation. |
| Date: | 2008–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:papers:halshs-00235179_v1&r=ets |
| By: | Jana Eklund; Sune Karlsson |
| Abstract: | The problem of having to select a small subset of predictors from a large number of useful variables can be circumvented nowadays in forecasting. One possibility is to efficiently and systematically evaluate all predictors and almost all possible models that these predictors in combination can give rise to. The idea of combining forecasts from various indicator models by using Bayesian model averaging is explored, and compared to diffusion indexes, another method using large number of predictors to forecast. In addition forecasts based on the median model are considered. |
| Date: | 2007–05 |
| URL: | http://d.repec.org/n?u=RePEc:ice:wpaper:wp34&r=ets |
| By: | Bruno Eklund |
| Abstract: | This paper considers the modelling and forecasting of the Icelandic business cycle. The method of selecting monthly variables, coincident and leading, that mimic the cyclical behavior of the quarterly GDP is described. The general business cycle is then modelled by a vector autoregressive, VAR, model. The cyclical behavior of the business cycle is summarized by a composite coincident index, which is based on the root mean squared forecast error over a pseudo out of sample. By applying a bootstrap forecasting procedure, using the estimated VAR model, point and interval forecasts of the composite coincident index are estimated. |
| Date: | 2007–09 |
| URL: | http://d.repec.org/n?u=RePEc:ice:wpaper:wp36&r=ets |
| By: | Jonas Dovern; Christina Ziegler |
| Abstract: | In this paper we analyze the power of various indicators to predict growth rates of aggregate production using real-time data. In addition, we assess their ability to predict turning points of the economy. We consider four groups of indicators: survey data, composite indicators, real economic indicators, and financial data. Almost all indicators are found to improve short-run growth forecasts whereas the results for four-quarter-ahead growth forecasts and the prediction of recession probabilities in general are mixed. We can confirm the result that an indicator suited to improve growth forecasts does not necessarily help to produce more accurate recession forecasts. Only composite leading indicators perform generally well in both forecasting exercises. |
| Keywords: | leading indicators, forecasting, recessions |
| JEL: | C25 C32 E32 E37 |
| Date: | 2008–01 |
| URL: | http://d.repec.org/n?u=RePEc:kie:kieliw:1397&r=ets |
| By: | Andreea Halunga; Chris D. Orme |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:man:sespap:0721&r=ets |
| By: | Ralf Becker; Denise Osborn |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:man:sespap:0724&r=ets |
| By: | J. Isaac Miller (Department of Economics, University of Missouri-Columbia); Joon Y. Park |
| Abstract: | We consider nonlinear transformations of random walks driven by thick-tailed innovations that may have infinite means or variances. These three nonstandard characteristics: nonlinearity, nonstationarity, and thick tails interact to generate a spectrum of asymptotic autocorrelation patterns consistent with long-memory processes. Such autocorrelations may decay very slowly as the number of lags increases or may not decay at all and remain constant at all lags. Depending upon the type of transformation considered and how the model error is speci- fied, the autocorrelation functions are given by random constants, deterministic functions that decay slowly at hyperbolic rates, or mixtures of the two. Such patterns, along with other sample characteristics of the transformed time series, such as jumps in the sample path, excessive volatility, and leptokurtosis, suggest the possibility that these three ingredients are involved in the data generating processes of many actual economic and financial time series data. In addition to time series characteristics, we explore nonlinear regression asymptotics when the regressor is observable and an alternative regression technique when it is unobservable. To illustrate, we examine two empirical applications: wholesale electricity price spikes driven by capacity shortfalls and exchange rates governed by a target zone. |
| Keywords: | persistency in memory, nonlinear transformations, random walks, thick tails, stable distributions, wholesale electricity prices, target zone exchange rates |
| JEL: | C22 |
| Date: | 2008–01–15 |
| URL: | http://d.repec.org/n?u=RePEc:umc:wpaper:0801&r=ets |
| By: | Gonzalo Camba-Méndez (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.); George Kapetanios (Queen Mary, University of London, Mile End Road, London, E1 4NS, United Kingdom.) |
| Abstract: | Testing and estimating the rank of a matrix of estimated parameters is key in a large variety of econometric modelling scenarios. This paper describes general methods to test for and estimate the rank of a matrix, and provides details on a variety of modelling scenarios in the econometrics literature where such methods are required. Four different methods to test the true rank of a general matrix are described, as well as one method that can handle the case of a matrix subject to parameter constraints associated with defineteness structures. The technical requirements for the implementation of the tests of rank of a general matrix differ and hence there are merits to all of them that justify their use in applied work. Nonetheless, we review available evidence of their small sample properties in the context of different modelling scenarios where all, or some, are applicable. JEL Classification: C12, C15, C32. |
| Keywords: | Multiple time series, model specification, tests of rank. |
| Date: | 2008–01 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20080850&r=ets |
| By: | Shiyi Chen; Kiho Jeong; Wolfgang Härdle |
| Abstract: | In recent years, support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a moving average (MA), a recurrent NN and a parametric GACH in terms of their ability to forecast financial markets volatility. The real data in this study uses British Pound-US Dollar (GBP) daily exchange rates from July 2, 2003 to June 30, 2005 and New York Stock Exchange (NYSE) daily composite index from July 3, 2003 to June 30, 2005. The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms the MA, the recurrent NN and the parametric GARCH based on the criteria of mean absolute error (MAE) and directional accuracy (DA). No structured way being available to choose the free parameters of SVR, the sensitivity of performance is also examined to the free parameters. |
