The Experts below are selected from a list of 49647 Experts worldwide ranked by ideXlab platform
Yogesh Dwivedi - One of the best experts on this subject based on the ideXlab platform.
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a test for second order Stationarity of a time series based on the discrete fourier transform
Journal of Time Series Analysis, 2011Co-Authors: Yogesh Dwivedi, Suhasini Subba RaoAbstract:We consider a zero mean discrete time series, and define its discrete Fourier transform at the canonical frequencies. It can be shown that the discrete Fourier transform is asymptotically uncorrelated at the canonical frequencies if and if only the time series is second order stationary. Exploiting this important property, we construct a Portmanteau type test statistic for testing Stationarity of the time series. It is shown that under the null of Stationarity, the test statistic is approximately a chi square distribution. To examine the power of the test statistic, the asymptotic distribution under the locally stationary alternative is established. It is shown to be a generalised noncentral chi-square, where the noncentrality parameter measures the deviation from Stationarity. The test is illustrated with simulations, where is it shown to have good power.
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a test for second order Stationarity of a time series based on the discrete fourier transform
Journal of Time Series Analysis, 2011Co-Authors: Yogesh DwivediAbstract:We consider a zero mean discrete time series, and define its discrete Fourier transform (DFT) at the canonical frequencies. It can be shown that the DFT is asymptotically uncorrelated at the canonical frequencies if and only if the time series is second-order stationary. Exploiting this important property, we construct a Portmanteau type test statistic for testing Stationarity of the time series. It is shown that under the null of Stationarity, the test statistic has approximately a chi square distribution. To examine the power of the test statistic, the asymptotic distribution under the locally stationary alternative is established. It is shown to be a generalized non-central chi square, where the non-centrality parameter measures the deviation from Stationarity. The test is illustrated with simulations, where is it shown to have good power.
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a test for second order Stationarity of a time series based on the discrete fourier transform technical report
arXiv: Methodology, 2009Co-Authors: Yogesh DwivediAbstract:We consider a zero mean discrete time series, and define its discrete Fourier transform at the canonical frequencies. It is well known that the discrete Fourier transform is asymptotically uncorrelated at the canonical frequencies if and if only the time series is second order stationary. Exploiting this important property, we construct a Portmanteau type test statistic for testing Stationarity of the time series. It is shown that under the null of Stationarity, the test statistic is approximately a chi square distribution. To examine the power of the test statistic, the asymptotic distribution under the locally stationary alternative is established. It is shown to be a type of noncentral chi-square, where the noncentrality parameter measures the deviation from Stationarity. The test is illustrated with simulations, where is it shown to have good power. Some real examples are also included to illustrate the test.
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A test for second order Stationarity of a time series based on the Discrete Fourier Transform,” arXiv:0911.4744
2009Co-Authors: Yogesh Dwivedi, Suhasini Subba RaoAbstract:We consider a zero mean discrete time series, and define its discrete Fourier transform at the canonical frequencies. It can be shown that the discrete Fourier transform is asymptotically uncorrelated at the canonical frequencies if and if only the time series is second order stationary. Exploiting this important property, we construct a Portmanteau type test statistic for testing Stationarity of the time series. It is shown that under the null of Stationarity, the test statistic is approximately a chi square distribution. To examine the power of the test statistic, the asymptotic distribution under the locally stationary alternative is established. It is shown to be a generalised noncentral chi-square, where the noncentrality parameter measures the deviation from Stationarity. The test is illustrated with simulations, where is it shown to have good power
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A test for second order Stationarity of a time series based on the Discrete Fourier Transform,” arXiv:0911.4744
2009Co-Authors: Yogesh Dwivedi, Suhasini Subba RaoAbstract:We consider a zero mean discrete time series, and define its discrete Fourier transform at the canonical Fourier frequencies. It is well known that the discrete Fourier transform is asymptotically uncorrelated at the canonical frequencies if and if only the time series is second order stationary. Exploiting this important property, we construct a Portmanteau type test statistic for testing Stationarity of the time series. It is shown that under the null of Stationarity, the test statistic is approximately a chi square distribution. To examine the power of the test statistic, the asymptotic distribution under the locally stationary alternative is established. It is shown to be a type of noncentral chi-square, where the noncentrality parameter measures the deviation from Stationarity. The test is illustrated with simulations, where is it shown to have good power. Some real examples are also included to illustrate the test. Kew words and phrases Discrete Fourier Transform, local Stationarity, Portmanteau test, test for second order Stationarity.
