## 6. China: Quandt-Andrews Unknown Breakpoint Test in the Money Demand Function

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---

### Introduction and policy relevance
- The effectiveness of targeting a monetary aggregate depends on the stability of the money demand function (MDF).
- Structural changes in the financial system (financial innovation and liberalization) can alter the link between money aggregates, output, and interest rates and therefore weaken the usefulness of quantity-based monetary targeting.
- China has continued to rely heavily on M2 targets and active use of window guidance; rapid financial reform and innovation since 2008 make assessing MDF stability in China particularly important.

### Drivers of MDF stability and instability (literature synthesis)
- Financial innovation:
  - Can decrease transaction demand for money and reduce income elasticity (Lieberman 1977; Arrau and others, 1995).
  - Can increase interest-rate sensitivity through the emergence of new interest-bearing assets (Sharma and Ericsson, 1998; Pradhan and Subramanian, 2003).
  - Liquidity effects from central bank bond purchases can reduce or flip the sign of interest sensitivity (Ireland, 2009; Alvarez and Lippi, 2011).
- Financial liberalization:
  - Increased competition and removal of controls tend to lower transaction costs, favor financial deepening, and increase interest elasticity while reducing income elasticity (McPhail, 1991; Haug, 1999).
- Other factors:
  - Wealth effects (positive or negative on money demand depending on income vs. substitution effects).
  - Macroeconomic uncertainty reduces preference for liquidity and can lower income elasticity and interest-rate sensitivity (Choi and Oh, 2003; Atta-Mensah, 2004).

### China’s financial liberalization and international lessons
- China’s reforms since the early 1980s: commercialization of state-owned banks, shareholding banks (since 1996), opening to foreign banks, greater interest-rate flexibility (expansion of floating ranges in mid-2012; eliminating the floor on lending rates in July 2013).
- Nonbank financing and modern financial products expanded rapidly (WMPs, mutual funds, options); in 2012, around half of financial intermediation took place outside the traditional banking sector (compared to about 10 percent in the early 2000s).
- Empirical lessons from Japan, Korea, and the United States:
  - Japan and Korea: financial liberalization and innovation reduced the effectiveness of quantitative targeting and led to a shift toward indirect instruments (market-determined interest rates and open-market operations).
  - United States: gradual changes and innovations (e.g., money market funds, NOW accounts) produced gradual shifts in velocity and multiplier; MDF shifted slowly over time.

### Empirical methodology and data (overview)
- Two-step approach:
  1. Test long-run stability (cointegration) between real money balances, real income, and short-term interest rates using the standard MDF specification (log real money = α + β log real income + γ interest rate + error).
  2. Analyze short-run, time-varying sensitivities via recursive/expanding-window estimation of an error-correction representation:
     - Δ(money) = φ Δ(income) + ψ Δ(interest rate) + ρ̂(-1)·λ + error, where ρ̂(-1) is the lagged error correction term; φ and ψ are the short-run sensitivities estimated recursively.
- Drivers tested in panel regressions of the time-varying parameters include:
  - Financial innovation proxied by Log(M3/M2)
  - Domestic financial liberalization (DFL) index (1/2/3 for none/partial/full, source: Kaminsky and Schmukler (2008))
  - REER volatility, changes in equity prices, changes in housing prices, G7 dummy, time trend, banking crisis dummy (Laeven and Valencia, 2013).
- Data:
  - Quarterly panels for eight countries (Denmark, France, Japan, Korea, Mexico, Norway, Sweden, United States) covering 1970–2012 where available.
  - China analysis uses available data (M2) from 1998:Q4 to 2012:Q3; Quandt-Andrews tests reported for 2002:Q1–2012:Q3.