| Keywords: | recurrent support vector regression, GARCH model, volatility forecasting |
| JEL: | C45 C53 G32 |
| Date: | 2008–01 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2008-014&r=ets |
| By: | Enzo Weber |
| Abstract: | A small strand of recent literature is occupied with identifying simultaneity in multiple equation systems through autoregressive conditional heteroscedasticity. Since this approach assumes that the structural innovations are uncorrelated, any contemporaneous connection of the endogenous variables needs to be exclusively explained by mutual spillover effects. In contrast, this paper allows for instantaneous covariances, which become identifiable by imposing the constraint of structural constant conditional correlation (SCCC). In this, common driving forces can be modelled in addition to simultaneous transmission effects. The new methodology is applied to the Dow Jones and Nasdaq Composite indexes in a small empirical example, illuminating scope and functioning of the SCCC model. |
| Keywords: | Simultaneity, Identification, EGARCH, CCC |
| JEL: | C32 G10 |
| Date: | 2008–01 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2008-015&r=ets |
| By: | Viktor Winschel; Markus Krätzig |
| Abstract: | We present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse grids. TheSmolyak operator underlying the sparse grids approach frees global approximation from the curse of dimensionality and we apply it to a Chebyshev approximation of the model solution. The operator also eliminates the curse from Gaussian quadrature and we use it for the integrals arising from rational expectations and in three new nonlinear state space filters. The filters substantially decrease the computational burden compared to the sequential importance resampling particle filter. The posterior of the structural parameters is estimated by a new Metropolis-Hastings algorithm with mixing parallel sequences. The parallel extension improves the global maximization property of the algorithm, simplifies the choice of the innovation variances, allows for unbiased convergence diagnostics and for a simple implementation of the estimation on parallel computers. Finally, we provide all algorithms in the open source software JBendge4 for the solution and estimation of a general class of models. |
| Keywords: | Dynamic Stochastic General Equilibrium (DSGE) Models, Bayesian Time Series Econometrics, Curse of Dimensionality |
| JEL: | C11 C13 C15 C32 C52 C63 C68 C87 |
| Date: | 2008–02 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2008-018&r=ets |
| By: | Silvestro Di Sanzo (Department of Economics, University Of Alicante) |
| Abstract: | Recent studies have showed that it is troublesome, in practice, to distinguish between long memory and nonlinear processes. Therefore, it is of obvious interest to try to capture both features of long memory and non-linearity into a single time series model to be able to assess their relative importance. In this paper we put forward such a model, where we combine the features of long memory and Markov nonlinearity. A Markov Chain Monte Carlo algorithm is proposed to estimate the model and evaluate its forecasting performance using Bayesian predictive densities. The resulting forecasts are a significant improvement over those obtained by the linear long memory and Markov switching models. |
| Keywords: | Markov-Switching models, Bootstrap, Gibbs Sampling |
| JEL: | C11 C15 C22 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:2007_03&r=ets |
| By: | Monica Billio (Department of Economics, University Of Venice Cà Foscari); Jacques Anas (Coe Rexecode, Paris); Laurent Ferrara (Banque de Frances); Marco Lo Duca (European Central Bank) |
| Abstract: | The class of Markov switching models can be extended in two main directions in a multivariate framework. In the first approach, the switching dynamics are introduced by way of a common latent factor. In the second approach a VAR model with parameters depending on one common Markov chain is considered (MSVAR). We will extend the MSVAR approach allowing for the presence of specific Markov chains in each equation of the VAR (MMSVAR). In the MMSVAR approach we also explore the introduction of correlated Markov chains which allow us to evaluate the relationships among phases in different economies or sectors and introduce causality relationships, which allow a more parsimonious representation. We apply our model to study the relationship between cyclical phases of the industrial production in the US and Euro zone. Moreover, we construct a MMS model to explore the cyclical relationship between the Euro zone industrial production and the industrial component of the European Sentiment Index. |
| Keywords: | Economic cycles, Multivariate models, Markov switching models, Common latent factors, Causality, Euro-zone |
| JEL: | C50 C32 E32 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:2007_32&r=ets |
| By: | Andros Kourtellos (University of Cyprus, Cyprus.); Chih Ming Tan (Tufts University, USA.); Thanasis Stengos (University of Guelph, Canada and The Rimini Centre for Economic Analysis, Rimini, Italy.) |
| Abstract: | This paper extends the simple threshold regression framework of Hansen (2000) and Caner and Hansen (2004) to allow for endogeneity of the threshold variable. We develop a concentrated two-stage least squares (C2SLS) estimator of the threshold parameter that is based on an inverse Mills ratio bias correction. Our method also allows for the endogeneity of the slope variables. We show that our estimator is consistent and investigate its performance using a Monte Carlo simulation that indicates the applicability of the method in finite samples. We also illustrate its usefulness with an empirical example from economic growth. JEL Classifications: C13, C51 |
| Date: | 2008–01 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:05-08&r=ets |