Global Economic Policy Uncertainty on Manufacturing: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Rita Xu and Version 1 by Yuhang Bai.

Events such as COVID-19 and the Russia–Ukraine conflict have significantly increased the uncertainty and volatility of global economic policies. In the context of economic globalization, the key question wresearchers investigate is whether global economic policy uncertainty will have different impacts on the manufacturing of the three major economies in China, the United States, and Europe Union.

  • global economic policy uncertainty
  • manufacturing
  • PMI

1. Introduction

The manufacturing industry is an important component of the national economy [1,2][1][2]. As one of the important engines of economic growth [3[3][4][5],4,5], the manufacturing sector is indispensable in increasing employment, advancing technology, and boosting exports [1,6][1][6]. Researchers typically use the manufacturing purchasing manager’s index (PMI) to measure and forecast the trend of manufacturing [7,8,9,10][7][8][9][10]. The manufacturing PMI originated in the United States. This index, which is one of the most widely used in the world to track macroeconomic trends, is compiled from the results of a monthly survey of corporate purchasing managers; it is calculated using data from the manufacturing sector’s new orders, inventory, production, supplier delivery, and employment domains; it offers effective warning and prediction capabilities [11], and it is also considered the “leading indicator of overall economic activity in the US ” (Purchasing Managers’ Index (PMI) Defined and How It Works (investopedia.com)). The manufacturing boom line, which indicates the strength of the manufacturing industry, is often regarded by researchers as the manufacturing PMI of 50: when the PMI is higher than 50, the manufacturing sector of the economy is experiencing prosperity; when the PMI is lower than 50, the manufacturing sector is experiencing a recession. As a result, market economy players can use the manufacturing PMI as a useful decision-making tool [12,13][12][13].
The outlook toward the world economy is gloomy (World Economic Outlook Update, July 2022: Gloomy and More Uncertain (imf.org)); global economic policy uncertainty is rising while the manufacturing purchasing manager’s index is declining. Global economic policy uncertainty (GEPU) evolves from economic policy uncertainty (EPU), and economic policy uncertainty (EPU) refers to the fact that in the process of economic policy adjustment, the primary body involved in economic production cannot predict changes in economic policy, thereby leading to a series of unpredictable economic risks [14]. The formulation of economic policy uncertainty (EPU) is based on the coverage frequency method of mainstream newspapers, which calculates the number of news-related to keywords such as economy, policy, and uncertainty in mainstream newspapers with high circulation. It is composed of three weighted indices: news index, tax law invalidation index, and economic forecast difference index [14,15][14][15]. The modification of economic policies, such as monetary, fiscal, and national security policies, has a significant impact on the development of EPU [14]. Following Baker [14], Davis [16] weighted the national EPU indices of 21 major industrial countries according to the proportion of each country’s gross domestic product (GDP) and then developed the global economic policy uncertainty (GEPU) method. According to Davis [16], the specific calculation steps of GEPU are as follows: first, renormalize each national EPU index to a mean of 100 from 1997 (or first year) to 2015; second, impute missing values for certain countries using a regression-based method; this step yields a balanced panel of monthly EPU index values for 21 countries from January 1997 onwards; third, compute the GEPU Index value for each month as the GDP-weighted average of the 21 national EPU index values, using GDP data from the IMF’s World Economic Outlook Database. Global economic recovery has been hampered by the US financial crisis, the European debt crisis, the China–US trade war, COVID-19, the Russia–Ukraine conflict, and numerous other uncertainties (Multiple crises unleash one of the lowest global economic outputs in recent decades, says UN report|UNCTAD) [17,18,19][17][18][19]. Unexpected events led to a rapid rise in global economic policy uncertainty, while the manufacturing purchasing manager’s index has dropped sharply. For instance, from March 2018 to June 2019, the China–US trade war led to an increase in GEPU from 162.22 to 335.35, an increase of 2.07 times; meanwhile, China’s manufacturing PMI dropped from 51.5 to 49.4, a drop of 4.82%; while the US manufacturing PMI dropped from 59.3 to 51.7, a drop of 12.82%; and the EU manufacturing PMI dropped from 56.6 to 47.6, a drop of 15.9%. Based on this, it is worth studying the impact of GEPU on the manufacturing sectors of major economies.
