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What drives China’s long-term economic growth trend? A re-measurement based on a time-varying mixed-frequency dynamic factor model

    Dayu Liu Affiliation
    ; Bin Xu Affiliation
    ; Yang Song Affiliation
    ; Qiaoru Wang Affiliation

Abstract

The unprecedented downward pressure of China’s economic growth trend raises several questions, including what the current level of China’s long-term economic growth trend is, and what drives and how to inhibit the downward trend. Therefore, we develop a time-varying mixedfrequency dynamic factor model using data with different start dates to measure the trend, and perform a real-time decomposition of changes in the trend. We find that the trend has entered a downward stage since 2007, left a high-speed phase since 2012, and stepped in an accelerated downward stage since 2018. The current level of the trend is about 4%. However, the lower limit of the 90% confidence interval is below 2%, which is lower than natural rate level. Additionally, decelerated capital deepening, diminishing demographic dividend and technological recession all drive the downward trend. Compared to the relatively weak push-down effects of capital deepening and demographic dividend that are less than two percentage points, the downward trend is mainly driven by technological recession. Given that technological progress is unlikely to improve significantly in the short run, mitigating the mismatch between technological progress and obsolete capital, revitalizing existing capital stock, and increasing the efficiency of technology utilization become more feasible means.

Keyword : economic growth, long-term trend, time-varying mixed-frequency dynamic factor model using data with different start dates, factor decomposition

How to Cite
Liu, D., Xu, B., Song, Y., & Wang, Q. (2023). What drives China’s long-term economic growth trend? A re-measurement based on a time-varying mixed-frequency dynamic factor model. Technological and Economic Development of Economy, 29(3), 741–774. https://doi.org/10.3846/tede.2023.18705
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Apr 12, 2023
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References

Antolin-Diaz, J., Drechsel, T., & Petrella, I. (2017). Tracking the slowdown in long-run GDP growth. The Review of Economics and Statistics, 99(2), 343–356. https://doi.org/10.1162/REST_a_00646

Asali, M. (2020). Vgets: A command to estimate general-to-specific VARs, Granger causality, steady-state effects, and cumulative impulse-responses. The Stata Journal, 20(2), 426–434. https://doi.org/10.1177/1536867X20931004

Autor, D., Dorn, D., & Hanson, G. (2013). The China syndrome: Local labor market effects of import competition in the United States. American Economic Review, 103(6), 2121–2168. https://doi.org/10.1257/aer.103.6.2121

Backhouse, R. E., & Boianovsky, M. (2016). Secular stagnation: The history of a macroeconomic heresy. The European Journal of the History of Economic Thought, 23(6), 946–970. https://doi.org/10.1080/09672567.2016.1192842

Ball, R., & Brown, P. (1968). An empirical evaluation of accounting income numbers. Journal of Accounting Research, 6(2), 159–178. https://doi.org/10.2307/2490232

Banbura, M., & Modugno, M. (2014). Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. Journal of Applied Econometrics, 29(1), 133–160. https://doi.org/10.1002/jae.2306

Baxter, M. (1991). Business cycles, stylized facts, and the exchange rate regime: Evidence from the United States. Journal of International Money and Finance, 10(1), 71–88. https://doi.org/10.1016/0261-5606(91)90027-H

Baxter, M., & King, R. G. (1999). Measuring business cycles: Approximate band-pass filters for economic time series. Review of Economics & Statistics, 81(4), 575–593. https://doi.org/10.1162/003465399558454

Beveridge, S., & Nelson, C. R. (1981). A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the “business cycle”. Journal of Monetary Economics, 7(2), 151–174. https://doi.org/10.1016/0304-3932(81)90040-4

Camacho, M., & Perez-Quiros, G. (2010). Introducing the euro-sting: Short-term indicator of euro area growth. Journal of Applied Econometrics, 25(4), 663–694. https://doi.org/10.1002/jae.1174

Canova, F. (1998). Detrending and business cycle facts. Journal of Monetary Economics, 41(3), 475–512. https://doi.org/10.1016/S0304-3932(98)00006-3

