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on Econometric Time Series |
| By: | Timo Teräsvirta (Aarhus University and CREATES); Yukai Yang (CORE, Université catholique de Louvain and CREATES) |
| Abstract: | We consider a nonlinear vector model called the logistic vector smooth transition autoregressive model. The bivariate single-transition vector smooth transition regression model of Camacho (2004) is generalised to a multivariate and multitransition one. A modelling strategy consisting of specification, including testing linearity, estimation and evaluation of these models is constructed. Nonlinear least squares estimation of the parameters of the model is discussed. Evaluation by misspecification tests is carried out using tests derived in a companion paper. The use of the modelling strategy is illustrated by two applications. In the first one, the dynamic relationship between the US gasoline price and consumption is studied and possible asymmetries in it considered. The second application consists of modelling two well known Icelandic riverflow series, previously considered by many hydrologists and time series analysts. JEL Classification: C32, C51, C52 |
| Keywords: | Vector STAR model, Modelling nonlinearity, Vector autoregression, Generalized impulse response, Asymmetry, Oil price River flow |
| Date: | 2014–03–21 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2014-08&r=ets |
| By: | Quoreshi, A.M.M. Shahiduzzaman (CITR, Blekinge Inst of Technology) |
| Abstract: | We develop a model to account for the long memory property in a bivariate count data framework. We propose a bivariate integer-valued fractional integrated (BINFIMA) model and apply the model to high frequency stock transaction data. The BINFIMA model allows for both positive and negative correlations between the counts. The unconditional and conditional first and second order moments are given. The CLS and FGLS estimators are discussed. The model is capable of capturing the covariance between and within intra-day time series of high frequency transaction data due to macroeconomic news and news related to a specific stock. Empirically, it is found that Ericsson B has mean recursive process while AstraZeneca has long memory property. It is also found that Ericsson B and AstraZenica react in a similar way due to macroeconomic news. |
| Keywords: | Count data; Intra-day; Time series; Estimation; Reaction time; Finance |
| JEL: | C13 C22 C25 C51 G12 G14 |
| Date: | 2014–04–02 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:bthcsi:2014-003&r=ets |
| By: | Li, Yushu (Dept. of Business and Management Science, Norwegian School of Economics); Reese, Simon (Dept. of Economics, School of Economics and Management, Lund University) |
| Abstract: | The Hidden Markov Model (HMM) has been widely used in regime classification and turning point detection for econometric series after the decisive paper by Hamilton (1989). The present paper will show that when using HMM to detect the turning point in cyclical series, the accuracy of the detection will be influenced when the data are exposed to high volatilities or combine multiple types of cycles that have different frequency bands. Moreover, outliers will be frequently misidentified as turning points. The present paper shows that these issues can be resolved by wavelet multi-resolution analysis based methods. By providing both frequency and time resolutions, the wavelet power spectrum can identify the process dynamics at various resolution levels. We apply a Monte Carlo experiment to show that the detection accuracy of HMMs is highly improved when combined with the wavelet approach. Further simulations demonstrate the excellent accuracy of this improved HMM method relative to another two change point detection algorithms. Two empirical examples illustrate how the wavelet method can be applied to improve turning point detection in practice. |
| Keywords: | HMM; turning point; wavelet; wavelet power spectrum; outlier |
| JEL: | C22 C38 C63 |
| Date: | 2014–03–25 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:nhhfms:2014_010&r=ets |
| By: | Li, Yushu (Dept. of Business and Management Science, Norwegian School of Economics); Andersson, Fredrik N. G. (Dept. of Economics, School of Economics and Management, Lund University) |
| Abstract: | Hong and Kao (2004) proposed a panel data test for serial correlation of unknown form. However, their test is computationally difficult to implement, and simulation studies show the test to have bad small-sample properties. We extend Gencay’s (2011) time series test for serial correlation to the panel data case in the framework proposed by Hong and Kao (2004). Our new test maintains the advantages of the Hong and Kao (2004) test, and it is simpler and easier to implement. Furthermore, simulation results show that our test has quicker convergence and hence better small-sample properties. |