China, the US, and the EU are the drivers of world manufacturing. According to the data from the World Bank (WB) Manufacturing, value added (current US$)—China, United States, European Union|Data (worldbank.org) (accessed on 18 July 2023 (worldbank.org)), in 2021, the GDP in China, the US, and the EU was 17.73 trillion dollars, 23.32 trillion dollars, and 17.09 trillion dollars, respectively, and the world GDP was 96.53 trillion dollars. The three economies account for 60.23% of world GDP; the manufacturing value added in China, the US, and the EU was 4.87 trillion dollars, 2.50 trillion dollars, and 2.53 trillion dollars, respectively, and the world manufacturing value added was 16.05 trillion dollars. The three economies account for 61.68% of the value added of world manufacturing. The ratio of manufacturing value added to GDP can be used to assess the contribution of manufacturing in the national economy [6,20][6][20]. The proportion of manufacturing value added to GDP in these three countries is 27.47%, 10.72%, and 14.80%, respectively, and as it is a single economy with the relatively high proportion of manufacturing value added in GDP, manufacturing’s vigorous development depends on a stable political and economic environment. China is transitioning from low-end to mid-to-high-end manufacturing [21[21][22],22], especially in the green energy such as photovoltaics, lithium batteries, wind energy, and electric vehicle manufacturing sectors [23,24,25][23][24][25]. The US and the EU have a comparative advantage in the field of high value-added manufacturing such as those concerning automobiles, electrical and optical equipment, semiconductors, and materials [26,27,28][26][27][28]. Although China, the US, and the EU are the world’s three main pillars, its manufacturing industry still struggles with a number of issues brought on by GEPU in the context of the decoupling of China and the US, the COVID-19 pandemic, and Russia–Ukraine conflict. Since 2018, manufacturing in China has suffered from the outflow of low-end manufacturing and slowing economic growth rate (Challenges and countermeasures facing the high-quality development of China’s manufacturing-NDRC); manufacturing in the US is subject to labor shortages and high inflation (Tackling the manufacturing hiring crisis|The Daily Reporter—WI Construction News & Bids); and manufacturing in the EU has endured a doubling of energy prices (Energy Crisis Poses Existential Threat to Europe’s Industry|OilPrice.com). Furthermore, the increase in GEPU led to severe fluctuations in the manufacturing PMI of major global economies, casting a shadow over the global economic recovery (World Economic Outlook Update, July 2022: Gloomy and More Uncertain (imf.org)).