Chernis, T., Cheung, C., & Velasco, G. (2020). A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth. International Journal of Forecasting, 36(3), 851–872. https://doi.org/10.1016/j.ijforecast.2019.09.006

Christiano, L., & Fitzgerald, T. J. (2003). The band-pass filter. International Economic Review, 44(2), 435–465. https://doi.org/10.1111/1468-2354.t01-1-00076

Clark, P. (1987). The cyclical component of U.S. economic activity. Quarterly Journal of Economics, 102(4), 797–814. https://doi.org/10.2307/1884282

Claus, I. (2003). Estimating potential output for New Zealand. Applied Economics, 35(7), 751–760. https://doi.org/10.1080/00036840210155168

Cogley, T. (2005). How fast can the new economy grow? A Bayesian analysis of the evolution of trend growth. Journal of Macroeconomics, 27(2), 179–207. https://doi.org/10.1016/j.jmacro.2003.11.018

Cogley, T., & Nason, J. M. (1995). Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research. Journal of Economic Dynamics and Control, 19(1), 253–278. https://doi.org/10.1016/0165-1889(93)00781-X

Cogley, T., & Sargent, T. J. (2005). Drifts and volatilities: Monetary policies and outcomes in the Post WWII U.S. Review of Economic Dynamics, 8(2), 262–302. https://doi.org/10.1016/j.red.2004.10.009

Di Giovanni, J., Levchenko, A., & Zhang, J. (2014). The global welfare impact of China: Trade integration and technological change. American Economic Journal: Macroeconomics, 6(3), 153–183. https://doi.org/10.1257/mac.6.3.153

Dinlersoz, E. M., & Fu, Z. (2022). Infrastructure investment and growth in China: A quantitative assessment. Journal of Development Economics, 158, 102916. https://doi.org/10.1016/j.jdeveco.2022.102916

Faber, B. (2014). Trade integration, market size, and industrialization: Evidence from China’s national trunk highway system. Review of Economic Studies, 81(3), 1046–1070. https://doi.org/10.1093/restud/rdu010

Fama, E. F., Fisher, L., Jensen, M. C., & Roll, R. (1969). The adjustment of stock prices to new information. International Economic Review, 10(1), 1–21. https://doi.org/10.2307/2525569

Greenwood, J., Hercowitz, Z., & Krusell, P. (1997). Long-run implications of investment-specific technological change. American Economic Review, 87(3), 342–362.

Harvey, A. C. (1989). Forecasting, structural time series models and the Kalman filter. Cambridge University Press. https://doi.org/10.1017/CBO9781107049994

Harvey, A. & Jaeger, A. (1993). Detrending, stylized facts and the business cycle. Journal of Applied Econometrics, 8(3), 231–247. https://doi.org/10.1002/jae.3950080302

Hodrick, R. J., & Prescott, E. C. (1997). Postwar U.S. business cycles: An empirical investigation. Journal of Money, Credit and Banking, 29(1), 1–16. https://doi.org/10.2307/2953682

Hsieh, C. T., & Ossa, R. (2016). A global view of productivity growth in China. Journal of International Economics, 102, 209–224. https://doi.org/10.1016/j.jinteco.2016.07.007

Jarocinski, M., & Lenza, M. (2018). An inflation predicting measure of the output gap in the euro area. Journal of Money, Credit and Banking, 50(6), 1189–1224. https://doi.org/10.1111/jmcb.12496

Jiang, Y., Guo, Y., & Zhang, Y. (2017). Forecasting China’s GDP growth using dynamic factors and mixed-frequency data. Economic Modelling, 66, 132–138. https://doi.org/10.1016/j.econmod.2017.06.005

Ju, J., Lin, J. Y., & Wang, Y. (2015). Endowment structures, industrial dynamics, and economic growth. Journal of Monetary Economics, 76, 244–263. https://doi.org/10.1016/j.jmoneco.2015.09.006

