Data Revisions Research Articles

Assessment to China's Recent Emission Pattern Shifts

AbstractEnergy and emission data are crucial to climate change research and mitigation efforts. The accuracy of energy statistics is essential for mitigation strategies and evaluating the performance of low carbon energy transition efforts. This study provides the most up‐to‐date emission inventories for China and its provinces for 2018 and 2019. We also update the carbon dioxide (CO2) emission inventories of China and 30 provinces since 2012 based on the newly revised energy statistics. The inventories are compiled in a combined accounting approach of scope 1 (Intergovernmental Panel on Climate Change territorial emissions from 17 types of fossil fuel combustion and cement production by 47 socioeconomic sectors) and scope 2 (emissions from purchased electricity and heat consumption). The most recent energy revision led to an increase in reported national CO2 emissions by an average of 0.3% from 2014 to 2017. The results show that data revisions raised China's carbon intensity mitigation baseline (in 2005) by 5.1%–10.8% and thus made it more challenging to fulfill the mitigation pledges. However, the 2020 carbon intensity mitigation target was achieved ahead of schedule in 2018. A preliminary estimate of China's national emissions for 2020 shows that the COVID‐19 pandemic and lockdown was not able to offset China's annual increase in CO2 emissions. These emissions inventories provide an improved evidence base for China's policies toward net‐zero emissions.

The nowcast revision analysis extended

The nowcast revision analysis is about the effect of new observations, data revisions, and parameter changes on a nowcast of a fixed target variable. Previously, only the new-observations effect can be broken down by variable. We provide a method for doing the same for the data-revisions and parameter-revisions effects as well. The method is applied to the COVID-battered U.S. GDP growth from 2020:Q1 to Q2.

Measurement error in US regional economic data

AbstractEstimates of publicly suppressed and unrevised regional economic data produce error and potentially bias statistical inference. This article estimates measurement error in suppressed cell estimated data sets (SCEDs) relative to unsuppressed federal administrative data. In a cross section, coefficient estimates based on relatively aggregated SCEDs are attenuated by about 31%, increasing to 50% in panel data. Coefficient estimates based on less‐aggregated SCEDs are not generally reliable. A review of the limited options for identification emphasizes both the systemic limitations suppression places on research inference and the value of retrospective revisions in confidential data, highlighting the importance of confidential data access and use.

Survey Expectations and Learning

In this paper, we evaluate a model that describes real-time inflation data together with the inflation expectations measured by the Survey of Professional Forecasters (SPF). We work with a second-order autoregressive model in which the agents learn over time the intercept and persistence coefficients based on real-time data. To model the process of revisions in real time data, we allow for news and noise disturbances. In contrast to the usual time-varying parameter vector autoregression, we use non-linear Kalman filter techniques to estimate the time-varying coefficients of the underlying inflation process. We identify systematic changes in the persistence of the inflation process and in the long-run expected inflation rate that are implied by the model. The inflation forecasts implied by the model are then compared with the SPF forecasts. As we cannot reject the hypothesis that the SPF forecasts are produced based on our model, we re-estimate the model using Survey nowcasts and forecasts as additional observables. This augmented model does not change the nature and magnitude of the time variation in the coefficients of the autoregressive model, but it does help to reduce the uncertainty in the estimates. Overall, the estimated time-variation confirms our results on the perceived inflation process present in estimated DSGE models with learning (Slobodyan and Wouters, 2012a, 2012b).

Predicting benchmarked US state employment data in real time

US payroll employment data come from a survey and are subject to revisions. While revisions are generally small at the national level, they can be large enough at the state level to alter assessments of current economic conditions. Users must therefore exercise caution in interpreting state employment data until they are “benchmarked” against administrative data 5–16 months after the reference period. This article develops a state-space model that predicts benchmarked state employment data in real time. The model has two distinct features: (1) an explicit model of the data revision process and (2) a dynamic factor model that incorporates real-time information from other state-level labor market indicators. We find that the model reduces the average size of benchmark revisions by about 11 percent. When we optimally average the model’s predictions with those of existing models, the model reduces the average size of the revisions by about 14 percent.

