cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13177264
title: Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles

description: This study describes a significant upgrade to the particulate matter (PM) size disaggregation profile library used for preparing emissions files for the GEM-MACH (Global Environmental Multiscale-Modelling Air-quality and CHemistry) chemical transport model (CTM). This model uses a sectional (bin) approach to represent the PM size distribution, where one configuration employs 12 size bins to disaggregate PM<sub>2.5</sub> and PM<sub>10</sub> inventory emissions into the first 10 bins ranging from 0.01 to 10.24 μm in diameter. For the size disaggregation step, a small library of three generic PM size disaggregation profiles is currently applied for three broad source categories (area, mobile, and point). However, as might be expected, these generic profiles are not always representative: for example, emissions from two very different area sources ‒ paved road dust and residential wood combustion ‒ are disaggregated using the same generic size distribution profile. In order to improve the current small PM size disaggregation profile library, a comprehensive literature review was conducted: over 100 relevant publications were identified and PM size distribution profiles for 36 different emission source types were selected and compiled. These 36 source-type-specific PM size distribution profiles were then combined based on process type with corresponding PM speciation profiles to create a library of chemically speciated and size-resolved PM disaggregation profiles. This library can now be used by the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system for the 12-bin version of GEM-MACH to perform PM chemical speciation and size allocation in one step. The size-profile data collected and compiled in this study may also be used for emissions processing for other CTMs with a size-resolved representation of PM. Details of the compilation of the 36 PM size disaggregation profiles are discussed, and the differences in processed PM emissions based on the current and updated PM size disaggregation profile libraries are shown. <i>Implications</i>: A new and expanded particulate matter (PM) size disaggregation profile library covering 36 emission source types has been developed based on an extensive literature review. Its use can produce significant changes in the size allocation of bulk PM inventory emissions processed for input to size-resolved PM chemical transport models. Such models are used to predict atmospheric visibility and to simulate the interactions of aerosol particles with atmospheric radiation and with clouds. The use of more accurate, size-resolved primary PM emissions by these models should improve their predictive skill for atmospheric PM processes affecting air quality, meteorology, and climate.
DOI: 10.6084/m9.figshare.13067725
title: A Simple Asymptotically <i>F</i>-Distributed Portmanteau Test for Diagnostic Checking of Time Series Models With Uncorrelated Innovations

description: We propose a simple asymptotically F-distributed portmanteau test for diagnostically checking whether the innovations in a parametric time series model are uncorrelated while allowing them to exhibit higher-order dependence of unknown forms. A transform of sample residual autocovariances removing the influence of parameter estimation uncertainty makes the test simple. Further, by employing the orthonormal series variance estimator, a special sample autocovariances estimator that is asymptotically invariant to parameter estimation uncertainty, we show that the proposed test statistic is asymptotically F-distributed under fixed-smoothing asymptotics. The asymptotic F-theory accounts for the estimation error of the variance estimator that the asymptotic chi-squared theory ignores. Moreover, an extensive Monte Carlo study demonstrates that the F-test has more accurate finite sample size than existing tests with virtually no power loss. An application to S&P 500 returns illustrates the merits of the proposed methodology.
DOI: 10.6084/m9.figshare.13067725.v2
title: A Simple Asymptotically <i>F</i>-Distributed Portmanteau Test for Diagnostic Checking of Time Series Models With Uncorrelated Innovations

description: We propose a simple asymptotically F-distributed portmanteau test for diagnostically checking whether the innovations in a parametric time series model are uncorrelated while allowing them to exhibit higher-order dependence of unknown forms. A transform of sample residual autocovariances removing the influence of parameter estimation uncertainty makes the test simple. Further, by employing the orthonormal series variance estimator, a special sample autocovariances estimator that is asymptotically invariant to parameter estimation uncertainty, we show that the proposed test statistic is asymptotically F-distributed under fixed-smoothing asymptotics. The asymptotic F-theory accounts for the estimation error of the variance estimator that the asymptotic chi-squared theory ignores. Moreover, an extensive Monte Carlo study demonstrates that the F-test has more accurate finite sample size than existing tests with virtually no power loss. An application to S&P 500 returns illustrates the merits of the proposed methodology.
DOI: 10.6084/m9.figshare.13067725.v1
title: A Simple Asymptotically F-Distributed Portmanteau Test for Diagnostic Checking of Time Series Models with Uncorrelated Innovations