### Results — cross-country evidence (high-level)
- Long-run cointegration:
  - For seven of the eight countries (all but Mexico), the Johansen cointegration test rejects a stable MDF over full sample periods.
  - Subsample (before vs. after liberalization) cointegration often holds, suggesting structural breaks associated with financial reforms (exceptions: Sweden and the United States).
  - Selected reported entries:
    - Denmark: Liberalization date 1981:Q1 — Full sample: No; Before Liberalization: Yes; After Liberalization: Yes.
    - France: Liberalization date 1985:Q1 — Full sample: No; Before Liberalization: Yes; After Liberalization: Yes.
    - Japan: Liberalization date 1991:Q4 — Full sample: No; Before Liberalization: No; After Liberalization: Yes.
    - Korea: Liberalization date 1988:Q1 — Full sample: No; Before Liberalization: No; After Liberalization: Yes.
    - Mexico: Liberalization date 1989:Q2 — Full sample: Yes; Before Liberalization: Yes; After Liberalization: No.
    - Norway: Liberalization date 1988:Q1 — Full sample: No; Before Liberalization: Yes; After Liberalization: Yes.
    - Sweden: Liberalization date 1985:Q1 — Before Liberalization: No; (full/after not available).
    - United States: Liberalization date 1982:Q1 — Full sample: No; Before Liberalization: No; After Liberalization: No.
- Time-varying short-run sensitivities (recursive estimates summary statistics):
  - Income parameter:
    - all sample mean 0.5718
    - standard deviation 0.6102
    - minimum 0.0101
    - maximum 1.1134
    - Observation 506 (significant at 10%: 414)
  - Interest rate parameter:
    - all sample mean -0.0056
    - standard deviation 0.0055
    - minimum -0.0216
    - maximum 0.0084
    - Observation 506 (significant at 10%: 205)
- Panel regressions of time-varying parameters (baseline) — selected reported coefficients and statistics:
  - Innovation [Log(M3/M2)]:
    - coefficient on income parameter -1.0120*** (standard error 0.0493)
    - coefficient on interest rate parameter 0.00722* (standard error 0.00390)
  - Domestic financial liberalization (DFL):
    - coefficient on income parameter -0.0291** (standard error 0.0143)
    - coefficient on interest rate parameter 0.00336*** (standard error 0.000392)
  - Intercepts and fit:
    - Income parameter regression intercept 0.892*** (0.0215); Observations 414; R-squared 0.6350.
    - Interest rate parameter regression intercept 0.662*** (0.0312); Observations 205; R-squared 0.260.
  - Interpretation: financial innovation and liberalization reduce the sensitivity of money demand to income and generally make M2 less sensitive to interest rates (positive coefficient on interest rate parameter implies a move toward zero for a mostly negative parameter).

### Interaction effects and robustness
- Interaction of innovation and DFL:
  - Positive and significant on income parameter.
  - Negative and significant on interest rate parameter.
  - Implication: simultaneous innovation and liberalization can increase MDF stability in interaction, while individually they tend to reduce income sensitivity.
- Housing price changes:
  - Associated with increased interest-rate sensitivity of money demand.
- REER volatility and equity price changes:
  - No statistically significant relationship with MDF parameters in the reported regressions.
- Robustness checks including G7 dummy, time trend, and banking crisis dummy (Laeven and Valencia, 2013) preserve main findings: financial innovation and liberalization are key drivers of MDF instability.

### China-specific findings
- Data and timing:
  - China analysis uses M2 as PBC’s intermediate target; sample periods include 1998:Q4–2012:Q3 for broader discussion and Quandt-Andrews tests conducted on 2002:Q1–2012:Q3.
  - Velocity began a downward trend since 2008; nonbank financial activity and total social financing increased rapidly from 2008/2009.
- Structural break detection:
  - Chow test and Quandt-Andrews unknown breakpoint test identify 2009:Q1 as a breakpoint coinciding with the large countercyclical stimulus and rapid expansion of nonbank activity.
  - Reported Quandt-Andrews test statistics for 2002:Q1–2012:Q3 (number of breaks compared: 38; note: Null hypothesis is no breakpoints within 5 percent of trimmed data):
    - Maximum LR F-statistic (2009:Q1) 189.14***
    - Maximum Wald F-statistic (2009:Q1) 189.14***
    - Exp. LR F-statistic 90.93***
    - Exp. Wald F-statistic 90.93***
    - Average LR F-statistic 30.50***
    - Average Wald F-statistic 30.50***
    - *** p<0.01.
- Cointegration and time-varying parameters:
  - Cointegration test on subsample 2002:Q1–2008:Q4 indicates a stable MDF; extending the sample to 2012:Q3 the cointegration relationship no longer holds, consistent with a structural shift around 2009:Q1.
  - Recursive expanding-window estimates using M2 show:
    - A significant decline in income sensitivity over time (income sensitivity notably lower in China than in other studied countries).
    - Interest rate sensitivity remains approximately the same but tends to be insignificant, implying money demand is not sensitive to benchmark rates.
  - Implication: M2’s usefulness as an intermediate target is weakening and regulated rates are questionable as effective policy instruments in the evolved financial environment.