Suhasini Subba Rao - One of the best experts on this subject based on the ideXlab platform.
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a test for second order Stationarity of a time series based on the discrete fourier transform
Journal of Time Series Analysis, 2011Co-Authors: Yogesh Dwivedi, Suhasini Subba RaoAbstract:We consider a zero mean discrete time series, and define its discrete Fourier transform at the canonical frequencies. It can be shown that the discrete Fourier transform is asymptotically uncorrelated at the canonical frequencies if and if only the time series is second order stationary. Exploiting this important property, we construct a Portmanteau type test statistic for testing Stationarity of the time series. It is shown that under the null of Stationarity, the test statistic is approximately a chi square distribution. To examine the power of the test statistic, the asymptotic distribution under the locally stationary alternative is established. It is shown to be a generalised noncentral chi-square, where the noncentrality parameter measures the deviation from Stationarity. The test is illustrated with simulations, where is it shown to have good power.
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A test for second order Stationarity of a time series based on the Discrete Fourier Transform,” arXiv:0911.4744
2009Co-Authors: Yogesh Dwivedi, Suhasini Subba RaoAbstract:We consider a zero mean discrete time series, and define its discrete Fourier transform at the canonical frequencies. It can be shown that the discrete Fourier transform is asymptotically uncorrelated at the canonical frequencies if and if only the time series is second order stationary. Exploiting this important property, we construct a Portmanteau type test statistic for testing Stationarity of the time series. It is shown that under the null of Stationarity, the test statistic is approximately a chi square distribution. To examine the power of the test statistic, the asymptotic distribution under the locally stationary alternative is established. It is shown to be a generalised noncentral chi-square, where the noncentrality parameter measures the deviation from Stationarity. The test is illustrated with simulations, where is it shown to have good power
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A test for second order Stationarity of a time series based on the Discrete Fourier Transform,” arXiv:0911.4744
2009Co-Authors: Yogesh Dwivedi, Suhasini Subba RaoAbstract:We consider a zero mean discrete time series, and define its discrete Fourier transform at the canonical Fourier frequencies. It is well known that the discrete Fourier transform is asymptotically uncorrelated at the canonical frequencies if and if only the time series is second order stationary. Exploiting this important property, we construct a Portmanteau type test statistic for testing Stationarity of the time series. It is shown that under the null of Stationarity, the test statistic is approximately a chi square distribution. To examine the power of the test statistic, the asymptotic distribution under the locally stationary alternative is established. It is shown to be a type of noncentral chi-square, where the noncentrality parameter measures the deviation from Stationarity. The test is illustrated with simulations, where is it shown to have good power. Some real examples are also included to illustrate the test. Kew words and phrases Discrete Fourier Transform, local Stationarity, Portmanteau test, test for second order Stationarity.
Russell Smyth - One of the best experts on this subject based on the ideXlab platform.
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convergence in energy consumption per capita among asean countries
Energy Policy, 2014Co-Authors: Vinod Mishra, Russell SmythAbstract:Abstract We test for convergence in energy consumption per capita among ASEAN countries over the period 1971 to 2011 using the panel KPSS Stationarity test and panel Lagrange multiplier (LM) unit root test. The results for the panel Stationarity and unit root tests with structural breaks find support for energy convergence in ASEAN.
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convergence in energy consumption per capita among asean countries
Research Papers in Economics, 2014Co-Authors: Vinod Mishra, Russell SmythAbstract:We test for convergence in energy consumption per capita among the ASEAN-5 over the period 1971 to 2011 using the univariate and panel KPSS Stationarity test with, and without, structural breaks and the univariate and panel lagrange multiplier (LM) unit root test with, and without, structural breaks. There is mixed evidence of convergence with the univariate tests with breaks, depending on whether the null is specified as Stationarity or a unit root. The results for the panel Stationarity and unit root tests with structural breaks find support for energy convergence in the ASEAN-5.