Versioning Data Is About More than Revisions: A Conceptual Framework and Proposed Principles

A dataset, small or big, is often changed to correct errors, apply new algorithms, or add new data (e.g., as part of a time series), etc. In addition, datasets might be bundled into collections, distributed in different encodings or mirrored onto different platforms. All these differences between versions of datasets need to be understood by researchers who want to cite the exact version of the dataset that was used to underpin their research. Failing to do so reduces the reproducibility of research results. Ambiguous identification of datasets also impacts researchers and data centres who are unable to gain recognition and credit for their contributions to the collection, creation, curation and publication of individual datasets. Although the means to identify datasets using persistent identifiers have been in place for more than a decade, systematic data versioning practices are currently not available. In this work, we analysed 39 use cases and current practices of data versioning across 33 organisations. We noticed that the term ‘version’ was used in a very general sense, extending beyond the more common understanding of ‘version’ to refer primarily to revisions and replacements. Using concepts developed in software versioning and the Functional Requirements for Bibliographic Records (FRBR) as a conceptual framework, we developed six foundational principles for versioning of datasets: Revision, Release, Granularity, Manifestation, Provenance and Citation. These six principles provide a high-level framework for guiding the consistent practice of data versioning and can also serve as guidance for data centres or data providers when setting up their own data revision and version protocols and procedures.

A new database structure for the IHFC Global Heat Flow Database

Periodic revisions of the Global Heat Flow Database (GHFD) take place under the auspices of the International Heat Flow Commission (IHFC) of the International Association of Seismology and Physics of the Earth's Interior (IASPEI). A growing number of heat-flow values, advances in scientific methods, digitization, and improvements in database technologies all warrant a revision of the structure of the GHFD that was last amended in 1976. We present a new structure for the GHFD, which will provide a basis for a reassessment and revision of the existing global heat-flow data set. The database fields within the new structure are described in detail to ensure a common understanding of the respective database entries. The new structure of the database takes advantage of today's possibilities for data management. It supports FAIR and open data principles, including interoperability with external data services, and links to DOI and IGSN numbers and other data resources (e.g., world geological map, world stratigraphic system, and International Ocean Drilling Program data). Aligned with this publication, a restructured version of the existing database is published, which provides a starting point for the upcoming collaborative process of data screening, quality control and revision. In parallel, the IHFC will work on criteria for a new quality scheme that will allow future users of the database to evaluate the quality of the collated heat-flow data based on specific criteria.

The importance of data revisions for statistical inference

The aim of the study is a quantitative analysis of revisions conducted by means of a new, real-time macroeconomic dataset for Poland, designed on the basis of the Statistical bulletin (Biuletyn statystyczny) published by Statistics Poland, covering the period from as early as 1995 until 2017. Polish data have positively verified a number of hypotheses concerning the impact of data revisions on the modelling process. Procedures assessing the properties of time series can yield widely discrepant results, depending on the extent to which the applied data have been revised. A comparison of the fitted ARIMA models for series of initial and final data demonstrates that the fitted models are similar for the majority of variables. In the cases where the form of the model is identical for both series, the coefficients retain their scale and sign. Most differences between coefficients result from a different structure of the fitted model, which causes differences in the autoregressive structure and can have a considerable impact on the ex ante inference. A prognostic experiment confirmed these observations. For a large number of variables, the total impact of revisions on the forecasting process exceeds 10%. Extreme cases, where the impact goes beyond 100%, or situations where data have a direct impact on the forecast sign, are also relatively frequent. Taking these results into account by forecasters could significantly improve the quality of their predictions. The forecast horizon has a minor impact on these conclusions. The article is a continuation of the author's work from 2017.

Risk Factors for New Onset Diplopia After Graduated Orbital Decompression.

The aim of the study was to identify possible risk factors for new onset diplopia in 20° of primary position (NOD PP) after orbital decompression. A predisposition for NOD has been established for patients with pre-existing diplopia in secondary gaze; therefore, the authors focused on patients without preoperative diplopia. Retrospective chart review of patients who underwent balanced orbital decompression between 2012 and 2019 due to Graves orbitopathy at the authors' institution. Exclusion criteria were incomplete clinical data set, revision surgery, and medial or lateral decompression only. The following clinical parameters were evaluated preoperatively and postoperatively: Hertel exophthalmometry, objective measurement of misalignment using the prism-cover-test, assessment of the field of binocular single vision, and measurement of monocular excursions. In addition, the diameter of the extraocular eye muscles was measured in all preoperative CT scans. We included 327 patients (612 orbits), 126 patients (242 orbits) had no preoperative diplopia. In patients with NOD PP (34%, n = 43/126), enlargement of the medial rectus muscle and restriction of abduction and elevation were significantly more frequent than in patients with no NOD PP. The degree of exophthalmos decrease positively correlated with postoperative squint angle. We were able to identify the diameter of the medial rectus muscle, restriction of abduction, and elevation as well as an extensive reduction of exophthalmos as risk factors for NOD PP in patients with no preoperative diplopia.

