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Timothy J Vogelsang - One of the best experts on this subject based on the ideXlab platform.
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powerful trend function tests that are robust to strong Serial Correlation with an application to the prebisch singer hypothesis
Journal of Business & Economic Statistics, 2005Co-Authors: Helle Bunzel, Timothy J VogelsangAbstract:We propose tests for hypotheses on the parameters of the deterministic trend function of a univariate time series. The tests do not require knowledge of the form of Serial Correlation in the data, and they are robust to strong Serial Correlation. The data can contain a unit root and still have the correct size asymptotically. The tests that we analyze are standard heteroscedasticity autoCorrelation robust tests based on nonparametric kernel variance estimators. We analyze these tests using the fixed-b asymptotic framework recently proposed by Kiefer and Vogelsang. This analysis allows us to analyze the power properties of the tests with regard to bandwidth and kernel choices. Our analysis shows that among popular kernels, specific kernel and bandwidth choices deliver tests with maximal power within a specific class of tests. Based on the theoretical results, we propose a data-dependent bandwidth rule that maximizes integrated power. Our recommended test is shown to have power that dominates a related test...
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powerful trend function tests that are robust to strong Serial Correlation with an application to the prebisch singer hypothesis
Research Papers in Economics, 2003Co-Authors: Helle Bunzel, Timothy J VogelsangAbstract:In this paper we propose tests for hypotheses regarding the parameters of the deterministic trend function of a univariate time series. The tests do not require knowledge of the form of Serial Correlation in the data and they are robust to strong Serial Correlation. The data can contain a unit root and the tests still have the correct size asymptotically. The tests we analyze are standard heteroskedasticity autoCorrelation (HAC) robust tests based on nonparametric kernel variance estimators. We analyze these tests using the i¾…xed-b asymptotic framework recently proposed by Kiefer and Vogelsang (2002). This analysis allows us to analyze the power properties of the tests with regards to bandwidth and kernel choices. Our analysis shows that among popular kernels, there are specii¾…c kernel and bandwidth choices that deliver tests with maximal power within a specii¾…c class of tests. Based on the theoretical results, we propose a data dependent bandwidth rule that maximizes integrated power. Our recommended test is shown to have power that dominates a related test proposed by Vogelsang (1998). We apply the recommended test to the logarithm of a net barter terms of trade series and we i¾…nd that this series has a statistically signii¾…cant negative slope. This i¾…nding is consistent with the well known Prebisch-Singer hypothesis.
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powerful trend function tests that are robust to strong Serial Correlation with an application to the prebisch singer hypothesis
Social Science Research Network, 2003Co-Authors: Helle Bunzel, Timothy J VogelsangAbstract:In this paper we propose tests for hypothesis regarding the parameters of a the deterministic trend function of a univariate time series. The tests do not require knowledge of the form of Serial Correlation in the data and they are robust to strong Serial Correlation. The data can contain a unit root and the tests still have the correct size asymptotically. The tests we analyze are standard heteroskedasticity autoCorrelation (HAC) robust tests based on nonparametric kernel variance estimators. We analyze these tests using the small-b asymptotic framework recently proposed by Kiefer and Vogelsang (2002). This analysis allows us to analyze the power properties of the tests with regards to bandwidth and kernel choices. Our analysis shows that among popular kernels, there are specific kernel and bandwidth choices that deliver tests with maximal power within a specific class of tests. We apply the recommended tests to the logarithm of a net barter terms of trade series and we find that this series has a statistically significant negative slope. This finding is consistent with the well known Prebisch-Singer hypothesis. Because our tests are robust to strong Serial Correlation or a unit root in the data, our results in support of the Prebisch-Singer hypothesis are relatively strong.