2. Global Economic Policy Uncertainty on Manufacturing

The existing research on economic policy uncertainty (EPU) is primarily concentrated on the fields of financial market volatility and economic growth [31,32,33,34,35,36,37,38,39][29][30][31][32][33][34][35][36][37]. It should be clarified that EPU exhibits countercyclicality, and its impact effect increases with the expansion of economic output scale; the degree of positive and negative impact on actual economic output varies by country [31][29]. Concerning the relationship between economic policy uncertainty and US stock returns, Arouri [32][30] claimed that it is nonlinear, with a decrease in stock returns leading to an increase in EPU. EPU not only lowers stock returns but also exacerbates volatility in exchange rates. For instance, Nilavongse [33][31] utilized a structural vector autoregressive model to claim that fluctuations in the pound exchange rate are mostly caused by changes in UK economic policies, but uncertainty in US economic policies causes a drop in industrial production capacity in the UK, and the UK’s Brexit vote caused the pound to decline significantly. Chen [34][32] employed quantile regression to discover that the influence of China’s EPU on its exchange rate fluctuations is asymmetric and heterogeneous, and that the impact of the US EPU on China’s exchange rate fluctuations rises with the quantile, while Hong Kong EPU has no effect on China’s exchange rate fluctuations, Japan and the EU EPU have an inverted U-shaped relationship with China’s exchange rate changes. Bank lending gives the economy a boost, while high EPU discourages investors from making credit investments. Nguyen [35][33], who used the generalized least squares method to evaluate the data, claims that there is a negative correlation between bank lending and EPU, with the latter having a larger negative influence on industrialized nations than emerging ones. Phan [36][34], who studied EPU and financial stability data from 23 countries worldwide from 1996 to 2016, does not appear to share Nguyen’s [35][33] point of view; Phan [36][34] discovered that EPU has a greater detrimental effect on financial stability for nations with smaller financial systems, lax regulation, and high levels of competition. It is clear from the foregoing that significant changes in EPU have a negative impact on economic growth, investment, and consumption; high uncertainty causes investment to decline more than output or consumption does [37][35]. However, tools like Bitcoin can be used to hedge EPU risks. Demir [38][36] discovered that EPU can forecast Bitcoin returns, even though Bitcoin returns and EPU have a negative correlation; quantile regression research results reveal that the return rates of Bitcoin and EPU are significant at lower and lower quantiles, and investors can use Bitcoin to hedge EPU risks. According to Fang [39][37], EPU has a negative impact when compared to Bitcoin bonds, but a positive impact when compared to Bitcoin-related commodities and equities. As a result, bitcoin can be used in hedging operations to lower the risk of EPU. Research on how EPU affects carbon emissions has increased along with the growth of the low-carbon economy. Pirgaip [40][38] found that there is a one-way causal relationship between Japan’s EPU on energy consumption, the US and Germany’s EPU on carbon emissions, and Canada’s EPU on energy consumption and carbon emissions. In other words, an increase in carbon emissions will result from increased energy use, EPU, and economic expansion. Moreover, he urged the G7 nations to consider the detrimental effects of EPU on energy conservation and emission reduction, as well as to reduce carbon dioxide emissions and consumption. Huang [41][39] demonstrated that outward direct investment, per capita GDP, and EPU all contribute to rising greenhouse gas emissions. Adams [42][40] examined how countries with high geopolitical risk emitted carbon; the study’s findings demonstrate not only that rising energy consumption raises carbon emissions but also that there is a strong negative correlation between rising EPU and carbon emissions, suggesting that rising EPU will make it more difficult for nations with high geopolitical risks to maintain environmental sustainability. Additionally, some academics have examined how EPU affects energy consumption by starting with renewable energy. EPU was shown to have an impact on the utilization of renewable energy in Nakhli’s [43][41] study, and there is only a one-way causative relationship between US electricity consumption and EPU, as opposed to a two-way causal relationship between carbon emissions and EPU; thus, when creating environmental policies, EPU should be taken into account. As research continues to advance, more academics are concentrating on the connection between EPU and carbon emissions at the micro level. Yu [44][42] asserts that China’s provincial EPU have a significant positive impact on the intensity of carbon emissions and that industrial firms are more willing to employ cheap fossil fuels to satisfy the EPUs’ continually increasing standards. Luo [45][43] discovered a strong inverse relationship between the unpredictability of economic policy and green innovation in businesses, while this relationship can be weakened by increasing the transparency of carbon information. Khan [46][44] found that carbon