Asset pricing with data revisions

We document two important asset pricing implications of the data release process of US consumption growth. First, initial releases are more suitable for asset pricing than final revised releases. This is because most revisions smooth out essential short-term consumption growth fluctuations. Second, first revisions incorporate novel information and their magnitude is strongly linked to consumption growth ambiguity. We formulate a novel consumption-based model, the Revised CCAPM, which incorporates these two effects using vintage data. It explains a striking 75% of the cross-sectional variation in average returns on 25 size-value portfolios. These results support the concept of state-dependent ambiguity attitudes.

Multinuclear Nuclear Magnetic Resonance Spectroscopy Is Used to Determine Rapidly and Accurately the Individual pK a Values of 2-Deoxystreptamine, Neamine, Neomycin, Paromomycin, and Streptomycin.

Unambiguous assignments have been made for each individual pKa value of the amino group and guanidine substituents on 2-deoxystreptamine, neamine, neomycin, paromomycin, and streptomycin by pH-titration evaluation of their 1H, 13C, and 15N (by 1H-15N heteronuclear multiple-bond correlation (HMBC) spectra) NMR chemical shifts (δXs) as the reporter nuclei. These data require minor revisions of the literature data in terms of the assignment order for neomycin and paromomycin. In situ titrations and NMR spectroscopy are shown to be a powerful combination for rapidly (minutes) obtaining each distinct pKa value of the similar amine and guanidine functional groups, which decorate aminoglycoside antibiotics.

Generalized Nowcast Revision Analysis

The nowcast revision analysis is about how a nowcast of the target variable is revised in response to data releases in real time. The content of a data release includes data revisions as well as new observations. The nowcast also changes when the model parameter is revised. The existing revision analysis is capable of breaking down the new-observations effect on the nowcast into contributions from individual indicator variables, but no such breakdown is available for the data-revisions effect and the parameter-revisions effect. This paper shows how the breakdown can be applied to all three effects. For illustration, the new method is applied to nowcasting U.S. GDP growth for 2021:Q2.

Stock Market and Real Economy: Unwritten History Matters!

Economic concepts, statistical methods, and variable classifications used to measure personal consumption expenditures (PCE) are regularly updated when they become obsolete for the evolving US economy. History is gradually rewritten and eventually lost because these structural revisions are applied retrospectively to historical data. We use data revisions to tease out novel empirical support for the conjecture of countercyclical equity risk premia. Latest-vintage PCE, a distorted measure of aggregate consumption risk, is significantly less informative about future stock market returns than real-time-vintage PCE. These results highlight the importance of using real-time data to test economic models.

The Role of Ecb Speeches in Nowcasting German Gdp

The literature shows that the nowcasting models generally use structured data such as real, financial and survey indicators. Recent research has focused on finding the way how to use the unstructured data in the nowcasting models. The search items such as sentiments or emotions were gathered from internet platforms and used as unstructured data. In this study, it is analysed how the ECB presidents' speeches are included in the nowcasting model and to what degree they affect the quarterly gross domestic product (GDP) of Germany. First, ECB presidents' speeches are analysed to obtain the emotion indicators with assistance of the newly harmonised complex dictionary. These emotion indicators are next added to the unbalanced and mixed frequency data and the nowcasting model estimation for GDP is performed with these data using the expectation-maximisation algorithm in the dynamic factor model representation. Moreover, the news analysis is performed to show how the revisions in the real-time data, including emotion indicators, affect the nowcasts for the current and next quarter GDPs. Finally, a forecast scenario is performed to demonstrate the effects of emotion indicators in the nowcasting model of GDP which shows a slowdown for the last two years. In conclusion, it is suggested that ECB presidents' speeches may increase the performance of nowcasting models for the German GDP.

Can GDP Measurement Be Further Improved? Data Revision and Reconciliation

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++, 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.