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the application of size robust trend statistics to global warming temperature series
Journal of Climate, 2002Co-Authors: Thomas B Fomby, Timothy J VogelsangAbstract:Abstract In this note, new evidence is provided confirming that global temperature series spanning back to the mid-1800s have statistically significant positive trends. Although there is a growing consensus that global temperatures are on the rise systematically, some recent studies have pointed out that strong Serial Correlation (or a unit root) in global temperature data could, in theory, generate spurious evidence of a significant positive trend. In other words, strong Serially correlated data can mimic trending behavior over fixed periods of time. A Serial-Correlation–robust trend test recently was proposed that controls for the possibility of spurious evidence due to strong Serial Correlation. This new test is valid whether the errors are stationary or have a unit root (strong Serial Correlation). This test also has the attractive feature that it does not require estimates of Serial Correlation nuisance parameters. The test is applied to six annual global temperature series, and it provides strong ev...
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testing for a shift in trend when Serial Correlation is of unknown form
2001Co-Authors: Timothy J VogelsangAbstract:In this paper test statistics are proposed that can be used to test for shifts in the trend function of au nivariate time series. The tests are valid in the presence of general forms of Serial Correlation in the errors and can be used without having to estimate the Serial Correlation parameters either parametrically or nonparametrically. The tests are valid for both I(0) and I(1) errors. The tests are designed to detect a single break at a known or unknown date. Asymptotic distributions are tabulated. A local asymptotic analysis is used to evaluate the size and power of the tests. Local asymptotic power indicates that the new tests have nontrivial asymptotic power. If the supremum statistic is used when the break date is unknown, one of the new tests has greater power than currently available statistics. Simulations are used to assess the finite sample size and power of the tests. A discussion is given on computing confidence intervals for trend function parameters when there is a trend shift at an unknown date. Such confidence intervals are computed for GNP growth rates of 16 countries using historical data.
Takashi Yamagata - One of the best experts on this subject based on the ideXlab platform.
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a robust approach to heteroskedasticity error Serial Correlation and slope heterogeneity for large linear panel data models with interactive effects
Social Science Research Network, 2019Co-Authors: Guowei Cui, Takashi Yamagata, Kazuhiko Hayakawa, Shuichi NagataAbstract:In this paper, we propose a robust approach against heteroskedasticity, error Serial Correlation and slope heterogeneity for large linear panel data models. First, we establish the asymptotic validity of the Wald test based on the widely used panel heteroskedasticity and autoCorrelation consistent (HAC) variance estimator of the pooled estimator under random coefficient models. Then, we show that a similar result holds with the proposed bias-corrected Bai's (2009) estimator for models with unobserved interactive effects. Our new theoretical result justifies the use of the same slope estimator and the variance estimator, both for slope homogeneous and heterogeneous models. This robust approach can significantly reduce the model selection uncertainty for applied researchers. In addition, we propose a novel test for the Correlation and dependence of the random coefficient with covariates. The test is of great importance, since the widely used estimators and/or its variance estimators can become inconsistent when the variation of coefficients depends on covariates, in general. The finite sample evidence supports the usefulness and reliability of our approach.
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a robust approach to heteroskedasticity error Serial Correlation and slope heterogeneity for large linear panel data models with interactive effects
Research Papers in Economics, 2019Co-Authors: Guowei Cui, Kazuhiko Hayakawa, Shuichi Nagata, Takashi YamagataAbstract:In this paper, we propose a robust approach against heteroskedasticity, error Serial Correlation and slope heterogeneity for large linear panel data models. First, we establish the asymptotic validity of the Wald test based on the widely used panel heteroskedasticity and autoCorrelation consistent (HAC) variance estimator of the pooled estimator under random coefficient models. Then, we show that a similar result holds with the proposed bias-corrected principal component-based estimators for models with unobserved interactive effects. Our new theoretical result justifies the use of the same slope estimator and the variance estimator, both for slope homogeneous and heterogeneous models. This robust approach can significantly reduce the model selection uncertainty for applied researchers. In addition, we propose a novel test for the Correlation and dependence of the random coefficient with covariates. The test is of great importance, since the widely used estimators and/or its variance estimators can become inconsistent when the variation of coefficients depends on covariates, in general. The finite sample evidence supports the usefulness and reliability of our approach.