Pierre Borgnat - One of the best experts on this subject based on the ideXlab platform.
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testing Stationarity with surrogates a time frequency approach
IEEE Transactions on Signal Processing, 2010Co-Authors: Pierre Borgnat, Paul Honeine, Cedric Richard, Patrick Flandrin, Jun XiaoAbstract:An operational framework is developed for testing Stationarity relatively to an observation scale, in both stochastic and deterministic contexts. The proposed method is based on a comparison between global and local time-frequency features. The originality is to make use of a family of stationary surrogates for defining the null hypothesis of Stationarity and to base on them two different statistical tests. The first one makes use of suitably chosen distances between local and global spectra, whereas the second one is implemented as a one-class classifier, the time- frequency features extracted from the surrogates being interpreted as a learning set for Stationarity. The principle of the method and of its two variations is presented, and some results are shown on typical models of signals that can be thought of as stationary or nonstationary, depending on the observation scale used.
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statistical hypothesis testing with time frequency surrogates to check signal Stationarity
International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Cedric Richard, Hassan Amoud, Paul Honeine, Andre Ferrari, Patrick Flandrin, Pierre BorgnatAbstract:An operational framework is developed for testing Stationarity relatively to an observation scale. The proposed method makes use of a family of stationary surrogates for defining the null hypothesis of Stationarity. As a further contribution to the field, we demonstrate the strict-sense Stationarity of surrogate signals and we exploit this property to derive the asymptotic distributions of their spectrogram and power spectral density. A statistical hypothesis testing framework is then proposed to check signal Stationarity. Finally, some results are shown on a typical model of signals that can be thought of as stationary or nonstationary, depending on the observation scale used.
Yoram Halevy - One of the best experts on this subject based on the ideXlab platform.
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Time Consistency: Stationarity and Time Invariance
2015Co-Authors: Yoram HalevyAbstract:A sequence of experiments documents static and dynamic ``preference reversals'' between sooner-smaller and later-larger rewards, when the sooner reward could be immediate. The theoretically-motivated design permits separate identification of time-consistent, stationary and time-invariant choices. At least half of the subjects are time consistent, but only three-quarters of them exhibit stationary choices. About half of subjects with time inconsistent choices have stationary preferences. These results challenge the view that present-bias preferences are the main source of time inconsistent choices.
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Time Consistency: Stationarity and Time Invariance
Econometrica, 2015Co-Authors: Yoram HalevyAbstract:A sequence of experiments documents static and dynamic "preference reversals" between sooner-smaller and later-larger rewards, when the sooner reward could be immediate. The theoretically motivated design permits separate identification of time consistent, stationary ,a ndtime invariant choices. At least half of the subjects are time consistent, but only three-quarters of them exhibit stationary choices. About half of subjects with time inconsistent choices have stationary preferences. These results chal- lenge the view that present-bias preferences are the main source of time inconsistent choices. THE PAST TWENTY YEARS have seen a surge of interest in time inconsistent preferences. Mostly, this has been motivated by psychological experiments and introspection that have suggested the existence of "present-bias": a decision maker prefers smaller immediate reward to a larger delayed reward, but when she is asked about her preferences between these two alternatives when both are equally shifted into the future, her preferences are reversed. Although there is no inconsistency per se in her answers, this behavior has been taken to suggest that when the decision maker will be asked to update her choices in the future, she will generally deviate from her ex ante plans. Our goal in the present study is to perform a dynamic preference reversal experiment. As such, one is required to extend the standard framework of pref- erences over temporal payments to a dated collection of such preferences. This extension allows us to formally define three distinct properties. Stationarity im- plies that ranking of temporal payments depends only on the time distance and payment distance between the alternatives. Time invariance assumes that the decision maker evaluates each temporal payment relative to the evaluation pe-
