Openness and Money Demand: Measuring the Opportunity Cost Effects of International Financial Markets
QIN Duo, LU Shan, WANG Huiwen, Sophie van Huellen, WANG Qingchao
School of Oriental and African Studies, University of London; School of Statistics and Mathematics, Central University of Finance and Economics; School of Economics and Management, Beihang University;Expedia Group
Summary:
Standard money demand models neglect the direct effects of economic openness. This omission is problematic when domestic opportunity cost variables fail to fully reflect the dynamics of international financial markets. Examining the effect of this omission is of great practical importance given the ever-increasing openness of China's economy. We propose composite international financial indices (CIFIs) to measure the latent variables that are omitted in standard money demand models. Using techniques from machine learning and measurement theory, we develop a novel model-based approach to construct CIFIs that combines both unsupervised and supervised dimension reduction methods. The choice of the popular error-correction model for the money demand function leads us to construct two types of CIFIs: long-run and short-run CIFIs. We collect a large set of around 100 financial input indicators to construct CIFIs using monthly data for the 1993M9-2015M6 period. These input indicators are obtained from 21 economies, covering almost all of China's major trading partners. The CIFI construction algorithm contains two stages of aggregation. First, it produces composite financial input indicators by aggregating groups of financial indicators. These groups are formed using clustering methods under the unsupervised learning approach. Second, it uses supervised dimension reduction methods to aggregate the composite financial input indicators following the principle of partial least-squares (PLS). The algorithm produces short-run CIFIs by targeting money growth rates, whereas it forms the target of long-run CIFIs using the error-correction term of standard money demand models. The second supervised aggregation stage sets the input indicators as leading indicators by construction, allows for dynamic dis-synchronization among them, and performs dynamic backward selection of different lags to make the dynamic input forms of the leading indicators as simple as possible. Concatenation is imposed on the resulting CIFIs during regular data updates. Experiments with CIFI-enhanced money demand models yield positive outcomes. Our key findings are as follows: (i) We find strong evidence of the effects of foreign opportunity costs on China's money demand based on the statistical significance and constancy of the coefficients of CIFIs and overall comparisons of model explanatory power; (ii) the effect of the short-run CIFIs is particularly robust, as evidenced by the 2007-2008 US-led financial crisis; however, in the enhanced error-correction term of the long-run CIFIs, a temporary coefficient variation toward insignificance is observed, which is interpreted as resulting from the emergency measures taken by the People's Bank of China in response to the crisis; (iii) model performance comparisons of the CIFIs produced with and without the first step of unsupervised dimension reduction show the necessity of this step in that it helps reduce redundant information in large financial datasets; (iv) tracing the compositions of CIFIs back to individual financial input indicators yields various patterns and features that enable the identification of the sources of the aggregate foreign opportunity cost effects. The explicit links between disaggregate input indicators and aggregate CIFIs provide valuable tools for policymakers to monitor external financial shocks from different geographical regions and markets and assess their aggregate risks in real time. Our CIFI algorithm opens a novel route of model-based composite construction. This route also sheds light on why the conventional route of principal component-based factor analysis is insufficient to construct composite indices for macro-modeling.
作者简介: 秦 朵,经济学博士,荣誉教授,伦敦大学亚非学院,E-mail:dq1@soas.ac.uk. 王惠文,工学博士,教授,北京航空航天大学经济管理学院,E-mail:wanghw@vip.sina.com. Sophie van Huellen,经济学博士,讲师,伦敦大学亚非学院,E-mail:Svanhuellen@gmail.com. 王庆超,经济学博士,数据科学家,Expedia Group,E-mail:qingchaowan@gmail.com.
引用本文:
秦朵, 卢珊, 王惠文, Sophie van Huellen, 王庆超. 经济开放与货币需求:国际金融风险及持币成本的测度[J]. 金融研究, 2021, 495(9): 30-50.
QIN Duo, LU Shan, WANG Huiwen, Sophie van Huellen, WANG Qingchao. Openness and Money Demand: Measuring the Opportunity Cost Effects of International Financial Markets. Journal of Financial Research, 2021, 495(9): 30-50.
Aramonte, S., J. W. Schindler and S. Rosen, 2013, “Assessing and Combining Financial Conditions Indexes,” Finance and Economics Discussion Series, 2013(39), pp.1～38.
[6]
Baba, Y., D. F. Hendry and R. M. Starr, 1992, “The Demand for M1 in the U.S.A.1960-1988,” The Review of Economic Studies, 59(1), pp.25～61.
[7]
Borio, C., 2011, “Rediscovering the Macroeconomic Roots of Financial Stability Policy: Journey, Challenges, and a Way Forward,” Annu. Rev. Financ. Econ., 3(1), pp.87～117.