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a joint Serial Correlation test for linear panel data models
Journal of Econometrics, 2008Co-Authors: Takashi YamagataAbstract:Abstract This paper proposes a joint error Serial Correlation test to be applied to linear panel data models after generalised method of moments estimation. This new test is an alternative inferential tool to both the m 2 test of [Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277–297] and the overidentifying restrictions test. The proposed test, called the m ( 2 , p ) 2 test, involves an examination of the joint significance of estimates of second to p th-order (first differenced) error Serial Correlations. The small sample properties of the m ( 2 , p ) 2 test are investigated by means of Monte Carlo experiments. The evidence shows that the proposed test mostly outperforms the conventional m 2 test and has high power when the overidentifying restrictions test does not, under a variety of alternatives including slope heterogeneity and cross section dependence.
Chihwa Kao - One of the best experts on this subject based on the ideXlab platform.
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testing cross sectional Correlation in large panel data models with Serial Correlation
Econometrics, 2016Co-Authors: Badi H. Baltagi, Chihwa Kao, Bin PengAbstract:This paper considers the problem of testing cross-sectional Correlation in large panel data models with Serially-correlated errors. It finds that existing tests for cross-sectional Correlation encounter size distortions with Serial Correlation in the errors. To control the size, this paper proposes a modification of Pesaran’s Cross-sectional Dependence (CD) test to account for Serial Correlation of an unknown form in the error term. We derive the limiting distribution of this test as N , T → ∞ . The test is distribution free and allows for unknown forms of Serial Correlation in the errors. Monte Carlo simulations show that the test has good size and power for large panels when Serial Correlation in the errors is present.
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testing cross sectional Correlation in large panel data models with Serial Correlation
Research Papers in Economics, 2016Co-Authors: Badi H. Baltagi, Chihwa Kao, Bin PengAbstract:This paper considers the problem of testing cross-sectional Correlation in large panel data models with Serially correlated errors. It finds that existing tests for cross-sectional Correlation encounter size distortions with Serial Correlation in the errors. To control the size, this paper proposes a modification of Pesaran’s CD test to account for Serial Correlation of an unknown form in the error term. We derive the limiting distribution of this test as (N, T) → ∞. The test is distribution free and allows for unknown forms of Serial Correlation in the errors. Monte Carlo simulations show that the test has good size and power for large panels when Serial Correlation in the errors is present. JEL Classification: C13; C33 Key words: Cross-sectional Correlation Test; Serial Correlation; Large Panel Data Model
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wavelet based testing for Serial Correlation of unknown form in panel models
Econometrica, 2004Co-Authors: Yongmiao Hong, Chihwa KaoAbstract:Wavelet analysis is a new mathematical method developed as a unified field of science over the last decade or so. As a spatially adaptive analytic tool, wavelets are useful for capturing Serial Correlation where the spectrum has peaks or kinks, as can arise from persistent dependence, seasonality, and other kinds of periodicity. This paper proposes a new class of generally applicable wavelet-based tests for Serial Correlation of unknown form in the estimated residuals of a panel regression model, where error components can be one-way or two-way, individual and time effects can be fixed or random, and regressors may contain lagged dependent variables or deterministic/stochastic trending variables. Our tests are applicable to unbalanced heterogenous panel data. They have a convenient null limit N(0,1) distribution. No formulation of an alternative model is required, and our tests are consistent against Serial Correlation of unknown form even in the presence of substantial in homogeneity in Serial Correlation across individuals. This is in contrast to existing Serial Correlation tests for panel models, which ignore inhomogeneity in Serial Correlation across individuals by assuming a common alternative, and thus have no power against the alternatives where the average of Serial Correlations among individuals is close to zero. We propose and justify a data-driven method to choose the smoothing parameter-the finest scale in wavelet spectral estimation, making the tests completely operational in practice. The data-driven finest scale automatically converges to zero under the null hypothesis of no Serial Correlation and diverges to infinity as the sample size increases under the alternative, ensuring the consistency of our tests. Simulation shows that our tests perform well in small and finite samples relative to some existing tests.