### Policy implications and recommendations
- Evidence indicates that financial innovation and liberalization reduce the stability of the MDF and the sensitivity of M2 to income and interest rates.
- China-specific recommendation:
  - Shift toward greater reliance on market-based, price-oriented instruments (interest rates) as intermediate targets for monetary control as financial reforms and nonbank activity expand.
  - Ongoing interest-rate liberalization will enhance the effectiveness of indirect monetary policy and the usefulness of interest rates as policy signals, but a transition will require a period of learning-by-doing.
- General implication for central banks:
  - As financial systems liberalize and innovate, central banks should monitor broader aggregates and complementary indicators (including broader measures than M2) and consider strengthening interest-rate-based frameworks and market operations.

*Source: 6. China: Quandt-Andrews Unknown Breakpoint Test in the Money Demand Function .......18*

### References .............................................................................................................

### _wp1475 - References

### Figures
- 1. China: Total Social Financing, 2006–12 ...............................................................................7
- 2. China: M2 Velocity and Multiplier Trends, 2001:Q2–2012:Q2 ...........................................8
- 3. Japan: M2 Velocity and Multiplier Trends, 1973:Q1–2005:Q4 ............................................9
- 4. Korea: M2 Velocity and Multiplier Trends, 1973:Q1–2005:Q4 .........................................10
- 5. United States: M2 Velocity and Multiplier Trends, 1973:Q1–2005:Q4..............................11
- 6. China: Interest Rate and Income Parameters Using M2, 2001:Q2–2012:Q2 ......................18

### Tables
- 1. Definition of Domestic Financial Liberalization (DFL) Index ............................................13
- 2. Cointegration Tests of Real Money, Real Output, and Short-Term Interest Rates 
        Using M2 .........................................................................................................................14
- 3. Summary Statistics of the Parameters in the Money Demand Function  ............................14
- 4. Factors of Instability in Money Demand Function Parameters ...........................................15
- 5. Factors of Instability in Money Demand Function Parameters ...........................................17

*Source: _wp1475 - References*

### 6. China: Quandt-Andrews Unknown Breakpoint Test in the Money Demand Function .......18

### 6. China: Quandt-Andrews Unknown Breakpoint Test in the Money Demand Function .......18

### Introduction and policy relevance
- The effectiveness of targeting a monetary aggregate depends on the stability of the money demand function (MDF).  
- Structural changes in the financial system (financial innovation and liberalization) can alter the link between money aggregates, output, and interest rates and therefore weaken the usefulness of quantity-based monetary targeting.  
- China has continued to rely heavily on M2 targets and active use of window guidance; rapid financial reform and innovation since 2008 make assessing MDF stability in China particularly important.

### Drivers of MDF stability and instability (literature synthesis)
- Financial innovation:
  - Can decrease transaction demand for money and reduce income elasticity (Lieberman 1977; Arrau and others, 1995).
  - Can increase interest-rate sensitivity through the emergence of new interest-bearing assets (Sharma and Ericsson, 1998; Pradhan and Subramanian, 2003).
  - Liquidity effects from central bank bond purchases can reduce or flip the sign of interest sensitivity (Ireland, 2009; Alvarez and Lippi, 2011).
- Financial liberalization:
  - Increased competition and removal of controls tend to lower transaction costs, favor financial deepening, and increase interest elasticity while reducing income elasticity (McPhail, 1991; Haug, 1999).
- Other factors:
  - Wealth effects (positive or negative on money demand depending on income vs. substitution effects).
  - Macroeconomic uncertainty reduces preference for liquidity and can lower income elasticity and interest-rate sensitivity (Choi and Oh, 2003; Atta-Mensah, 2004).

### China’s financial liberalization and international lessons
- China’s reforms since the early 1980s: commercialization of state-owned banks, shareholding banks (since 1996), opening to foreign banks, greater interest-rate flexibility (expansion of floating ranges in mid-2012; eliminating the floor on lending rates in July 2013).
- Nonbank financing and modern financial products expanded rapidly (WMPs, mutual funds, options); in 2012, around half of financial intermediation took place outside the traditional banking sector (compared to about 10 percent in the early 2000s).
- Empirical lessons from Japan, Korea, and the United States:
  - Japan and Korea: financial liberalization and innovation reduced the effectiveness of quantitative targeting and led to a shift toward indirect instruments (market-determined interest rates and open-market operations).
  - United States: gradual changes and innovations (e.g., money market funds, NOW accounts) produced gradual shifts in velocity and multiplier; MDF shifted slowly over time.

