nep-ets New Economics Papers
on Econometric Time Series
Issue of 2005‒11‒09
ten papers chosen by
Yong Yin
SUNY at Buffalo

  1. Real-Time or Current Vintage: Does the Type of Data Matter for Forecasting and Model Selection? By Hui Feng
  2. A maximal moment inequality for long range dependent time series with applications to estimation and model selection By Ching-Kang Ing; Ching-Zong Wei
  3. Subsampling Cointegration Ranks in Large Systems By Chen Pu; Hsiao Chihying
  4. Estimating Short and Long Run Relationships: A Guide to the Applied Economist By Bhaskara Rao
  5. The smooth transition autoregressive target zone model with the Gaussian stochastic volatility and TGARCH error terms with applications By Oleg Korenok; Stanislav Radchenko
  6. Classical Estimation of Multivariate Markov-Switching Models using MSVARlib By BENOIT BELLONE
  7. Can fear beat hope? A story of GARCH-in-Mean-Level effects for Emerging Market Country Risks By Maurício Yoshinori Une; Marcelo Savino Portugal
  8. State Space Modelling of Cointegrated Systems using Subspace Algorithms By Segismundo Izquierdo; Cesáreo Hernández; Javier Pajares
  9. Nonidentically distributed variables and nonlinear autocorrelation By Annibal Figueiredo; Iram Gleria; Raul Matsushita; Sergio Da Silva
  10. Do Time-Varying Covariances, Volatility Comovement and Spillover Matter? By Lakshmi Balasubramanyan

