Comparing China's GDP Statistics with Coincident Indicators

Mehrotra, Aaron; Pääkkönen, Jenni

doi:10.2139/ssrn.1767811

Cited by 11 publications

(12 citation statements)

References 26 publications

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“…Previous studies on GDP and EPC have mainly used statistical data for administrative units [1][2][3][4]. For instance, using GDP statistical data, Mehrotra et al [5] evaluated the dynamics of GDP growth for China against alternative indicators. Michieka et al [6] investigated the relationship among GDP, electricity production and coal consumption in mainland China from 1971 to 2009.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data

et al. 2014

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Abstract:The nighttime light data records artificial light on the Earth's surface and can be used to estimate the spatial distribution of the gross domestic product (GDP) and the electric power consumption (EPC). In early 2013, the first global NPP-VIIRS nighttime light data were released by the Earth Observation Group of National Oceanic and Atmospheric Administration's National Geophysical Data Center (NOAA/NGDC). As new-generation data, NPP-VIIRS data have a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. This study aims to investigate the potential of NPP-VIIRS data in modeling GDP and EPC at multiple scales through a case study of China. A series of preprocessing procedures are proposed to reduce the background noise of original data and to generate corrected NPP-VIIRS nighttime light images. Subsequently, linear regression is used to fit the correlation between the total nighttime light (TNL) (which is extracted from corrected NPP-VIIRS data and DMSP-OLS data) and the OPEN ACCESSRemote Sens. 2014, 6 1706 GDP and EPC (which is from the country's statistical data) at provincial-and prefectural-level divisions of mainland China. The result of the linear regression shows that R 2 values of TNL from NPP-VIIRS with GDP and EPC at multiple scales are all higher than those from DMSP-OLS data. This study reveals that the NPP-VIIRS data can be a powerful tool for modeling socioeconomic indicators; such as GDP and EPC.

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Section: Introductionmentioning

confidence: 99%

Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data

et al. 2014

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“…Our results might be relevant for experts who need to know which new data release is really valuable and which new data release has less valuable predictive content or contains just old information. We complement our indicator-wise analysis by evaluating the joint predictive power of a variety of indicators via factor analysis (see also Curran and Funke, 2006, Mehrotra and Pääkkönen, 2011, Yiu and Chow, 2011and Maier, 2011. Our day-specific pseudo-real time setup allows us to deal with ragged edge issues and to study how forecast errors evolve as ever new data get released over time.…”

Section: Resultsmentioning

confidence: 99%

“…First, there simply do not exist more accurate estimates of Chinese GDP growth than the estimates provided by the NBS. Second, the results of Mehrotra and Pääkkönen (2011) indicate that there is no systematical bias in China's real GDP growth data and that the data are in fact rather reliable. Third, even if there would be systematical or occasional bias in the data, an analysis seems worthwhile as long as the NBS releases provide at least some information about "true GDP".…”

Section: Datamentioning

confidence: 86%

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Forecasting Chinese GDP Growth with Mixed Frequency Data: Which Indicators to Look at?

Mikosch

Zhang²

2014

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract Building on a mixed data sampling (MIDAS) model we evaluate the predictive power of a variety of monthly macroeconomic indicators for forecasting quarterly Chinese GDP growth. We iterate the evaluation over forecast horizons from 370 days to 1 day prior to GDP release and track the release days of the indicators so as to only use information which is actually available at the respective day of forecast. This procedure allows us to detect how useful a specific indicator is at a specific forecast horizon relative to other indicators. Despite being published with an (additional) lag of one month the OECD leading indicator outperforms the leading indicators published by the Conference Board and by Goldman Sachs. Albeit being smaller in terms of market volume, the Shenzhen Composite Stock Exchange Index outperforms the Shanghai Composite Stock Exchange Index and several Hong Kong Stock Exchange indices. Consumer price inflation is especially valuable at forecast horizons of 11 to 7 months. The reserve requirement ratio for small banks proves to be a robust predictor at forecast horizons of 9 to 5 months, whereas the big banks reserve requirement ratio and the prime lending rate have lost their leading properties since 2009. Industrial production can be quite valuable for now-or even forecasting, but only if it is released shortly after the end of a month. Neither monthly retail sales, investment, trade, electricity usage, freight traffic nor the manufacturing purchasing managers' index of the Chinese National Bureau of Statistics help much for now-or forecasting. Our results might be relevant for experts who need to know which indicator releases are really valuable for predicting quarterly Chinese GDP growth, and which indicator releases have less predictive content. Terms of use: Documents inJEL classifications: C53, E27

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“…Some methods only use one or two statistics that are closely linked with data (Huenemann 2001). Other methods construct a multi-indicator system (Klein and Özmucur 2011;Mehrotra and Pääkkönen 2011;Reis et al 2015;Ye 2011) or use the multivariable regression method (Liu and Huang 2009). In addition, variations and trends are common indicators for analyzing the consistency of data and can be assessed using Page 3 of 22 Zhao et al Economic Structures (2017) 6:35 time-series analysis or simple comparisons (Sinton 2001).…”

Section: Researchmentioning

confidence: 99%

How to evaluate the reliability of regional input–output data? A case for China

Zhao

Liu

2017

Economic Structures

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Economic research has been increasingly concerned with structural issues and valueadded in multi-sector international trade. Therefore, input-output (IO) data have become a prevalent data source and been more frequently used in empirical analyses. As a result, the accuracy and reliability of IO data raise serious concerns since the credible data ensure credible empirical results. In fact, there has been ample research dedicated to the quality of government statistics (Zhao et al. 2011). It turns out that no socioeconomic statistical data are completely precise due to statistical regime defects (Xu 1994;Jin and Tao 2010; Holz Holz 2013a, b), investigation and aggregation errors (Park and Wang 2001;Agafiţei et al. 2015) and lack of independence in statistical agencies (Outrata 2015). It is reasonable to presume that IO statistics, as a type of government statistical data, also suffer from some similar quality issues. Thus, the core problem is how to assess the accuracy and quality of current IO data.There has been a prolonged history of using IO data in government statistics. As a part of national accounting, input-output first appeared in A System of National Accounts (SNA1968; United Nations 1968). Then, in System of National Accounts 1993 (SNA1993; Abstract Accurate statistical data are essential to a credible and cogent empirical analysis. However, there currently is no mature and specialized methodology to evaluate the accuracy of input-output (IO) data. This research constructs a comprehensive yet relatively concise framework for evaluating the accuracy of regional IO data by including several indicators that measure all three quadrants. The framework examines regional IO data from the perspectives of time consistency and variation, coefficient correlation and its homogeneity with national-level data. A score indicating the overall accuracy and detailed information that presents concrete shortcomings of regional IO data could be offered after analysis using this framework. As an example, the provincial-level IO data for 30 provinces for 3 years (2002, 2007 and 2012) are analyzed by this framework, and possible explanations of the results are offered. The main contribution and innovation of this research is the construction of an applicable and exhaustive quality evaluation framework for regional IO data. This framework enables researchers to realize flaws in IO data before utilizing them. It also allows government agencies to improve the quality of their data by avoiding issues that emerged in previous data quality evaluations.Keywords: Data quality, Quality evaluation, China, Regional IO data Open Access © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were...

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Comparing China's GDP Statistics with Coincident Indicators

Cited by 11 publications

References 26 publications

Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data

Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data

Forecasting Chinese GDP Growth with Mixed Frequency Data: Which Indicators to Look at?

How to evaluate the reliability of regional input–output data? A case for China

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