Data Revisions and the Statistical Relation of Global Mean Sea Level and Surface Temperature

We study the stability of estimated linear statistical relations of global mean temperature and global mean sea level with regard to data revisions. Using four different model specifications proposed in the literature, we compare coefficient estimates and long-term sea level projections using two different vintages of each of the annual time series, covering the periods 1880–2001 and 1880–2013. We find that temperature and sea level updates and revisions have a substantial influence both on the magnitude of the estimated coefficients of influence (differences of up to 50%) and therefore on long-term projections of sea level rise following the RCP4.5 and RCP6 scenarios (differences of up to 40 cm by the year 2100). This shows that in order to replicate earlier results that informed the scientific discussion and motivated policy recommendations, it is crucial to have access to and to work with the data vintages used at the time.

Pengembangan Model Pembelajaran Pendidikan Agama Islam Dengan Pendekatan Simulasi

This study aims to develop a learning model for Islamic religious education with a simulation approach in STKIP Banten. This research method uses research and development through 7 stages, namely research and data collection, needs analysis, development planning, expert evaluation, revision, user evaluation, development and revision. The experts involved in this study were material experts, instructional design experts, instructional media experts, while the test included one-on-one tests, small group tests and field tests. The instruments used to collect data were questionnaires and interviews. Ended with a test, an evaluation of the effectiveness of learning is carried out in the form of multiple choice tests (cognitive areas) and practice (psychomotor fields). The learning evaluation determines the competency standards that students must achieve after studying Islamic Education is 75 (seventy five). The results of this research data analysis can be concluded that the PAI learning model used is effective for students, because it can make it easier for students to understand competencies.

The Research on Integrated Production of Large Scale 3D Products Based on Mass Multi-angular Images

Aim at the problems of high cost and low efficiency of traditional large scale 3D (three-dimensional) products acquisition methods, this paper provides an integrated production of large scale 3D products based on massive multi-angular Images. Firstly, multi-angular images were obtained according to oblique photogrammetric technique. After the operation of aerial triangulation and 3D construction, the real 3D model is achieved. On these base, integrated production of large 3D products were done. Through the statistical analysis of 3D products accuracy, its precision completely meets the requirements of large-scale data specification, which provides a technical guarantee for rapid and efficient data revision of basic geographic information.

Predictive modeling of forest fire using geospatial tools and strategic allocation of resources: eForestFire

Fire is among major threats to the world’s forests, leading to tremendous biodiversity losses. Forest fire in India has greatly increased in the last few decades; the state of Arunachal Pradesh, a recognized Himalayan biodiversity hotspot, is extremely prone to this disaster. The objective of this study was to develop GIS integrated mapping of direct and indirect factors, leading to a predictive model to identify settlements/villages for the strategic allocation of resources towards damage mitigation and control. Initial hotspots were generated by integrating factors of socio-economy, geography and climate, using differential weightage. The intersection of these with fire data, led to identification of final hotspots within the predictive model. The model was improved by linking it to a mobile App and the WebGIS portal. Of the 5258 settlements/villages, a total of 560 were found to be at high fire risk. Percentage correlation increased from 63 to 74, after data revision through the App. A focused intervention on predicted villages was undertaken, resulting in a decrease of 31% of fire incidence in comparison of last five years (2015–20) data. Such advanced information about fire disaster with optimal use of limited resources was greatly helpful, and helped protect the rich Himalayan biodiversity.

Gentrificación e higienización del espacio en Callao, Perú. Barrio de Castilla

El objetivo de este estudio es analizar cómo la gentrificación de un barrio metropolitano empieza a dislocar a sus residentes por población flotante (turistas/consumidores). Para ello, exploramos la percepción de los vecinos del barrio de Castilla en el Centro Histórico del Callao, Perú, con respecto a un proyecto de inversión privada (Monumental Callao) a través de técnicas cualitativas (entrevistas semiestructuradas, observación no participante, revisión de data oficial). Para la lectura de los hallazgos hemos conjugado la semiótica de la espacialidad con el urbanismo para poder dar una lectura que no solo se concentra en la ontología del espacio, sino en sus relaciones sociales y su puesta en discurso con los procesos urbanísticos. Los resultados arrojan que la gentrificación del barrio de Castilla se ha conjugado con un proceso de higienización del espacio, en donde, solo se puede exigir derechos en tanto se vuelve consumidor.