description: We propose a simple asymptotically F-distributed portmanteau test for diagnostically checking whether the innovations in a parametric time series model are uncorrelated while allowing them to exhibit higher-order dependence of unknown forms. A transform of sample residual autocovariances removing the influence of parameter estimation uncertainty makes the test simple. Further, by employing the orthonormal series variance estimator, a special sample autocovariances estimator that is asymptotically invariant to parameter estimation uncertainty, we show that the proposed test statistic is asymptotically F-distributed under fixed-smoothing asymptotics. The asymptotic F theory accounts for the estimation error of the variance estimator that the asymptotic chi-squared theory ignores. Moreover, an extensive Monte Carlo study demonstrates that the F test has more accurate finite sample size than existing tests with virtually no power loss. An application to S&P 500 returns illustrates the merits of the proposed methodology.
DOI: 10.6084/m9.figshare.13615906.v2
title: Semiparametric Tests for the Order of Integration in the Possible Presence of Level Breaks

description: Lobato and Robinson developed semiparametric tests for the null hypothesis that a series is weakly autocorrelated, or <i>I</i>(0), about a constant level, against fractionally integrated alternatives. These tests have the advantage that the user is not required to specify a parametric model for any weak autocorrelation present in the series. We extend this approach in two distinct ways. First, we show that it can be generalized to allow for testing of the null hypothesis that a series is I(δ) for any <i>δ</i> lying in the usual stationary and invertible region of the parameter space. The second extension is the more substantive and addresses the well-known issue in the literature that long memory and level breaks can be mistaken for one another, with unmodeled level breaks rendering fractional integration tests highly unreliable. To deal with this inference problem, we extend the Lobato and Robinson approach to allow for the possibility of changes in level at unknown points in the series. We show that the resulting statistics have standard limiting null distributions, and that the tests based on these statistics attain the same asymptotic local power functions as infeasible tests based on the unobserved errors, and hence there is no loss in asymptotic local power from allowing for level breaks, even where none is present. We report results from a Monte Carlo study into the finite-sample behavior of our proposed tests, as well as several empirical examples.
DOI: 10.6084/m9.figshare.13615906
title: Semiparametric Tests for the Order of Integration in the Possible Presence of Level Breaks

description: Lobato and Robinson developed semiparametric tests for the null hypothesis that a series is weakly autocorrelated, or <i>I</i>(0), about a constant level, against fractionally integrated alternatives. These tests have the advantage that the user is not required to specify a parametric model for any weak autocorrelation present in the series. We extend this approach in two distinct ways. First, we show that it can be generalized to allow for testing of the null hypothesis that a series is I(δ) for any <i>δ</i> lying in the usual stationary and invertible region of the parameter space. The second extension is the more substantive and addresses the well-known issue in the literature that long memory and level breaks can be mistaken for one another, with unmodeled level breaks rendering fractional integration tests highly unreliable. To deal with this inference problem, we extend the Lobato and Robinson approach to allow for the possibility of changes in level at unknown points in the series. We show that the resulting statistics have standard limiting null distributions, and that the tests based on these statistics attain the same asymptotic local power functions as infeasible tests based on the unobserved errors, and hence there is no loss in asymptotic local power from allowing for level breaks, even where none is present. We report results from a Monte Carlo study into the finite-sample behavior of our proposed tests, as well as several empirical examples.
DOI: 10.6084/m9.figshare.13615906.v1
title: Semiparametric Tests for the Order of Integration in the Possible Presence of Level Breaks