### Empirical methodology and data (overview)
- Two-step approach:
  1. Test long-run stability (cointegration) between real money balances, real income, and short-term interest rates using the standard MDF specification (log real money = α + β log real income + γ interest rate + error).
  2. Analyze short-run, time-varying sensitivities via recursive/expanding-window estimation of an error-correction representation:
     - Δ(money) = φ Δ(income) + ψ Δ(interest rate) + ρ̂(-1)·λ + error, where ρ̂(-1) is the lagged error correction term; φ and ψ are the short-run sensitivities estimated recursively.
- Drivers tested in panel regressions of the time-varying parameters include:
  - Financial innovation proxied by Log(M3/M2)
  - Domestic financial liberalization (DFL) index (1/2/3 for none/partial/full, source: Kaminsky and Schmukler (2008))
  - REER volatility, changes in equity prices, changes in housing prices, G7 dummy, time trend, banking crisis dummy (Laeven and Valencia, 2013).
- Data: quarterly panels for eight countries (Denmark, France, Japan, Korea, Mexico, Norway, Sweden, United States) covering 1970–2012 where available; China analysis uses available data (M2) from 1998:Q4 to 2012:Q3; Quandt-Andrews tests reported for 2002:Q1–2012:Q3.

### Results — cross-country evidence (high-level)
- Long-run cointegration:
  - For seven of the eight countries (all but Mexico), the Johansen cointegration test rejects a stable MDF over full sample periods.
  - Subsample (before vs. after liberalization) cointegration often holds, suggesting structural breaks associated with financial reforms (exceptions: Sweden and the United States).
  - Table excerpts (selected entries preserved as reported):
    - Denmark: Liberalization date 1981:Q1 — Full sample: No; Before Liberalization: Yes; After Liberalization: Yes.
    - France: Liberalization date 1985:Q1 — Full sample: No; Before Liberalization: Yes; After Liberalization: Yes.
    - Japan: Liberalization date 1991:Q4 — Full sample: No; Before Liberalization: No; After Liberalization: Yes.
    - Korea: Liberalization date 1988:Q1 — Full sample: No; Before Liberalization: No; After Liberalization: Yes.
    - Mexico: Liberalization date 1989:Q2 — Full sample: Yes; Before Liberalization: Yes; After Liberalization: No.
    - Norway: Liberalization date 1988:Q1 — Full sample: No; Before Liberalization: Yes; After Liberalization: Yes.
    - Sweden: Liberalization date 1985:Q1 — Before Liberalization: No; (full/after not available).
    - United States: Liberalization date 1982:Q1 — Full sample: No; Before Liberalization: No; After Liberalization: No.
- Time-varying short-run sensitivities (summary statistics from recursive estimates):
  - Income parameter (all sample mean): 0.5718; standard deviation 0.6102; minimum 0.0101; maximum 1.1134; Observation 506 (significant at 10%: 414).
  - Interest rate parameter (all sample mean): -0.0056; standard deviation 0.0055; minimum -0.0216; maximum 0.0084; Observation 506 (significant at 10%: 205).
- Panel regressions of time-varying parameters (baseline):
  - Innovation [Log(M3/M2)]: coefficient -1.0120*** on income parameter (standard error 0.0493); coefficient 0.00722* on interest rate parameter (standard error 0.00390).
  - Domestic financial liberalization (DFL): coefficient -0.0291** on income parameter (standard error 0.0143); coefficient 0.00336*** on interest rate parameter (standard error 0.000392).
  - Intercept values and R-squared reported:
    - Income parameter regression intercept 0.892*** (0.0215); Observations 414; R-squared 0.6350.
    - Interest rate parameter regression intercept 0.662*** (0.0312); Observations 205; R-squared 0.260.
  - Interpretation: financial innovation and liberalization reduce the sensitivity of money demand to income and generally make M2 less sensitive to interest rates (positive coefficient on interest rate parameter implies a move toward zero for a mostly negative parameter).
- Interaction effects and other covariates (augmented model):
  - Innovation and DFL interaction: positive and significant on income parameter; negative and significant on interest rate parameter — implying that simultaneous innovation and liberalization can increase MDF stability.
  - Housing price changes: associated with increased interest-rate sensitivity of money demand.
  - REER volatility and equity price changes: no statistically significant relationship with MDF parameters in the reported regressions.
  - Robustness checks including G7 dummy, time trend, and banking crisis dummy (Laeven and Valencia, 2013) preserve main findings: financial innovation and liberalization are key drivers of MDF instability.

