nep-ets New Economics Papers
on Econometric Time Series
Issue of 2012‒06‒05
nine papers chosen by
Yong Yin
SUNY at Buffalo

  1. Short-time asymptotics for marginal distributions of semimartingales By Amel Bentata; Rama Cont
  2. A New Semiparametric Volatility Model By Jiangyu Ji; Andre Lucas
  3. Valid Locally Uniform Edgeworth Expansions Under Weak Dependence and Sequences of Smooth Transformations By Antonis Demos; Stelios Arvanitis
  4. Conclusive Evidence on the Benefits of Temporal Disaggregation to Improve the Precision of Time Series Model Forecasts By Ramirez, Octavio A.
  5. Modelling Breaks and Clusters in the Steady States of Macroeconomic Variables By Chan, Joshua C.C.; Koop, Gary
  6. Regime-Switching Cointegration By Jochmann, Markus; Koop, Gary
  7. A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models By Bauwens, Luc; Korobilis, Dimitris; Koop, Gary
  8. A Nonlinear Panel Unit Root Test under Cross Section Dependence By Cerrato, Mario; de Peretti, Christian; Larsson, Rolf; Sarantis, Nicholas
  9. Forecasting with Medium and Large Bayesian VARs By Koop, Gary

  1. By: Amel Bentata (LPMA - Laboratoire de Probabilités et Modèles Aléatoires - CNRS : UMR7599 - Université Paris VI - Pierre et Marie Curie - Université Paris VII - Paris Diderot); Rama Cont (LPMA - Laboratoire de Probabilités et Modèles Aléatoires - CNRS : UMR7599 - Université Paris VI - Pierre et Marie Curie - Université Paris VII - Paris Diderot)
    Abstract: We study the short-time asymptotics of conditional expectations of smooth and non-smooth functions of a (discontinuous) Ito semimartingale; we compute the leading term in the asymptotics in terms of the local characteristics of the semimartingale. We derive in particular the asymptotic behavior of call options with short maturity in a semimartingale model: whereas the behavior of out-of-the-money options is found to be linear in time, the short time asymptotics of at-the-money options is shown to depend on the fine structure of the semimartingale.
    Keywords: semimartingale ; short-time asymptotics ; marginal distribution ; short maturity asymptotics ; Levy process ; option pricing
    Date: 2012
  2. By: Jiangyu Ji (VU University Amsterdam); Andre Lucas (VU University Amsterdam, and Duisenberg school of finance)
    Abstract: We propose a new semiparametric observation-driven volatility model where the form of the error density directly influences the volatility dynamics. This feature distinguishes our model from standard semiparametric GARCH models. The link between the estimated error density and the volatility dynamics follows from the application of the generalized autoregressive score framework of Creal, Koopman, and Lucas (2012). We provide simulated evidence for the estimation efficiency and forecast accuracy of the new model, particularly if errors are fat-tailed and possibly skewed. In an application to equity return data we find that the model also does well in density forecasting.
    Keywords: volatility clustering; Generalized Autoregressive Score model; kernel density estimation; density forecast evaluation
    JEL: C10 C14 C22
    Date: 2012–05–22
  3. By: Antonis Demos (; Stelios Arvanitis
    Abstract: In this paper we are concerned with the issue of the existence of locally uniform Edgeworth expansions for the distributions of random vectors. Our motivation resides on the fact that this could enable subsequent uniform approximations of analogous moments and their derivatives. We derive sufficient conditions either in the case of stochastic processes exhibiting weak dependence, or in the case of smooth transformations of such expansions. The combination of the results can lead to the establishment of high order asymptotic properties for estimators of interest.
    Keywords: Locally uniform Edgeworth expansion, formal Edgeworth distribution, weak dependence, smooth transformations, moment approximations, GMM estimators, Indirect estimators, GARCH model.
    JEL: C10 C13
    Date: 2012–05–28
  4. By: Ramirez, Octavio A.
    Abstract: Simulation methods are used to measure the expected differentials between the Mean Square Errors of the forecasts from models based on temporally disaggregated versus aggregated data. This allows for novel comparisons including long-order ARMA models, such as those expected with weekly data, under realistic conditions where the parameter values have to be estimated. The ambivalence of past empirical evidence on the benefits of disaggregation is addressed by analyzing four different economic time series for which relatively large sample sizes are available. Because of this, a sufficient number of predictions can be considered to obtain conclusive results from out-of-sample forecasting contests. The validity of the conventional method for inferring the order of the aggregated models is revised.
    Keywords: Data Aggregation, Efficient Forecasting, Research Methods/ Statistical Methods,
    Date: 2012
  5. By: Chan, Joshua C.C.; Koop, Gary
    Abstract: Macroeconomists working with multivariate models typically face uncertainty over which (if any) of their variables have long run steady states which are subject to breaks. Furthermore, the nature of the break process is often unknown. In this paper, we draw on methods from the Bayesian clustering literature to develop an econometric methodology which: i) finds groups of variables which have the same number of breaks; and ii) determines the nature of the break process within each group. We present an application involving a five-variate steady-state VAR.
    Date: 2011
  6. By: Jochmann, Markus; Koop, Gary
    Abstract: We develop methods for Bayesian inference in vector error correction models which are subject to a variety of switches in regime (e.g. Markov switches in regime or structural breaks). An important aspect of our approach is that we allow both the cointegrating vectors and the number of cointegrating relationships to change when the regime changes. We show how Bayesian model averaging or model selection methods can be used to deal with the high-dimensional model space that results. Our methods are used in an empirical study of the Fisher effect.
    Keywords: Bayesian, Markov switching, structural breaks, cointegration, model averaging,
    Date: 2011
  7. By: Bauwens, Luc; Korobilis, Dimitris; Koop, Gary
    Abstract: This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.
    Keywords: Forecasting, change-points, Markov switching, Bayesian inference,
    Date: 2011
  8. By: Cerrato, Mario; de Peretti, Christian; Larsson, Rolf; Sarantis, Nicholas
    Abstract: We propose a nonlinear heterogeneous panel unit root test for testing the null hypothesis of unit-roots processes against the alternative that allows a proportion of units to be generated by globally stationary ESTAR processes and a remaining non-zero proportion to be generated by unit root processes. The proposed test is simple to implement and accommodates cross sectional dependence. We show that the distribution of the test statistic is free of nuisance parameters as (N, T) −! 1. Monte Carlo simulation shows that our test holds correct size and under the hypothesis that data are generated by globally stationary ESTAR processes has a better power than the recent test proposed in Pesaran [2007]. Various applications are provided.
    Keywords: Nonlinear panel unit root tests, cross sectional dependence,
    Date: 2011
  9. By: Koop, Gary
    Abstract: This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic data set containing 168 variables. We nd that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Our empirical results show the importance of using forecast metrics which use the entire predictive density, instead of using only point forecasts.
    Keywords: Bayesian, Minnesota prior, stochastic search variable selection, predictive likelihood,
    Date: 2011

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