description: Lobato and Robinson (1998) develop semiparametric tests for the null hypothesis that a series is weakly autocorrelated, or <i>I</i>(0), about a constant level, against fractionally integrated alternatives. These tests have the advantage that the user is not required to specify a parametric model for any weak autocorrelation present in the series. We extend this approach in two distinct ways. First we show that it can be generalised to allow for testing of the null hypothesis that a series is I(δ) for any <i>δ</i> lying in the usual stationary and invertible region of the parameter space. The second extension is the more substantive and addresses the well known issue in the literature that long memory and level breaks can be mistaken for one another, with unmodelled level breaks rendering fractional integration tests highly unreliable. To deal with this inference problem we extend the Lobato and Robinson (1998) approach to allow for the possibility of changes in level at unknown points in the series. We show that the resulting statistics have standard limiting null distributions, and that the tests based on these statistics attain the same asymptotic local power functions as infeasible tests based on the unobserved errors, and hence there is no loss in asymptotic local power from allowing for level breaks, even where none is present. We report results from a Monte Carlo study into the finite-sample behaviour of our proposed tests, as well as several empirical examples.
DOI: 10.6084/m9.figshare.13119974
title: Can GDP Measurement Be Further Improved? Data Revision and Reconciliation

description: Recent years have seen many attempts to combine expenditure-side estimates of U.S. real output (GDE) growth with income-side estimates (GDI) to improve estimates of real GDP growth. We show how to incorporate information from multiple releases of noisy data to provide more precise estimates while avoiding some of the identifying assumptions required in earlier work. This relies on a new insight: using multiple data releases allows us to distinguish news and noise measurement errors in situations where a single vintage does not. We find that (a) the data prefer averaging across multiple releases instead of discarding early releases in favor of later ones, and (b) that initial estimates of GDI are quite informative. Our new measure, GDP<sup>++</sup>, undergoes smaller revisions and tracks expenditure measures of GDP growth more closely than either the simple average of the expenditure and income measures published by the BEA or the GDP growth measure of Aruoba et al. published by the Federal Reserve Bank of Philadelphia.
DOI: 10.6084/m9.figshare.13119974.v3
title: Can GDP Measurement Be Further Improved? Data Revision and Reconciliation

description: Recent years have seen many attempts to combine expenditure-side estimates of U.S. real output (GDE) growth with income-side estimates (GDI) to improve estimates of real GDP growth. We show how to incorporate information from multiple releases of noisy data to provide more precise estimates while avoiding some of the identifying assumptions required in earlier work. This relies on a new insight: using multiple data releases allows us to distinguish news and noise measurement errors in situations where a single vintage does not. We find that (a) the data prefer averaging across multiple releases instead of discarding early releases in favor of later ones, and (b) that initial estimates of GDI are quite informative. Our new measure, GDP<sup>++</sup>, undergoes smaller revisions and tracks expenditure measures of GDP growth more closely than either the simple average of the expenditure and income measures published by the BEA or the GDP growth measure of Aruoba et al. published by the Federal Reserve Bank of Philadelphia.
DOI: 10.6084/m9.figshare.13119974.v2
title: Can GDP Measurement Be Further Improved? Data Revision and Reconciliation

description: Recent years have seen many attempts to combine expenditure-side estimates of U.S. real output (GDE) growth with income-side estimates (GDI) to improve estimates of real GDP growth. We show how to incorporate information from multiple releases of noisy data to provide more precise estimates while avoiding some of the identifying assumptions required in earlier work. This relies on a new insight: using multiple data releases allows us to distinguish news and noise measurement errors in situations where a single vintage does not. We find that (a) the data prefer averaging across multiple releases instead of discarding early releases in favor of later ones, and (b) that initial estimates of GDI are quite informative. Our new measure, GDP<sup>++</sup>, undergoes smaller revisions and tracks expenditure measures of GDP growth more closely than either the simple average of the expenditure and income measures published by the BEA or the GDP growth measure of Aruoba et al. published by the Federal Reserve Bank of Philadelphia.
DOI: 10.6084/m9.figshare.13341796
title: Development of a magnetically separable co-immobilized laccase and versatile peroxidase system for the conversion of lignocellulosic biomass to vanillin