emissions in China, South Korea, Singapore, and Japan are positively correlated with trade, GDP, and EPU, and the environmental quality of East Asian economies is enhanced by FDI and the use of renewable energy sources. The research focus of global economic policy uncertainty (GEPU) is comparable to that of EPU, and it examines how GEPU has affected global financial and commodity future markets. Yu [47][45] contended that as China’s integration into the global economy progresses, the unpredictability of global economic policy will become one of the main causes of, and a substantial contributor to, the volatility of the Chinese stock market. Miao [48][46] was of the opinion that the addition of a regime switching model could increase the precision with which GEPU forecasts change in the US stock market. Qin [49][47] thought that gold could be utilized as a hedge against GEPU risks during economic downturns; gold prices fluctuate as a result of GEPU, but GEPU is also impacted by gold prices both favorably and unfavorably. Shao [50][48] claims that there is an imbalanced relationship between GEPU and the transmission of global grain prices, with the effects of GEPU being more beneficial for soybean prices than maize and wheat prices. While GEPU research methods are relatively rich and unique, the present research methods on PMI are pretty straightforward. Yu [51][49] used the DCC-MVGARCH method to study the dynamic correlation between the PMI and GDP growth rate of manufacturing in the US. Yanik [52][50] examined the causal relationship between manufacturing PMI and Türkiye’s stock market through the Granger causality test. Zhang [8] used the state space equation and KALMAN filter algorithm to test the fluctuation of PMI potential signals of manufacturing industries in the US, the EU, Japan and China. The nested regression and VAR models were used to study the relation between economic activity and uncertainty by Shaikh [53][51]. In the latest research method on GEPU, Zhang [54][52] used the TVP-VAR model to study the time-varying relationship between EPU, geopolitical risk (GPR), and inbound tourism in China. Gu [55][53] used the TVP-VAR model to compare the dynamic impact of GPR and EPU indices on the oil market. Although the above research clearly proves that EPU and GEPU affect the stability of stock returns [32][30], exacerbate exchange rate fluctuations [32,33,34][30][31][32], inhibit investment and output [35][33], and affect carbon emission intensity [41,42,43[39][40][41][42],44], the above-mentioned research methods on EPU and GEPU are mostly based on linear models such as the VAR model and its evolutions [31,32,33,40[29][30][31][38][39][40][41],41,42,43], quantile regression [34][32], the least square method [35][33], etc. These methods are relatively simple. Moreover, it is impossible to capture the dynamic time-varying characteristics of EPU or GEPU shocks, and it is impossible to further analyze the time-point shocks caused by crucial events in EPU and GEPU. Among the above research methods on PMI, Granger causality can only test the overall causality among variables and cannot reflect its dynamic correlation [52][50]; the state space equation and KALMAN filter algorithm are limited to the correlation study of two variables; the DDC MVGRACH model has relatively low accuracy in the long-term prediction of variables [51][49]; the VAR model can better predict multiple variables, but the conclusions drawn lack completeness and accuracy due to its failure to consider the changes in model coefficients with different periods. Although the above methods can, to some extent, reflect the dynamic interaction between variables, they cannot reflect the dynamic correlation between variables in different periods and the marginal impact effects at different time points. With the deepening of the research, the dynamic and time-varying impact of emergencies in EPU and GEPU on the economy has gradually attracted the attention of scholars. Zhang [54][52] used the TVP-VAR model to empirically analyze the impact of GPR emergencies such as the 911 incident and the US financial crisis on tourism but did not involve the impact of COVID-19 or the Russia–Ukraine conflict on tourism. Gu [55][53] also uses the TVP-VAR model, but the selected research scope was up to September 2020; thus, it cannot deeply analyze the impact of COVID-19 on international oil prices, and it does not involve the Russia–Ukraine conflict which has had an important impact on the global economy. Therefore, this restudyearch will draw on the gaps and deficiencies of the above research methods and for the first time, try to put the impact of global economic policy uncertainty on manufacturing PMI within a unified analysis framework, pay more attention to the dynamic time-varying characteristics of GEPU, and use the TVP-VAR model to analyze the time-varying impact of GEPU on manufacturing PMI in China, the US, and the EU. In this paper, weResearchers perform a comparative analysis of the impact of the US financial crisis, the European debt crisis, and especially the China–US trade war, COVID-19, the Russia–Ukraine conflict and other major global events on manufacturing. TIt also helps to compare and analyze the risk resilience of manufacturing in these three economic systems, providing relevant empirical references for industrial investors and policymakers.