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wavelet based testing for Serial Correlation of unknown form in panel models
Research Papers in Economics, 2000Co-Authors: Yongmiao Hong, Chihwa KaoAbstract:Wavelet analysis is a new mathematical tool developed as a unified field of science over the last decade. As spatially adaptive analytic tools, wavelets are useful for capturing Serial Correlation where the spectrum has peaks or kinks, as can arise from persistent/strong dependence, seasonality or use of seasonal data such as quarterly and monthly data, business cycles, and other kinds of periodicity. This paper proposes a new class of wavelet-based tests for Serial Correlation of unknown form in the estimated residuals of an error component model, where the error components can be one-way or two-way, the individual and time effects can be fixed or random, the regressors may contain lagged dependent variables or deterministic/stochastic trending variables. The proposed tests are applicable to unbalanced heterogeneous panel data. They have a convenient null limit N (0,1) distribution. No formulation of an alternative is required, and the tests are consistent against Serial Correlation of unknown form. We propose and justify a data-driven finest scale that, in an automatic manner, converges to zero under the null hypothesis of no Serial Correlation and grows to infinity as the sample size increases under the alternative, ensuring the consistency of the proposed tests. Simulation studies show that the new tests perform rather well in small and finite samples in comparison with some existing popular tests for panel models, and can be used as an effective evaluation procedure for panel models. KEY WORD: error component, panel model, hypothesis testing, Serial Correlation of unknown form, spectral peak, unbalanced panel data, wavelet.
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wavelet based testing for Serial Correlation of unknown form in panel models
Social Science Research Network, 2000Co-Authors: Yongmiao Hong, Chihwa KaoAbstract:Wavelet analysis is a new mathematical tool developed as a unified field of science over the last decade. As spatially adaptive analytic tools, wavelets are useful for capturing Serial Correlation where the spectrum has peaks or kinks, as can arise from persistent/strong dependence, seasonality or use of seasonal data such as quarterly and monthly data, business cycles, and other kinds of periodicity. This paper proposes a new class of wavelet-based tests for Serial Correlation of unknown form in the estimated residuals of an error component model, where the error components can be one-way or two-way, the individual and time effects can be fixed or random, the regressors may contain lagged dependent variables or deterministic/stochastic trending variables. The proposed tests are applicable to unbalanced heterogeneous panel data. They have a convenient null limit N (0,1) distribution. No formulation of an alternative is required, and the tests are consistent against Serial Correlation of unknown form. We propose and justify a data-driven finest scale, in an automatic manner, converges to zero under the null hypothesis of no Serial Correlation and grows to infinity as the sample size increases under the alternative, ensuring the consistency of the proposed tests. Simulation studies show that the new tests perform rather well in small and finite samples in comparison with some existing popular tests for panel models and can be used as an effective evaluation procedure for panel models.
Karen B. Strier - One of the best experts on this subject based on the ideXlab platform.