  1. By: Hui Feng (Department of Economics, University of Victoria)
    Abstract: In this paper we investigate the impact of data revisions on forecasting and model selection procedures. A linear ARMA model and nonlinear SETAR model are considered in this study. Two Canadian macroeconomic time series have been analyzed: the real-time monetary aggregate M3 (1977-2000), and residential mortgage credit (1975-1998). The forecasting method we use is multi-step-ahead non-adaptive forecasting.
    Keywords: Vintage Data, Real-time Data, Model Selection, SETAR Model, ARMA model, Forecasting
    JEL: C22 C53
    Date: 2005–08–24
  2. By: Ching-Kang Ing (Institute of Statistical Science, Academia Sinica); Ching-Zong Wei (Institute of Statistical Science, Academia Sinica)
    Abstract: We establish a maximal moment inequality for the weighted sum of a long- range dependent process. An extension to H$\acute{a}$jek-R$\acute{e}$ny and Chow's type inequality is then obtained. It enables us to deduce a strong law for the weighted sum of a stationary long-range dependent time series. To illustrate its usefulness, applications of the inequality to estimation and model selection in multiple regression models with long-range dependent errors are given.
    Keywords: Autoregressive fractionally integrated moving average, long range dependence, maximal inequality, model selection, convergence system, strong consistency.
    JEL: C1 C2 C3 C4 C5 C8
    Date: 2005–08–07
  3. By: Chen Pu (University Bielefeld); Hsiao Chihying (University Bielefeld)
    Abstract: In this paper we investigate the possibility of the application of subsampling procedure for testing cointegration relations in large multivariate systems. The subsampling technique is applied to overcome the difficulty of nonstandard distribution and nuisance parameters in testing for cointegration rank without an explicitly formulated structural model. The contribution in this paper is twofold: theoretically this paper shows that the subsampling testing procedure is consistent and has asymptotically power 1;practically this paper demonstrates that the subsampling procedure can be applied to determine the cointegration rank in large scale models, where the standard procedures hits already its limit. For empirical relevant cases our simulation studies show that centered subsampling improves decisively the performance of subsampling test procedure and makes it applicable also for cases when the number of independent stochastic trends are very large.
    Keywords: Cointegration, Large Systems, Nonparametric Tests, Subsampling
    JEL: C1 C2 C3 C4 C5 C8
    Date: 2005–08–07
  4. By: Bhaskara Rao (University of the South Pacific)
    Abstract: Many applied economists face problems in selecting an appropriate technique to estimate short and long run relationships with the time series methods. This paper reviews three alternative approaches viz., general to specific (GETS), vector autoregressions (VAR) and the vector error correction models (VECM). As in other methodological controversies, definite answers are difficult. It is suggested that if these techniques are seen as tools to summarize data, as in Smith (2000), often there may be only minor differences in their estimates. Therefore a computationally attractive technique is likely to be popular.
    Keywords: Var, Cointegration, General to Specific Approach
    JEL: C1 C2 C3 C4 C5 C8
    Date: 2005–08–13
  5. By: Oleg Korenok (Virginia Commonwealth University); Stanislav Radchenko (University of North Carolina at Charlotte)
    Abstract: This paper proposes to model the error term in smooth transition autoregressive target zone model as Gaussian with stochastic volatility (STARTZ-SV) or as Student-t with GARCH volatility (STARTZ-TGARCH). Using the dynamics of Norwegian krone exchange rate index, we show that both models produce standardized residuals that are closer to assumed distributions and do not produce a hump in the estimated marginal distribution of exchange rate which is more consistent with theoretical predictions. We apply developed models to test whether the dynamics of oil price can be well approximated by the Krugman’s target zone model. Our estimates of conditional volatility and marginal distribution reject the target zone hypothesis.
    Keywords: target zone, oil price, exchange rate, stochastic volatility, griddy Gibbs, smooth transition
    JEL: C52 Q38 F31
    Date: 2005–08–18
    Abstract: This paper introduces an upgraded version of MSVARlib, a Gauss and Ox- Gauss compliant library, focusing on Multivariate Markov Switching Regressions in their most general specification. This new set of procedures allows to estimate, through classical optimization methods, models belonging to the MSI(M)(AH)-VARX ``intercept regime dependent'' family. This research enhances the first package MSVARlib 1.1, which has been deeply inspired by the works of Hamilton and Krolzig. Not to mention the extension to a generalized multivariate regression framework, it notably augments the range of models with a possibly unlimited finite number of Markov states, offers automatic or manual intialization procedures and adds new statistical tests. The first part of this article provides the basic theoretical grounds of the related Markov-switching models. Following sections give some illustrations of the programs through univariate and multivariate examples. One is based on a non-linear reading of the american unemployment rate. A second study is focused on coincident stochastic models of US recessions and slowdowns. The paper concludes on possible extensions and new applications. Detailed guidelines in appendices and tutorial programs are provided to help the reader handling the Gauss package and the joined replication files.
    Keywords: Multivariate Markov-Switching Regressions, Hidden markov Models, Non linear regressions, Open source Gauss library, Business cycle, EM algorithm, Kittagawa-Hamilton Filtering, Recession Detection Models, MSVAR, MS-VAR, Hamilton's Model, Krolzig MSVAR library,Filtered probabilities, Smoothed probabilities.
    JEL: C32 E32 E44
    Date: 2005–08–19
  7. By: Maurício Yoshinori Une (Banco Itaú S.A.); Marcelo Savino Portugal (PPGE/UFRGS)
    Abstract: Upon winning the 2002 presidential elections, event that considerably increased the Brazilian country risk levels and volatility, Lula celebrated by declaring: “hope has beaten fear”. Extending Une and Portugal (2004), the aim of this paper is twofold: to empirically test the interrelations between country risk conditional mean (“hope”) and conditional variance (“fear”) and cast light on the role of country risk stability in the conduction of macroeconomic policies in developing small open economies. We compare the forecasting performance of various alternative GARCH-in-Mean-Level models for n-step conditional volatility point forecasts of the Brazilian country risk estimated for the period May 1994 - February 2005. The results support the idea that both hope and fear play important roles in the Brazilian case and confirms that hope and fear act in the same direction.
    Keywords: nonlinear GARCH, GARCH-in-Mean-Level effect, country risk, fear of disruption, forecast performance
    JEL: C22 F47 G14
    Date: 2005–09–04
  8. By: Segismundo Izquierdo (University of Valladolid); Cesáreo Hernández (University of Valladolid); Javier Pajares (University of Valladolid)
    Abstract: The use of subspace algorithms for the identification of non-stationary cointegrated stochastic systems is a promising technique that is currently under discussion. A revision of the literature provides two distinct algorithms: State Space Aoki Time Series (SSATS) identification algorithm (Aoki and Havenner 1991) and the Adapted Canonical Correlations Analysis (ACCA) of Bauer and Wagner (2002). Aoki’s method is intuitively appealing, but lacks statistical foundation. In contrast, ACCA has a sound statistical basis, though intuition is somewhat lost. Both algorithms are revisited and commented. The study of the underlying ideas and properties of both previous algorithms leads us to propose a new method for subspace identification of non-stationary cointegrated stochastic systems, trying to combine the best features of each one. This new method provides a state space trend-cycle representation of a cointegrated system. Some preliminary simulation results are summarised, comparing these subspace methods with Johansen’s maximum likelihood approach.
    Keywords: system identification, state space, subspace, cointegration, CCA
    JEL: C32
    Date: 2005–09–06
  9. By: Annibal Figueiredo (University of Brasilia); Iram Gleria (Federal University of Alagoas); Raul Matsushita (University of Brasilia); Sergio Da Silva (Federal University of Santa Catarina)
    JEL: G
    Date: 2005–08–18
  10. By: Lakshmi Balasubramanyan (Penn State University)
    Abstract: Financial markets and their respective assets are so intertwined; analyzing any single market in isolation ignores important information. We investigate whether time varying volatility comovement and spillover impact the true variance-covariance matrix under a time-varying correlation set up. Statistically significant volatility spillover and comovement between US, UK and Japan is found. To demonstrate the importance of modelling volatility comovement and spillover, we look at a simple portfolio optimization application. A utility based comparison is used to evaluate the economic performance of the portfolio which considers time varying correlation with volatility comovement and spillover. This paper shows that a portfolio strategy incorporating time-varying correlation with asymmetric volatility comovement and spillover outperforms the constant correlation model without comovement and spillover by yielding the highest level of wealth and utility difference of up to 250 basis points.
    JEL: C32 F3 G15
    Date: 2005–09–04

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