References

  1. Marconi, N.; de Borja Reis, C.F.; de Araújo, E.C. Manufacturing and Economic Development: The Actuality of Kaldor’s First and Second Laws. Struct. Chang. Econ. Dyn. 2016, 37, 75–89.
  2. Liu, F.; Ding, Y.; Gao, J.; Gong, P. Effects of Cost Factors on National Manufacturing Based on Global Perspectives. Economies 2017, 5, 45.
  3. McCausland, W.D.; Theodossiou, I. Is Manufacturing Still the Engine of Growth? J. Post Keynes. Econ. 2012, 35, 79–92.
  4. Su, D.; Yao, Y. Manufacturing as the Key Engine of Economic Growth for Middle-Income Economies. J. Asia Pac. Econ. 2017, 22, 47–70.
  5. Cantore, N.; Clara, M.; Lavopa, A.; Soare, C. Manufacturing as an Engine of Growth: Which Is the Best Fuel? Struct. Chang. Econ. Dyn. 2017, 42, 56–66.
  6. Haraguchi, N.; Cheng, C.F.C.; Smeets, E. The Importance of Manufacturing in Economic Development: Has This Changed? World Dev. 2017, 93, 293–315.
  7. de Bondt, G.J. A PMI-Based Real GDP Tracker for the Euro Area. J. Bus. Cycle Res. 2019, 15, 147–170.
  8. Zhang, D.; Xiao, M.; Yang, X.; He, Y. The Analysis of Manufacturing PMI Potential Trends of the US, EU, Japan and China. Procedia Comput. Sci. 2015, 55, 43–51.
  9. He, Y.; Zhang, Y.; Tian, P. The Study of Warning Threshold of Chinese Manufacturing PMI for Important Macroeconomic Indicators. Procedia Comput. Sci. 2015, 55, 1374–1380.
  10. Meyer, D.F.; Habanabakize, T. An assessment of the value of PMI and manufacturing sector growth in predicting overall economic output (GDP) in South Africa. Int. J. EBusiness EGovernment Stud. 2019, 11, 191–206.
  11. Wei, Y.; Bai, L.; Yang, K.; Wei, G. Are Industry-Level Indicators More Helpful to Forecast Industrial Stock Volatility? Evidence from Chinese Manufacturing Purchasing Managers Index. J. Forecast. 2021, 40, 17–39.
  12. Cziráky, D.; Sambt, J.; Rovan, J.; Puljiz, J. Regional Development Assessment: A Structural Equation Approach. Eur. J. Oper. Res. 2006, 174, 427–442.
  13. Raiser, M.; Di Tommaso, M. The Measurements and Determinants of Institutional Change: Evidence from Transition Economies; 2001. Available online: https://www.ebrd.com/downloads/research/economics/workingpapers/wp0060.pdf (accessed on 16 May 2023).
  14. Baker, S.R.; Bloom, N.; Davis, S.J. Measuring Economic Policy Uncertainty. Q. J. Econ. 2016, 131, 1593–1636.
  15. Gulen, H.; Ion, M. Policy Uncertainty and Corporate Investment. Rev. Financ. Stud. 2016, 29, 523–564.
  16. Davis, S.J. An Index of Global Economic Policy Uncertainty; National Bureau of Economic Research: Cambridge, MA, USA, 2016.
  17. Baker, S.R.; Bloom, N.; Davis, S.J. Has Economic Policy Uncertainty Hampered the Recovery? In Government Policies and the Delayed Economic Recovery; Hoover Institution: Stanford, CA, USA, 2012.
  18. Stock, J.H.; Watson, M.W. Disentangling the Channels of the 2007–2009 Recession; National Bureau of Economic Research: Cambridge, MA, USA, 2012.