| Keywords: | Energy Distribution; MODWT; Serial correlation; Static and dynamic panel models |
| JEL: | C11 C12 C15 |
| Date: | 2014–03–25 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:nhhfms:2014_011&r=ets |
| By: | Li, Yushu (Dept. of Business and Management Science, Norwegian School of Economics); Andersson, Jonas (Dept. of Business and Management Science, Norwegian School of Economics) |
| Abstract: | In this paper, we propose a likelihood ratio and Markov chain based method to evaluate density forecasting. This method can jointly evaluate the unconditional forecasted distribution and dependence of the outcomes. This method is an extension of the widely applied evaluation method for interval forecasting proposed by Christoffersen (1998). It is also a more refined approach than the pure contingency table based density forecasting method in Wallis (2003). We show that our method has very high power against incorrect forecasting distributions and dependence. Moreover, the straightforwardness and ease of application of this joint test provide a high potentiality for further applications in both financial and economical areas. |
| Keywords: | Likelihood ratio test; Markov Chain; Density forecasting |
| JEL: | C14 C53 C61 |
| Date: | 2014–03–25 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:nhhfms:2014_012&r=ets |
| By: | Costantini, Mauro; Lupi, Claudio |
| Abstract: | Sequential panel selection methods (spsms) are based on the repeated application of panel unit root tests and are increasingly used to identify I (0) time series in macro- panels. We check the reliability of spsms by using Monte Carlo simulations based on generating the individual test statistics and the p values to be combined into panel unit root tests, both under the unit root null and under selected local alternatives. The analysis is carried out considering both independent and dependent test statistics. We show that spsms do not possess better classification performances than conventional univariate tests. |
| Keywords: | Unit root, Panel data, ROC curve, Simulation |
| JEL: | C12 C15 C23 |
| Date: | 2014–03–29 |
| URL: | http://d.repec.org/n?u=RePEc:mol:ecsdps:esdp14073&r=ets |
| By: | Geert Dhaene; Koen Jochmans (Département d'économie) |
| Abstract: | We calculate the bias of the profile score for the regression coefficients in a multistratum autoregressive model with stratum-specific intercepts. The bias is free of incidental parameters. Centering the profile score delivers an unbiased estimating equation and, upon integration, an adjusted profile likelihood. A variety of other approaches to constructing modified profile likelihoods are shown to yield equivalent results. However, the global maximizer of the adjusted likelihood lies at infinity for any sample size, and the adjusted profile score has multiple zeros. We argue that the parameters are local maximizers inside or on an ellipsoid centered at the maximum likelihood estimator. |
| Keywords: | adjusted likelihood, autoregression, incidental parameters, local maximizer, recentered estimating equation |
| Date: | 2013–01 |
| URL: | http://d.repec.org/n?u=RePEc:spo:wpmain:info:hdl:2441/dambferfb7dfprc9m052g20qh&r=ets |
| By: | Manabu Asai (Faculty of Economics, Soka University); Michael McAleer (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute, The Netherlands, Department of Quantitative Economics, Complutense University of Madrid, and Institute of Economic Research, Kyoto University.) |
| Abstract: | Modelling covariance structures is known to suffer from the curse of dimensionality. In order to avoid this problem for forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures that accommodates asymmetry and long memory. Using the basic structure of the fMSV model, the authors extend the dynamic correlation MSV model, the onditional/stochastic Wishart autoregressive models, the matrix-exponential MSV model, and the Cholesky MSV model. Empirical results for 7 financial asset returns for US stock returns indicate that the new fMSV models outperform existing dynamic conditional correlation models for forecasting future covariances. Among the new fMSV models, the Cholesky MSV model with long memory and asymmetry shows stable and better forecasting performance for one-day, five-day and ten-day horizons in the periods before, during and after the global financial crisis. |
| Keywords: | Dimension reduction; Factor Model; Multivariate Stochastic Volatility; Leverage Effects; Long Memory; Realized Volatility. |
| JEL: | C32 C53 C58 G17 |
| Date: | 2014–03 |
| URL: | http://d.repec.org/n?u=RePEc:ucm:doicae:1405&r=ets |