The Experts below are selected from a list of 2202 Experts worldwide ranked by ideXlab platform
Helmut Lütkepohl - One of the best experts on this subject based on the ideXlab platform.
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Multivariate ARCH and GARCH Models
New Introduction to Multiple Time Series Analysis, 2020Co-Authors: Helmut LütkepohlAbstract:In the previous chapters, we have discussed modelling the conditional mean of the Data Generation Process of a multiple time series, conditional on the past at each particular time point. In that context, the variance or covariance matrix of the conditional distribution was assumed to be time invariant. In fact, in much of the discussion, the residuals or forecast errors were assumed to be independent white noise. Such a simplification is useful and justified in many applications.
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TESTING FOR A UNIT ROOT IN A TIME SERIES WITH A LEVEL SHIFT AT UNKNOWN TIME
Econometric Theory, 2002Co-Authors: Pentti Saikkonen, Helmut LütkepohlAbstract:Unit root tests for time series with level shifts of general form are considered when the timing of the shift is unknown. It is proposed to estimate the nuisance parameters of the Data Generation Process including the shift date in a first step and apply standard unit root tests to the residuals. The estimation of the nuisance parameters is done in such a way that the unit root tests on the residuals have the same limiting distributions as for the case of a known break date. Simulations are performed to investigate the small sample properties of the tests, and empirical examples are discussed to illustrate the procedure.
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Testing for the Cointegrating Rank of a VAR Process With Structural Shifts
Journal of Business & Economic Statistics, 2000Co-Authors: Pentti Saikkonen, Helmut LütkepohlAbstract:Tests for the cointegrating rank of a vector autoregressive Process are considered that allow for possible exogenous shifts in the mean of the Data-Generation Process. The break points are assumed to be known a priori. It is proposed to estimate and remove the deterministic terms such as mean, linear-trend term, and a shift in a first step. Then systems cointegration tests are applied to the adjusted series. The resulting tests are shown to have known limiting null distributions that are free of nuisance parameters and do not depend on the break point. The tests are applied for analyzing the number of cointegrating relations in two German money-demand systems.
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Order Selection in Testing for the Cointegrating Rank of a VAR Process
1997Co-Authors: Helmut Lütkepohl, Pentti SaikkonenAbstract:The impact of the choice of the lag length on tests for the number of cointegration relations in a vector autoregressive (VAR) Process is investigated. It is shown that the asymptotic distribution of likelihood ratio (LR) tests for the cointegrating rank remains unchanged if the true Data Generation Process (DGP) is of finite order and a consistent model selection criterion is used for choosing the lag length. A similar result also holds if the true DGP is an in finite order VAR. In a simulation study we find that small sample power and size of LR cointegration tests strongly depend on the choice of the lag order.
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Fitting Finite Order VAR Models to Infinite Order Processes
Introduction to Multiple Time Series Analysis, 1991Co-Authors: Helmut LütkepohlAbstract:In the previous chapters we have derived properties of models, estimators, forecasts, and test statistics under the assumption of a true model. We have also argued that such an assumption is virtually never fulfilled in practice. In other words, in practice, all we can hope for is a model that provides a useful approximation to the actual Data Generation Process of a given multiple time series. In this chapter we will, to some extent, take into account this state of affairs and assume that an approximating rather than a true model is fitted. Specifically we assume that the true Data Generation Process is a stable, infinite order VAR Process and, for a given sample size T, a finite order VAR(p) is fitted to the Data.
Sarabjeet Kochhar - One of the best experts on this subject based on the ideXlab platform.
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Innovative Applications in Data Mining - Towards Characterization of the Data Generation Process
Innovative Applications in Data Mining, 2020Co-Authors: Vasudha Bhatnagar, Sarabjeet KochharAbstract:Data Mining applications have found interesting applications in commercial and scientific domains. Last two decades have seen rapid strides in development of elegant algorithms that induce useful predictive and descriptive models from large Data repositories available widely.
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towards characterization of the Data Generation Process
Innovative Applications in Data Mining, 2009Co-Authors: Vasudha Bhatnagar, Sarabjeet KochharAbstract:Data Mining applications have found interesting applications in commercial and scientific domains. Last two decades have seen rapid strides in development of elegant algorithms that induce useful predictive and descriptive models from large Data repositories available widely.
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ISDA - Beyond Mining: Characterizing the Data Generation Process
Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), 2007Co-Authors: Vasudha Bhatnagar, Sarabjeet KochharAbstract:Data in Databases is the outcome of an underlying Data Generation Process (dgp), which is a complex function of unknown number of parameters. A few of these parameters may be known and understood by the domain expert or the end-user. The trends hidden in the evolving Databases evolve with time and are manifestations of the underlying dgp. We believe that one of the objectives of the KDD technology is to discover the unknown parameters of the dgp and enable its characterization. This is a pre-requisite for developing capabilities to understand and possibly control the dgp. In this paper we draw the attention of the Data mining research community towards the need to look beyond mining. The community is already paying serious attention to the issue of changes in the patterns discovered from evolving Data repositories. We identify the commanalities in these works and show that they fit in a common framework where the discovered knowledge is consolidated and deviations are quantified. We extend this framework by introducing abstraction levels facilitation characterization of the underlying Data Generation Process.