### China-specific findings
- Data and timing:
  - China analysis uses M2 as PBC’s intermediate target; sample periods noted include 1998:Q4–2012:Q3 for broader discussion and Quandt-Andrews tests conducted on 2002:Q1–2012:Q3.
  - Velocity began a downward trend since 2008; nonbank financial activity and total social financing increased rapidly from 2008/2009 (Figures referenced in source).
- Structural break detection:
  - Chow test and Quandt-Andrews unknown breakpoint test identify 2009:Q1 as a breakpoint coinciding with the large countercyclical stimulus and rapid expansion of nonbank activity.
  - Table 6 (reported test statistics for 2002:Q1–2012:Q3):
    - Maximum LR F-statistic (2009:Q1) 189.14***
    - Maximum Wald F-statistic (2009:Q1) 189.14***
    - Exp. LR F-statistic 90.93***
    - Exp. Wald F-statistic 90.93***
    - Average LR F-statistic 30.50***
    - Average Wald F-statistic 30.50***
    - Note: Null hypothesis is no breakpoints within 5 percent of trimmed data. Number of breaks compared: 38. *** p<0.01.
- Cointegration and time-varying parameters:
  - Cointegration test on subsample 2002:Q1–2008:Q4 indicates a stable MDF; extending the sample to 2012:Q3 the cointegration relationship no longer holds, consistent with a structural shift around 2009:Q1.
  - Recursive expanding-window estimates using M2 show:
    - A significant decline in income sensitivity over time (income sensitivity notably lower in China than in other studied countries).
    - Interest rate sensitivity remains approximately the same but tends to be insignificant, implying money demand is not sensitive to benchmark rates.
  - Implication: M2’s usefulness as an intermediate target is weakening and regulated rates are questionable as effective policy instruments in the evolved financial environment.

### Policy implications and recommendations
- Evidence indicates that financial innovation and liberalization reduce the stability of the MDF and the sensitivity of M2 to income and interest rates.
- China-specific recommendation:
  - Shift toward greater reliance on market-based, price-oriented instruments (interest rates) as intermediate targets for monetary control as financial reforms and nonbank activity expand.
  - Ongoing interest-rate liberalization will enhance the effectiveness of indirect monetary policy and the usefulness of interest rates as policy signals, but a transition will require a period of learning-by-doing.
- General implication for central banks:
  - As financial systems liberalize and innovate, central banks should monitor broader aggregates and complementary indicators (including broader measures than M2) and consider strengthening interest-rate-based frameworks and market operations.

*Source: 6. China: Quandt-Andrews Unknown Breakpoint Test in the Money Demand Function .......18*

### REFERENCES

### _wp1475 - REFERENCES

### Money demand, liquidity, and monetary theory
- Alvarez, Fernando E. and Francesco Lippi, 2011, “Persistent Liquidity Effects and Long Run Money Demand,” NBER Working Paper No. 17566, pp. 1–39 (Cambridge, Massachusetts: National Bureau of Economic Research).
- Atta-Mensah, Joseph, 2004, “Money Demand and Economic Uncertainty,” Working Papers 04-25 (Ottawa, Ontario: Bank of Canada).
- Ball, Laurence, 2001, “Another Look at Long-Run Money Demand,” Journal of Monetary Economics, Vol. 47, No. 1, pp. 31–44.
- Beyer, Andreas, 2009, “A Stable Model for Euro Area Money Demand: Revisiting the Role of Wealth,” Working Paper Series No. 1111, pp. 1‒62 (Frankfurt: European Central Bank).
- Caporale, Guglielmo M. and Luis A. Gil-Alana, 2005, “Fractional Cointegration and Aggregate Money Demand Functions,” Vol. 73, No. 6, pp. 737–53 (Manchester: The Manchester School, University of Manchester).
- Choi, Woon Gyu and Seonghwan Oh, 2003, “A Money Demand Function with Output Uncertainty, Monetary Uncertainty, and Financial Innovations,” Journal of Money, Credit, and Banking, Vol. 35, No. 5, pp. 685–709.
- Fischer, Andreas M., 2007, “Measuring Income Elasticity for Swiss Money Demand: What Do the Cantons Say About Financial Innovation?” European Economic Review, Vol. 51, No.7, pp. 1641–60.
- Haug, Alfred. A., 1999, “Money Demand Functions: Data Span and Tests,” Conference Paper Presented at the New Zealand Econometric Study Group Meeting, Auckland July 1999. Available via the Internet at http://www.econ.canterbury.ac.nz/research/working_papers.shtml
- Ireland, Peter N, 2009, “On the Welfare Cost of Inflation and the Recent Behavior of Money Demand,” American Economic Review, Vol. 99, No. 3, pp. 1040–52.
- Laidler, David E., 1977, The Demand for Money: Theories and Evidence (New York: Harper and Row, 2nd ed.).
- Lieberman, Charles, 1977, “The Transactions Demand for Money and Technological Change,” Review of Economics and Statistics, Vol. 59, No. 3, pp. 307–17.
- Maki, Daiki and Shin-ichi Kitasaka, 2006, “The Equilibrium Relationship among Money, Income, Prices, and Interest Rates: Evidence from a Threshold Cointegration Test,” Applied Economics, Vol. 38, No. 13, pp. 1585–92.
- McPhail, Kim, 1991, “The Long-Run Demand for Money, Canada Savings Bonds and Treasury Bills in Canada.” Available via the Internet at http://www.esri.go.jp/en/archive/dis/discussion-e.html
- Miyao, Ryuzo, 1996, “Does a Cointegrating M2 Demand Relation Really Exist in Japan?” Journal of the Japanese and International Economies, Vol. 10, No. 2, pp. 169–80.
- Pradhan, Basanta K. and Arjunan Subramanian, 2003, “On the Stability of Demand for Money in a Developing Economy: Some Empirical Issues,” Journal of Development Economics, Vol. 72, No. 1, pp. 335–51.
- Sharma, Sunil and Neil R. Ericsson, 1998, “Broad Money Demand and Financial Liberalization in Greece,” Empirical Economics, Vol. 23, No. 3, pp. 417–36.

