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Sökning: LAR1:lu > Högskolan i Jönköping > Shukur Ghazi

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  • Hussain, S., et al. (författare)
  • Parsimonious modelling, testing and forecasting of long-range dependence in wind speed
  • 2004
  • Ingår i: Environmetrics. - John Wiley & Sons. - 1099-095X. ; 15:2, s. 155-171
  • Tidskriftsartikel (refereegranskat)abstract
    • Detecting and estimating long-range dependence are important in the analysis of many environmental time series. This article proposes a periodogram roughness (PR) estimator and describes its uses for testing and estimating the dependence structure. Asymptotic critical values are generated for performing the test, and special attention is given to investigating the properties of the PR regarding size and power. The conventional short-memory models, such as the autoregressive (AR), are shown to be less parsimonious. Forecasting errors of both fractional Gaussian noise (FGN) and fractional autoregressive moving average (FARMA) are investigated by conducting simulation studies. In addition to the PR, maximum likelihood (ML) and semi-parametric (SP) estimators are used and evaluated. Our results have shown that more accurate forecasted points are obtained when using the fractional forecasting. The methods are illustrated using Swedish wind speed data. Copyright (C) 2004 John Wiley Sons, Ltd.
  • Li, Yushu, et al. (författare)
  • Linear and Nonlinear Causality Test in LSTAR Models : Wavelet Decomposition in Nonlinear Environment
  • 2011
  • Ingår i: Journal of Statistical Computation and Simulation. - 0094-9655. ; 81:12, s. 1913-1925
  • Tidskriftsartikel (refereegranskat)abstract
    • In this paper, we use simulated data to investigate the power of different causality tests in a two-dimensional vector autoregressive (VAR) model. The data are presented in a nonlinear environment that is modelled using a logistic smooth transition autoregressive function. We use both linear and nonlinear causality tests to investigate the unidirection causality relationship and compare the power of these tests. The linear test is the commonly used Granger causality F test. The nonlinear test is a non-parametric test based on Baek and Brock [A general test for non-linear Granger causality: Bivariate model. Tech. Rep., Iowa State University and University of Wisconsin, Madison, WI, 1992] and Hiemstra and Jones [Testing for linear and non-linear Granger causality in the stock price–volume relation, J. Finance 49(5) (1994), pp. 1639–1664]. When implementing the nonlinear test, we use separately the original data, the linear VAR filtered residuals, and the wavelet decomposed series based on wavelet multiresolution analysis. The VAR filtered residuals and the wavelet decomposition series are used to extract the nonlinear structure of the original data. The simulation results show that the non-parametric test based on the wavelet decomposition series (which is a model-free approach) has the highest power to explore the causality relationship in nonlinear models.
  • Li, Yushu, et al. (författare)
  • Testing for Unit Root Against LSTAR Models: Wavelet Improvement under GARCH Distortion
  • 2010
  • Ingår i: Communications in statistics. Simulation and computation. - 0361-0918. ; 39:2, s. 277-286
  • Tidskriftsartikel (refereegranskat)abstract
    • In this article, we propose a nonlinear Dickey-Fuller F test for unit root against first-order Logistic Smooth Transition Autoregressive (LSTAR) (1) model with time as the transition variable. The nonlinear Dickey-Fuller F test statistic is established under the null hypothesis of random walk without drift and the alternative model is a nonlinear LSTAR (1) model. The asymptotic distribution of the test is analytically derived while the small sample distributions are investigated by Monte Carlo experiment. The size and power properties of the test were investigated using Monte Carlo experiment. The results showed that there is a serious size distortion for the test when GARCH errors appear in the Data Generating Process (DGP), which led to an over-rejection of the unit root null hypothesis. To solve this problem, we use the Wavelet technique to count off the GARCH distortion and improve the size property of the test under GARCH error. We also discuss the asymptotic distributions of the test statistics in GARCH and wavelet environments.
  • Li, Yushu, et al. (författare)
  • Wavelet Improvement of the Over-rejection of Unit root test under GARCH errors : An Application to Swedish Immigration Data
  • 2011
  • Ingår i: Communications in Statistics - Theory and Methods. - 0361-0926. ; 40:13, s. 2385-2396
  • Tidskriftsartikel (refereegranskat)abstract
    • In this article, we use the wavelet technique to improve the over-rejection problem of the traditional Dickey-Fuller tests for unit root when the data is associated with volatility like the GARCH(1, 1) effect. The logic of this technique is based on the idea that the wavelet spectrum decomposition can separate out information of different frequencies in the data series. We prove that the asymptotic distribution of the test in the wavelet environment is still the same as the traditional Dickey-Fuller type of tests. The finite sample property is improved when the data suffers from GARCH error. The investigation of the size property and the finite sample distribution of the test is carried out by Monte Carlo experiment. An empirical example with data on the net immigration to Sweden during the period 1950-2000 is used to illustrate the performance of the wavelet improved test under GARCH errors. The results reveal that using the traditional Dickey-Fuller type of tests, the unit root hypothesis is rejected while our wavelet improved test do not reject as it is more robust to GARCH errors in finite samples.
