Economic Modelling

ISSN: 0264-9993
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  • Annual data for the period 2003-2013 are obtained from both China’s industrial enterprise database and China’s urban statistical yearbook for 207 cities. No existing yearbook has a prefecture city’s data from different industries, and so our research uses the sum of China’s industrial enterprise database to replace the missing index, and it just lasts until the year 2013. In this talbe, there contain many variables. First column is the code numbers of regions. Second column is the code numbers of industries. third colunm is year. 21th column is GML. 5th column is the EG index. 6th column is FDI. 7th column is FI. 8th column is Edu. 9th column is R&D. 10th column is Road. 11th column is GOV. 12th column is GC. 13th column is RV.
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  • This is the code to replicate the analysis in the paper "Mixed Frequency GVAR analysis of macro-uncertainty and financial stress spillovers in the Eurozone" by Andrea Cipollini and Ieva Mikaliunaite. # CLIFS.txt contains the Country-level index of financial stress from ECB database # GDP_uncertainty.txt contains GDP growth uncertainty index, by Rossi and Sekhposyan (2017) # weights_trade.txt contains the trade weights from BIS. # The file Rstudio_code replicate the results for full sample MF-GVAR model, in Tables 3-6 (Panels A, Full sample, h=4). # Please choose a working directory using setwd("set working directory")
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  • data for the paper: Regulation and Innovation: Examining Outcomes in Chinese Pollution Control Policy Areas this data is run on R
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  • Globalization; Innovation
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  • Data set to replicate figures in the main text, plus the Online Appendix.
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    • Software/Code
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    • Document
  • We use Efficiency to represent GDP divided by energy use, Unilateral to represent sanctions imposed by either the U.S. or the EU only, Plurilateral to represent the sanctions imposed by the U.S. and the EU jointly, Multilateral (or UN) to represent the international sanctions imposed by the United Nations, US, EU, and UN to stand for the sanction senders, Eco to represent that the sanctions affect the economy, Non-eco to represent that the sanctions do not affect the economy, Intensity to represent the formal intensity of sanctions, GDP to stand for per capita GDP of the target state, FDI to represent the net inflows of foreign direct investment, Investment to stand for the gross fixed capital formation as a percent of GDP, Industry to represent percentage of energy industry value added to GDP, Ideology to stand for the ideological orientation of the respective government, Urbanization to represent the proportion of urban population, and Openness to stand for exports plus imports as a percentage of GDP.
    Data Types:
    • Tabular Data
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  • Economic Sanctions and Exchange Rate Volatility
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    • Tabular Data
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  • Data-set for spatial fiscal interactions using SDM. Paper: The Role of Infrastructure Investment and Factor Productivity in International Tax Competition.
    Data Types:
    • Tabular Data
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