description: Lignin obtained from renewable biomass is a potential feedstock for the synthesis of various value-added chemicals through efficient biocatalytic routes. The ligninolytic enzymes-assisted depolymerization of lignin to vanillin constitutes the most commercially attractive and promising approach in green chemistry as vanillin constitutes the second most prevalent flavoring agent. Thus, in the present work, immobilized laccase and versatile peroxidase, and further, a co-immobilized laccase and versatile peroxidase system on magnetic silica microspheres (MSMS) were developed to generate a robust biocatalytic system that mediates the depolymerization of lignin obtained from <i>Casuarina equisetifolia</i> biomass. The depolymerization of lignin by free and immobilized laccase showed a vanillin yield of 24.8% and 23%, respectively, at pH 4.0 in 6 h at 30°C against a vanillin yield of 20% and 21.7% with the free and immobilized versatile peroxidase, respectively, at pH 5.0°C and 50°C. Comparatively, the system with the co-immobilized laccase and versatile peroxidase exhibited a 1-fold and 1.2-fold higher vanillin yield than the free and immobilized laccase system, respectively. On comparing with the versatile peroxidase system, the co-immobilized biocatalytic system displayed 1.3-fold and 1.2-fold increased vanillin yield than the free and immobilized versatile peroxidase system, respectively, at a pH of 6.0 in 6 h at 30°C with an enzyme concentration of 1 U/ml. The reusability studies of the co-immobilized biocatalytic system exhibited that both the enzymes retained up to 40% of its activity till sixth cycle. <i>Implications</i>: The waste biomass of <i>Casuarina equisetifolia</i> is widely available around the coastal regions of India which does not have any agricultural or industrial applications. The present work exploits the lignocellulosic content of the <i>Casuarina</i> biomass to extract the lignin, which provides a renewable alternative for the production of the commercially high-valued compound, vanillin. This work also integrates a co-immobilized biocatalytic process comprising of laccase and versatile peroxidase leading to an environmentally benign enzymatic process for the depolymerization of lignin to vanillin.
DOI: 10.6084/m9.figshare.13341796.v1
title: Development of a magnetically separable co-immobilized laccase and versatile peroxidase system for the conversion of lignocellulosic biomass to vanillin

description: Lignin obtained from renewable biomass is a potential feedstock for the synthesis of various value-added chemicals through efficient biocatalytic routes. The ligninolytic enzymes-assisted depolymerization of lignin to vanillin constitutes the most commercially attractive and promising approach in green chemistry as vanillin constitutes the second most prevalent flavoring agent. Thus, in the present work, immobilized laccase and versatile peroxidase, and further, a co-immobilized laccase and versatile peroxidase system on magnetic silica microspheres (MSMS) were developed to generate a robust biocatalytic system that mediates the depolymerization of lignin obtained from <i>Casuarina equisetifolia</i> biomass. The depolymerization of lignin by free and immobilized laccase showed a vanillin yield of 24.8% and 23%, respectively, at pH 4.0 in 6 h at 30°C against a vanillin yield of 20% and 21.7% with the free and immobilized versatile peroxidase, respectively, at pH 5.0°C and 50°C. Comparatively, the system with the co-immobilized laccase and versatile peroxidase exhibited a 1-fold and 1.2-fold higher vanillin yield than the free and immobilized laccase system, respectively. On comparing with the versatile peroxidase system, the co-immobilized biocatalytic system displayed 1.3-fold and 1.2-fold increased vanillin yield than the free and immobilized versatile peroxidase system, respectively, at a pH of 6.0 in 6 h at 30°C with an enzyme concentration of 1 U/ml. The reusability studies of the co-immobilized biocatalytic system exhibited that both the enzymes retained up to 40% of its activity till sixth cycle. <i>Implications</i>: The waste biomass of <i>Casuarina equisetifolia</i> is widely available around the coastal regions of India which does not have any agricultural or industrial applications. The present work exploits the lignocellulosic content of the <i>Casuarina</i> biomass to extract the lignin, which provides a renewable alternative for the production of the commercially high-valued compound, vanillin. This work also integrates a co-immobilized biocatalytic process comprising of laccase and versatile peroxidase leading to an environmentally benign enzymatic process for the depolymerization of lignin to vanillin.
DOI: 10.6084/m9.figshare.13302558.v2
title: LM Tests for Joint Breaks in the Dynamics and Level of a Long-Memory Time Series