[8]
Borio, C., 2013, “The Great Financial Crisis: Setting Priorities for New Statistics,” Journal of Banking Regulation, 14(3-4), pp.306～317.
[9]
Borio, C., 2014, “The International Monetary and Financial System: its Achilles Heel and What to do about it,” BIS Working Paper, No. 456.
[10]
Calza, A., D. Gerdesmeier and J. V. F. Levy, 2001, “Euro Area Money Demand: Measuring the Opportunity Costs Appropriately,” IMF Working Paper, No. 01/179.
[11]
Charrad, M., N. Ghazzali, V. Boiteau and A. Niknafs, 2012, “Nbclust Package: Finding the Relevant Number of Clusters in a Dataset,” User.
[12]
Chowdhury, A., 1995, “The Demand for Money in a Small Open Economy: the Case of Switzerland,” Open Economies Review, 6(2), pp.131～144.
[13]
Clements, M. P. and D. F. Hendry, 2008, A Companion to Economic Forecasting, Published by John Wiley & Sons.
[14]
Cox, D. R., 1961, “Tests of Separate Families of Hypotheses,” Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics, 1, pp.105～123.
[15]
El-Shagi, M. and Y. Zheng, 2017, “Money Demand in China: A Meta-Study,” CFDS Discussion Paper Series 2017/3.
[16]
Engle, R. F., D. F. Hendry, and J.-F. Richard, 1983, “Exogeneity,” Econometrica: Journal of the Econometric Society, 51, pp.277～304.
[17]
Hansen, B. E., 1992, “Testing for Parameter Instability in Linear Models,” Journal of policy Modeling, 14 (4), pp.517～533.
[18]
Hatzius, J., P. Hooper, F. S. Mishkin, K. L. Schoenholtz and M.W.Watson, 2010, “Financial Conditions Indexes: A Fresh Look after the Financial Crisis,” NBER Working Paper, No. w16150.
[19]
Hendry, D. F. and N. R. Ericsson, 1991, “An Econometric Analysis of uk Money Demand in Monetary Trends in the United States and the United Kingdom by Milton Friedman and anna j. schwartz,” The American Economic Review, pp.8～38.
[20]
Howell, R. D., E. Breivik and J. B. Wilcox, 2013, “Formative Measurement: A Critical Perspective,” ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 44(4), pp.44～55.
[21]
Leroux, M., R. Kotchoni and D. Stevanovic, 2017, “Macroeconomic Forecast Accuracy in a Data-rich Environment,” Technical Report, CIRANO, 2017(5).
[22]
Marcellino, M., 2013, “Leading Indicators,” Handbook of Economic Forecasting, 1, pp.879～960
[23]
Markus, K. A. and D. Borsboom, 2013, Frontiers of Test Validity Theory: Measurement, Causation, and Meaning, Published by Routledge.
[24]
Qin, D. and X. He, 2012, “Modelling the impact of Aggregate Financial Shocks External to the Chinese Economy,” BOFIT Discussion Paper, No. 25/2012.
[25]
Qin, D., S. van Huellen, Q.C. Wang and T. Moraitis, 2018, “Algorithm Modelling of Financial Conditions for Macro Predictive Purposes: Pilot Application on the USA,” Technical Report, SOAS Department of Economics Working Paper Series, No. 212.
[26]
Sokal, R. R., 1958, “A Statistical Method for Evaluating Systematic Relationship,” University of Kansas Science Bulletin, 28, pp.1409～1438.
[27]
Stiglitz, J. E., 2018, “Where Modern Macroeconomics went Wrong,” Oxford Review of Economic Policy, 34(1-2), pp.70～106.
[28]
Terry, L. and K. Kelley, 2012, “Sample Size Planning for Composite Reliability Coefficients: Accuracy in Parameter Estimation via Narrow Confidence Intervals,” British Journal of Mathematical and Statistical Psychology, 65(3), pp.371～401.
[29]
Van Huellen, S., D. Qin, S. Lu, H. Wang, Q.C. Wang and T. Moraitis, 2020, “Modelling Opportunity Cost Effects in Money Demand due to Openness,” International Journal of Finance & Economics, pp.1～48.
[30]
Wang, Q.-C., 2017, Testing a New Approach to Construct International Financial Market indices: an Application to Asian-Pacific Economies, Published by PhD thesis, SOAS University of London.
[31]
Wold, H, 1966, “Estimation of Principal Components and Related Models by Iterative Least Squares,” Multivariate analysis, pp.391～420.
[32]
Wold, H, 1975, “Path Models with Latent Variables: The Nipals Approach,” Quantitative Sociology, pp.307～357.
[33]
Wold, H, 1980, “Model Construction and Evaluation When Theoretical Knowledge is Scarce: Theory and Application of Partial Least Squares,” Evaluation of Econometric Models, pp.47～74.