  19. Collins, A.; Florin, M.-V.; Renn, O. COVID-19 Risk Governance: Drivers, Responses and Lessons to Be Learned. J. Risk Res. 2020, 23, 1073–1082.
  20. Zhang, J. The changing trend and internal law of the proportion of China’s manufacturing added value in GDP. J. Explor. Controv. 2021, 379, 57–72+181+178.
  21. Zhou, R.; Tang, D.; Da, D.; Chen, W.; Kong, L.; Boamah, V. Research on China’s Manufacturing Industry Moving towards the Middle and High-End of the GVC Driven by Digital Economy. Sustainability 2022, 14, 7717.
  22. Wang, L.; Wei, L. Low-End Locking or Crowding-out Effects-An Empirical Analysis of China’s Manufacturing Industry Embedded in GVCS. J. Trans Bus. Econ. 2018, 17, 216–236.
  23. Kimble, C.; Wang, H. China’s New Energy Vehicles: Value and Innovation. J. Bus. Strategy 2013, 34, 13–20.
  24. Wang, S. Exploring the Sustainability of China’s New Energy Vehicle Development: Fresh Evidence from Population Symbiosis. Sustainability 2022, 14, 10796.
  25. Xu, B.; Lin, B. Assessing the Development of China’s New Energy Industry. Energy Econ. 2018, 70, 116–131.
  26. Council, N.R. Rising to the Challenge: U.S. Innovation Policy for the Global Economy; National Academies Press: Washington, DC, USA, 2012; ISBN 978-0-309-25551-6.
  27. Appelbaum, E. Manufacturing Advantage: Why High-Performance Work Systems Pay Off; Cornell University Press: Ithaca, NY, USA, 2000; ISBN 978-0-8014-3765-6.
  28. Behun, M.; Gavurová, B.; Tkáčová, A.; Kotaskova, A. The impact of the manufacturing industry on the economic cycle of European Union countries. J. Compet. 2018, 10, 23–39.
  29. Istiak, K.; Serletis, A. Economic Policy Uncertainty and Real Output: Evidence from the G7 Countries. Appl. Econ. 2018, 50, 4222–4233.
  30. Arouri, M.; Estay, C.; Rault, C.; Roubaud, D. Economic Policy Uncertainty and Stock Markets: Long-Run Evidence from the US. Financ. Res. Lett. 2016, 18, 136–141.
  31. Nilavongse, R.; Michal, R.; Uddin, G.S. Economic Policy Uncertainty Shocks, Economic Activity, and Exchange Rate Adjustments. Econ. Lett. 2020, 186, 108765.
  32. Chen, L.; Du, Z.; Hu, Z. Impact of Economic Policy Uncertainty on Exchange Rate Volatility of China. Financ. Res. Lett. 2020, 32, 101266.
  33. Nguyen, C.P.; Le, T.-H.; Su, T.D. Economic Policy Uncertainty and Credit Growth: Evidence from a Global Sample. Res. Int. Bus. Financ. 2020, 51, 101118.
  34. Phan, D.H.B.; Iyke, B.N.; Sharma, S.S.; Affandi, Y. Economic Policy Uncertainty and Financial Stability—Is There a Relation? Econ. Model. 2021, 94, 1018–1029.
  35. Sahinoz, S.; Erdogan Cosar, E. Economic Policy Uncertainty and Economic Activity in Turkey. Appl. Econ. Lett. 2018, 25, 1517–1520.
  36. Demir, E.; Gozgor, G.; Lau, C.K.M.; Vigne, S.A. Does Economic Policy Uncertainty Predict the Bitcoin Returns? An Empirical Investigation. Financ. Res. Lett. 2018, 26, 145–149.
  37. Fang, L.; Bouri, E.; Gupta, R.; Roubaud, D. Does Global Economic Uncertainty Matter for the Volatility and Hedging Effectiveness of Bitcoin? Int. Rev. Financ. Anal. 2019, 61, 29–36.