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Beyond Mining: Characterizing the Data Generation Process
Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), 2007Co-Authors: Vasufha Bhatnagar, Sarabjeet KochharAbstract:Data in Databases is the outcome of an underlying Data Generation Process (dgp), which is a complex function of unknown number of parameters. A few of these parameters may be known and understood by the domain expert or the end-user. The trends hidden in the evolving Databases evolve with time and are manifestations of the underlying dgp. We believe that one of the objectives of the KDD technology is to discover the unknown parameters of the dgp and enable its characterization. This is a pre-requisite for developing capabilities to understand and possibly control the dgp. In this paper we draw the attention of the Data mining research community towards the need to look beyond mining. The community is already paying serious attention to the issue of changes in the patterns discovered from evolving Data repositories. We identify the commanalities in these works and show that they fit in a common framework where the discovered knowledge is consolidated and deviations are quantified. We extend this framework by introducing abstraction levels facilitation characterization of the underlying Data Generation Process.
Pentti Saikkonen - One of the best experts on this subject based on the ideXlab platform.
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Testing for a Unit Root in a Time Series with a Level Shift at Unknown Time
2020Co-Authors: Helmut Luetkepohl, Pentti SaikkonenAbstract:Unit root tests for time series with level shifts of general form are considered when the timing of the shift is unknown. It is proposed to estimate the nuisance parameters of the Data Generation Process including the shift date in a first step and apply standard unit root tests to the residuals. The estimation of the nuisance parameters is done in such a way that the unit root tests on the residuals have limiting distributions for which critical values are tabulated elsewhere in the literature. Empirical examples are discussed to illustrate the procedure.
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TESTING FOR A UNIT ROOT IN A TIME SERIES WITH A LEVEL SHIFT AT UNKNOWN TIME
Econometric Theory, 2002Co-Authors: Pentti Saikkonen, Helmut LütkepohlAbstract:Unit root tests for time series with level shifts of general form are considered when the timing of the shift is unknown. It is proposed to estimate the nuisance parameters of the Data Generation Process including the shift date in a first step and apply standard unit root tests to the residuals. The estimation of the nuisance parameters is done in such a way that the unit root tests on the residuals have the same limiting distributions as for the case of a known break date. Simulations are performed to investigate the small sample properties of the tests, and empirical examples are discussed to illustrate the procedure.
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Testing for the Cointegrating Rank of a VAR Process With Structural Shifts
Journal of Business & Economic Statistics, 2000Co-Authors: Pentti Saikkonen, Helmut LütkepohlAbstract:Tests for the cointegrating rank of a vector autoregressive Process are considered that allow for possible exogenous shifts in the mean of the Data-Generation Process. The break points are assumed to be known a priori. It is proposed to estimate and remove the deterministic terms such as mean, linear-trend term, and a shift in a first step. Then systems cointegration tests are applied to the adjusted series. The resulting tests are shown to have known limiting null distributions that are free of nuisance parameters and do not depend on the break point. The tests are applied for analyzing the number of cointegrating relations in two German money-demand systems.
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Order Selection in Testing for the Cointegrating Rank of a VAR Process
1997Co-Authors: Helmut Lütkepohl, Pentti SaikkonenAbstract:The impact of the choice of the lag length on tests for the number of cointegration relations in a vector autoregressive (VAR) Process is investigated. It is shown that the asymptotic distribution of likelihood ratio (LR) tests for the cointegrating rank remains unchanged if the true Data Generation Process (DGP) is of finite order and a consistent model selection criterion is used for choosing the lag length. A similar result also holds if the true DGP is an in finite order VAR. In a simulation study we find that small sample power and size of LR cointegration tests strongly depend on the choice of the lag order.
B. Korel - One of the best experts on this subject based on the ideXlab platform.
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ITC - Software test Data Generation using the chaining approach
Proceedings of 1995 IEEE International Test Conference (ITC), 1995Co-Authors: R. Ferguson, B. KorelAbstract:Software testing, specifically, test Data Generation is very labor-intensive and expensive. As a result, it accounts for a significant portion of software system development cost. In this paper we present a chaining approach for automated software test Data Generation. The chaining approach uses Data dependence analysis to guide the test Data Generation Process. The experiments have shown that the chaining approach may significantly improve the chances of finding test Data as compared to the existing methods of automated test Data Generation.
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Software test Data Generation using the chaining approach
Proceedings of 1995 IEEE International Test Conference (ITC), 1995Co-Authors: R. Ferguson, B. KorelAbstract:Software testing, specifically, test Data Generation is very labor-intensive and expensive. As a result, it accounts for a significant portion of software system development cost. In this paper we present a chaining approach for automated software test Data Generation. The chaining approach uses Data dependence analysis to guide the test Data Generation Process. The experiments have shown that the chaining approach may significantly improve the chances of finding test Data as compared to the existing methods of automated test Data Generation.
Kirk Harland - One of the best experts on this subject based on the ideXlab platform.
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improving the synthetic Data Generation Process in spatial microsimulation models
Environment and Planning A, 2009Co-Authors: Dianna Smith, Graham Clarke, Kirk HarlandAbstract:Simulation models are increasingly used in applied research to create synthetic micropopulations and predict possible individual-level outcomes of policy intervention. Previous research highlights the relevance of simulation techniques in estimating the potential outcomes of changes in areas such as taxation and child benefit policy, crime, education, or health inequalities. To date, however, there is very little published research on the creation, calibration, and testing of such micropopulations and models, and little on the issue of how well synthetic Data can fit locally as opposed to globally in such models. This paper discusses the Process of improving the Process of synthetic micropopulation Generation with the aim of improving and extending existing spatial microsimulation models. Experiments using different variable configurations to constrain the models are undertaken with the emphasis on producing a suite of models to match the different sociodemographic conditions found within a typical city. The results show that creating Processes to generate area-specific synthetic populations, which reflect the diverse populations within the study area, provides more accurate population estimates for future policy work than the traditional global model configurations.