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low demographic variability in wild primate populations fitness impacts of variation covariation and Serial Correlation in vital rates
The American Naturalist, 2011Co-Authors: William F. Morris, Jeanne Altmann, Diane K. Brockman, Marina Cords, Linda M. Fedigan, Anne E. Pusey, Tara S. Stoinski, Anne M. Bronikowski, Susan C. Alberts, Karen B. StrierAbstract:Abstract: In a stochastic environment, long‐term fitness can be influenced by variation, covariation, and Serial Correlation in vital rates (survival and fertility). Yet no study of an animal population has parsed the contributions of these three aspects of variability to long‐term fitness. We do so using a unique database that includes complete life‐history information for wild‐living individuals of seven primate species that have been the subjects of long‐term (22–45 years) behavioral studies. Overall, the estimated levels of vital rate variation had only minor effects on long‐term fitness, and the effects of vital rate covariation and Serial Correlation were even weaker. To explore why, we compared estimated variances of adult survival in primates with values for other vertebrates in the literature and found that adult survival is significantly less variable in primates than it is in the other vertebrates. Finally, we tested the prediction that adult survival, because it more strongly influences fitnes...
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Low Demographic Variability in Wild Primate Populations: Fitness Impacts of Variation, Covariation, and Serial Correlation in Vital Rates
The American naturalist, 2010Co-Authors: William F. Morris, Jeanne Altmann, Diane K. Brockman, Marina Cords, Linda M. Fedigan, Anne E. Pusey, Tara S. Stoinski, Anne M. Bronikowski, Susan C. Alberts, Karen B. StrierAbstract:In a stochastic environment, long-term fitness can be influenced by variation, covariation, and Serial Correlation in vital rates (survival and fertility). Yet no study of an animal population has parsed the contributions of these three aspects of variability to long-term fitness. We do so using a unique database that includes complete life-history information for wild-living individuals of seven primate species that have been the subjects of long-term (22-45 years) behavioral studies. Overall, the estimated levels of vital rate variation had only minor effects on long-term fitness, and the effects of vital rate covariation and Serial Correlation were even weaker. To explore why, we compared estimated variances of adult survival in primates with values for other vertebrates in the literature and found that adult survival is significantly less variable in primates than it is in the other vertebrates. Finally, we tested the prediction that adult survival, because it more strongly influences fitness in a constant environment, will be less variable than newborn survival, and we found only mixed support for the prediction. Our results suggest that wild primates may be buffered against detrimental fitness effects of environmental stochasticity by their highly developed cognitive abilities, social networks, and broad, flexible diets.
Jundong Wang - One of the best experts on this subject based on the ideXlab platform.
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Serial Correlation in management earnings forecast errors
Journal of Accounting Research, 2011Co-Authors: Guojin Gong, Jundong WangAbstract:We examine whether management earnings forecast errors exhibit Serial Correlation and how analysts understand the Serial Correlation property of management forecast errors (MFEs). MFEs should not exhibit Serial Correlation if managers efficiently process information in prior forecast errors and truthfully convey their earnings expectations through management forecasts. However, for long-horizon management forecasts of annual earnings, we find significantly positive Serial Correlation in MFEs, and sample self-selection does not seem to drive this phenomenon. Further analyses suggest that managers’ unintentional information processing bias contributes to this positive Serial Correlation. Analysts anticipate the intertemporal persistence of MFEs but underestimate the persistence level when reacting to management forecasts. Our findings have implications for market participants who rely on management forecasts to form earnings expectations, and also shed light on the efficiency of managerial decision making.
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Serial Correlation in management earnings forecast errors
Social Science Research Network, 2011Co-Authors: Guojin Gong, Jundong WangAbstract:We examine whether management earnings forecast errors exhibit Serial Correlation and how analysts understand the Serial Correlation property of management forecast errors. Management forecast errors should not exhibit Serial Correlation if managers efficiently process information in prior forecast errors and truthfully convey their earnings expectations through management forecasts. However, for long-horizon management forecasts of annual earnings, we find significantly positive Serial Correlation in management forecast errors, and sample self-selection does not seem to drive this phenomenon. Further analyses suggest that managers’ unintentional information processing bias contributes to this positive Serial Correlation. Analysts anticipate the inter-temporal persistence of management forecast errors but underestimate the persistence level when reacting to management forecasts. Our findings have implications for market participants who rely on management forecasts to form earnings expectations, and also shed light on the efficiency of managerial decision making.