  • Mantalos, Panagiotis, et al. (författare)
  • An Examination of the Robustness of the Vector Autoregressive Granger-Causality Test in the Presence of GARCH and Variance Shifts
  • 2007
  • Ingår i: International Review of Business Research Papers. - 1832-9543. ; 3:5, s. 280-296
  • Tidskriftsartikel (refereegranskat)abstract
    • The properties of the Granger-causality test in stationary and stable Vector Autoregressive (VAR) models are studied with different types of volatility processes imposed on the unconditional variance. For this test, it is examined how the size and power properties are affected by different magnitudes of GARCH processes and by structural shifts in the volatility. The study has been conducted by means of Monte Carlo simulations for different sample sizes. Our analysis reveals that substantial GARCH effects influence the size properties of the Granger-causality test, especially in small samples. The power functions of the test are usually slightly lower in the presence of GARCH disturbances compared to the case of white noise residuals. When a structural variance break is imposed, the size problem is rather severe, and the power functions are lower compared to the case with the pure GARCH processes.
  • Mantalos, Panagiotis, et al. (författare)
  • Bootstrap methods for autocorrelation test with uncorrelated but not independent errors
  • 2008
  • Ingår i: Economic Modelling. - Elsevier. - 0264-9993. ; 25:5, s. 1040-1050
  • Tidskriftsartikel (refereegranskat)abstract
    • By using bootstrap technique we investigate the properties of the Breusch [Breusch, T.S., 1978. Testing for autocorrelation in dynamic linear models. Australian Economic Papers 17, 334-355]-Godfrey [Godfrey, L.G., 1978. Testing for higher order serial correlation in regression equations when the regressors include lagged dependent variables. Econometrica 46, 1303-1310] autocorrelation tests in dynamic models with uncorrelated but not independent errors. In this paper we show that, under conditions when the errors are uncorrelated but not independent, even the best likelihood ratio test cannot achieve the asymptotic distribution under the null hypothesis of no autocorrelation. Standard bootstrap methods also flail to produce consistent results. To overcome this problem we applied several bootstrap testing methods for the same: purpose and found the stationary bootstrap and Wild bootstrap with static model to perform adequately among the other bootstrap methods. (C) 2008 Elsevier B.V. All rights reserved.
  • Mantalos, Panagiotis, et al. (författare)
  • The effect of the GARCH(1,1) on autocorrelation tests in dynamic systems of equations
  • 2005
  • Ingår i: Applied Economics. - Routledge. - 1466-4283. ; 37:16, s. 1907-1913
  • Tidskriftsartikel (refereegranskat)abstract
    • Using Monte Carlo methods, the properties of systemwise generalizations of the Breusch Godfrey test for autocorrelated errors are studied when there are some kinds of GARCH effects among the errors. The analysis, regarding the size of the test, reveals that the GARCH have considerable effects of the properties of the test regarding the size, especially in large systems of equations. The corrected LR tests, however, have been shown to perform satisfactorily in small systems when the errors are white noise or they have low GARCH effects, whilst the commonly used TR2 test behaves badly even in single equations. All tests perform badly, however, when the number of equations increases and the GARCH effect is strong. As regards the power of the test, the GARCH was not found to have any significant effects on the power properties of the test.
  • Mantalos, Panagiotis, et al. (författare)
  • The Robustness of the RESET Test to Non-Normal Error Terms
  • 2007
  • Ingår i: Computational Economics. - Springer. - 0927-7099. ; 30:4, s. 393-408
  • Tidskriftsartikel (refereegranskat)abstract
    • In systems ranging from 1 to 10 equations, the size and power of various generalization of the Regression Specification Error Test (RESET) test for functional misspecification are investigated, using both the assymptotic and the bootsrap critical values. Furthermore, the robusteness of the RESET test to various numbers of non-normal error terms has been investigated. The properties of eight versions of the test are studied using Monte Carlo methods. Using the assyptotic critical values together with normally distributed error terms,we find theRao’smultivariate F-test to be best among all other alternative testmethods (i.e.Wald, Lagrange Multiplier and Likelihood Ratio). In the cases of heavy tailed error terms, short or long tailed errors, however, the properties of the bestRao test deteriorates especially in larg systems of equations.By using the bootstrap critical values, we find that the Rao test exhibits correct size but still slightlyunder reject the null hypothesis in cases when the error terms are short tailed. The powerof the test is low, however, in small samples and when the number of equations grows.
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