King, R. G., & Rebelo, S. T. (1993). Low frequency filtering and real business cycles. Journal of Economic Dynamics and Control, 17(1), 207–231. https://doi.org/10.1016/S0165-1889(06)80010-2

Leukhina, O., & Turnovsky, S. J. (2016). Population size effects in the structural development of England. American Economic Journal: Macroeconomics, 8(3), 195–229. https://doi.org/10.1257/mac.20140032

Li, K., & Lin, B. (2018). How to promote energy efficiency through technological progress in China? Energy, 143, 812–821. https://doi.org/10.1016/j.energy.2017.11.047

Li, X., Zhou, X., & Yan, K. (2022) Technological progress for sustainable development: An empirical analysis from China. Economic Analysis and Policy, 76, 146–155. https://doi.org/10.1016/j.eap.2022.08.002

Liu, C., & Xia, G. (2018). Research on the dynamic interrelationship among R&D investment, technological innovation, and economic growth in China. Sustainability, 10(11), 4260. https://doi.org/10.3390/su10114260

Liu, W., & Fan, X. (2019). China remains in an important period of strategic opportunities of its development: China’s potential growth rate and growth leaps. Management World, 35(01), 13–23 (in Chinese).

Marcellino, M., Porqueddu, M., & Venditti, F. (2016). Short-Term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility. Journal of Business and Economics Statistics, 34(1), 118–127. https://doi.org/10.1080/07350015.2015.1006773

Mariano, B. S., & Murasawa, Y. (2003). A new coincident index of business cycles based on monthly and quarterly series. Journal of Applied Econometrics, 18(4), 427–443. https://doi.org/10.1002/jae.695

Minetti, R., & Peng, T. (2018). Credit policies, macroeconomic stability and welfare: The case of China. Journal of Comparative Economics, 46(1), 35–52. https://doi.org/10.1016/j.jce.2016.11.005

Morley, J. C., Nelson, C. R., & Zivot, E. (2003). Why are the Beveridge-Nelson and unobserved-components decompositions of GDP so different? The Review of Economics and Statistics, 85(2), 235–243. https://doi.org/10.1162/003465303765299765

Orphanides, A., & Van Norden, S. (2002). The unreliability of output gap estimations in real time. Review of Economics and Statistics, 84(4), 569–583. https://doi.org/10.1162/003465302760556422

Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821–852. https://doi.org/10.1111/j.1467-937X.2005.00353.x

Ravn, M. O., & Uhlig, H. (2002). On adjusting the Hodrick-Prescott filter for the frequency of observations. Review of Economics and Statistics, 84(2), 371–376. https://doi.org/10.1162/003465302317411604

Shi, Z., Wu, Y., Chiu, Y., Shi, C., & Na, X. (2022). Comparing the efficiency of regional knowledge innovation and technological innovation: A case study of China. Technological and Economic Development of Economy, 28(5), 1392–1418. https://doi.org/10.3846/tede.2022.17125

Stock, J. H., & Watson, M. W. (1988). Testing for common trends. Journal of the American Statistical Association, 83(404), 1097–1107. https://doi.org/10.1080/01621459.1988.10478707

Stock, J. H., & Watson, M. W. (2012). Disentangling the channels of the 2007–2009 recession (NBER Working Paper, No. 18094). https://doi.org/10.3386/w18094

Wang, Q., Liu, J., & Liu, D. (2019). Consistent fluctuation, regional coordinated development and idiosyncratic divergence of provincial business cycles in China. China Industrial Economics, 10, 61–79 (in Chinese).

Ye, G. (2015). Research on the coincident index and economic fluctuations in China with mixed–frequency data. Statistical Research, 32(08), 17–26 (in Chinese).

Zhang, Y. (2021). The regional disparity of influencing factors of technological innovation in China: Evidence from high-tech industry. Technological and Economic Development of Economy, 27(4), 811–832. https://doi.org/10.3846/tede.2021.14828

Zheng, T., & Wang, X. (2013). Measuring China’s business cycle with mixed-frequency data and its real time analysis. Economic Research Journal, 48(06), 58–70 (in Chinese).