description: We consider a single-step Lagrange multiplier (LM) test for joint breaks (at known or unknown dates) in the long memory parameter, the short-run dynamics, and the level of a fractionally integrated time-series process. The regression version of this test is easily implementable and allows to identify the specific sources of the break when the null hypothesis of parameter stability is rejected. However, its size and power properties are sensitive to the correct specification of short-run dynamics under the null. To address this problem, we propose a slight modification of the LM test (labeled LMW-type test) which also makes use of some information under the alternative (in the spirit of a Wald test). This test shares the same limiting distribution as the LM test under the null and local alternatives but achieves higher power by facilitating the correct specification of the short-run dynamics under the null and any alternative (either local or fixed). Monte Carlo simulations provide support for these theoretical results. An empirical application, concerning the origin of shifts in the long-memory properties of forward discount rates in five G7 countries, illustrates the usefulness of the proposed LMW-type test.
DOI: 10.6084/m9.figshare.13302558
title: LM Tests for Joint Breaks in the Dynamics and Level of a Long-Memory Time Series

description: We consider a single-step Lagrange multiplier (LM) test for joint breaks (at known or unknown dates) in the long memory parameter, the short-run dynamics, and the level of a fractionally integrated time-series process. The regression version of this test is easily implementable and allows to identify the specific sources of the break when the null hypothesis of parameter stability is rejected. However, its size and power properties are sensitive to the correct specification of short-run dynamics under the null. To address this problem, we propose a slight modification of the LM test (labeled LMW-type test) which also makes use of some information under the alternative (in the spirit of a Wald test). This test shares the same limiting distribution as the LM test under the null and local alternatives but achieves higher power by facilitating the correct specification of the short-run dynamics under the null and any alternative (either local or fixed). Monte Carlo simulations provide support for these theoretical results. An empirical application, concerning the origin of shifts in the long-memory properties of forward discount rates in five G7 countries, illustrates the usefulness of the proposed LMW-type test.
DOI: 10.6084/m9.figshare.13302558.v1
title: LM tests for joint breaks in the dynamics and level of a long-memory time series*

description: We consider a single-step Lagrange Multiplier (LM) test for joint breaks (at known or unknown dates) in the long memory parameter, the short-run dynamics and the level of a fractionally integrated time-series process. The regression version of this test is easily implementable and allows to identify the specific sources of the break when the null hypothesis of parameter stability is rejected. However, its size and power properties are sensitive to the correct specification of short-run dynamics under the null. To address this problem, we propose a slight modification of the LM test (labeled LMW-type test) which also makes use of some information under the alternative (in the spirit of a Wald test). This test shares the same limiting distribution as the LM test under the null and local alternatives but achieves higher power by facilitating the correct specification of the short-run dynamics under the null and any alternative (either local or fixed). Monte Carlo simulations provide support for these theoretical results. An empirical application, concerning the origin of shifts in the long-memory properties of forward discount rates in five G7 countries, illustrates the usefulness of the proposed LMW-type test.