  38. Pirgaip, B.; Dinçergök, B. Economic Policy Uncertainty, Energy Consumption and Carbon Emissions in G7 Countries: Evidence from a Panel Granger Causality Analysis. Environ. Sci. Pollut. Res. 2020, 27, 30050–30066.
  39. Huang, H.; Ali, S.; Solangi, Y.A. Analysis of the Impact of Economic Policy Uncertainty on Environmental Sustainability in Developed and Developing Economies. Sustainability 2023, 15, 5860.
  40. Adams, S.; Adedoyin, F.; Olaniran, E.; Bekun, F.V. Energy Consumption, Economic Policy Uncertainty and Carbon Emissions; Causality Evidence from Resource Rich Economies. Econ. Anal. Policy 2020, 68, 179–190.
  41. Nakhli, M.S.; Shahbaz, M.; Jebli, M.B.; Wang, S. Nexus between Economic Policy Uncertainty, Renewable & Non-Renewable Energy and Carbon Emissions: Contextual Evidence in Carbon Neutrality Dream of USA. Renew. Energy 2022, 185, 75–85.
  42. Yu, J.; Shi, X.; Guo, D.; Yang, L. Economic Policy Uncertainty (EPU) and Firm Carbon Emissions: Evidence Using a China Provincial EPU Index. Energy Econ. 2021, 94, 105071.
  43. Luo, X.; Yu, M.; Jin, Y. The Impact of Economic Policy Uncertainty on Enterprise Green Innovation: A Study on the Moderating Effect of Carbon Information Disclosure. Sustainability 2023, 15, 4915.
  44. Khan, Y.; Hassan, T.; Kirikkaleli, D.; Xiuqin, Z.; Shukai, C. The Impact of Economic Policy Uncertainty on Carbon Emissions: Evaluating the Role of Foreign Capital Investment and Renewable Energy in East Asian Economies. Environ. Sci. Pollut. Res. 2022, 29, 18527–18545.
  45. Yu, H.; Fang, L.; Sun, W. Forecasting Performance of Global Economic Policy Uncertainty for Volatility of Chinese Stock Market. Phys. Stat. Mech. Appl. 2018, 505, 931–940.
  46. Yu, M.; Song, J. Volatility Forecasting: Global Economic Policy Uncertainty and Regime Switching. Phys. Stat. Mech. Appl. 2018, 511, 316–323.
  47. Qin, M.; Su, C.-W.; Xiao, Y.-D.; Zhang, S. Should Gold Be Held under Global Economic Policy Uncertainty? J. Bus. Econ. Manag. 2020, 21, 725–742.
  48. Long, S.; Li, J.; Luo, T. The Asymmetric Impact of Global Economic Policy Uncertainty on International Grain Prices. J. Commod. Mark. 2022, 30, 100273.
  49. Yu, Z.; Qian, L. Research on the dynamic correlation between U.S. manufacturing PMI and GDP growth rate. J. Times Financ. 2012, 499, 278.
  50. Yanik, R.; Osman, A.B.; Ozturk, O. Impact of Manufacturing PMI on Stock Market Index: A Study on Turkey. SSRN. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3743277 (accessed on 5 July 2023).
  51. Shaikh, I. On the Relation between Purchasing Manager’s Index and Trade Policy Uncertainty: Evidence from China, Japan and the USA. J. Chin. Econ. Foreign Trade Stud. 2021, 14, 202–223.
  52. Zhang, H.; Jiang, Z.; Gao, W.; Yang, C. Time-Varying Impact of Economic Policy Uncertainty and Geopolitical Risk on Tourist Arrivals: Evidence from a Developing Country. Tour. Manag. Perspect. 2022, 41, 100928.
  53. Gu, X.; Zhu, Z.; Yu, M. The Macro Effects of GPR and EPU Indexes over the Global Oil Market—Are the Two Types of Uncertainty Shock Alike? Energy Econ. 2021, 100, 105394.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Video Production Service
Feedback