### Financial innovation, liberalization, and asset prices
- Arrau, Patricio, Jose De Gregorio, Carmen M. Reinhart, and Peter Wickham, 1991, “The Demand for Money in Developing Countries: Assessing the Role of Financial Innovation,” IMF Working Paper 91/45 (Washington: International Monetary Fund).
- _________, 1995, “The Demand for Money in Developing Countries: Assessing the Role of Financial Innovation,” Journal of Development Economics, Vol. 46, No. 2, pp. 317–40.
- Blundell-Wignall, Adrian, Frank Browne, and Paolo Manasse, 1990, “Monetary Policy in Liberalised Financial Markets,” OECD Economic Studies No. 15, pp. 146–74 (Paris: OECD).
- Kaminsky, Graciela L. and Sergio L. Schmukler, 2008, “Short-Run Pain, Long-Run Gain: Financial Liberalization and Stock Market Cycles,” Review of Finance, Vol. 12, No. 2, pp. 253–92.
- Bernanke, Ben S. and Mark Gertler, 2001, “Should Central Banks Respond to Movements in Asset Prices,” American Economic Review, Vol. 91, No. 2, pp. 253–57.
- Carstensen, Kai, 2006, “Stock Market Downswing and the Stability of European Monetary Union Money Demand,” Journal of Business and Economic Statistics, Vol. 24, pp. 395‒402.
- Dreger, Christian and Jürgen Wolters, 2010, “M3 Money Demand and Excess Liquidity in the Euro Area,” Public Choice, Vol. 144, No. 3, pp. 459–72.
- Boone, Laurence and Paul van den Noord, 2008, “Wealth Effects on Money Demand in the Euro Area,” Empirical Economics, Vol. 34, No. 3, pp. 525–36.
- Caporale, Guglielmo M. and Luis A. Gil-Alana, 2005, “Fractional Cointegration and Aggregate Money Demand Functions,” Vol. 73, No. 6, pp. 737–53 (Manchester: The Manchester School, University of Manchester).

### Banking sector, ownership, and China-specific studies
- Berger, Allen N., Iftekhar Hasan, and Mingming Zhou, 2007, “Bank Ownership and Efficiency in China: What Lies Ahead in the World’s Largest Nation?” Research Discussion Papers 16/2007 (Helsinki: Bank of Finland).
- Laurens, Bernard J. and Rodolfo Maino, 2007, “China: Strengthening Monetary Policy Implementation,” IMF Working Paper 07/14 (Washington: International Monetary Fund).
- Lin, Xiaochi and Yi Zhang, 2009, “Bank Ownership Reform and Bank Performance in China,” Journal of Banking and Finance, Vol. 33, Issue 1, pp. 20–29.
- N’Diaye, Papa, 2010, “Transforming China: Insights from the Japanese Experience of the 1980s,” IMF Working Paper 10/284 (Washington: International Monetary Fund).
- Zhang, Xiaoxi and Kevin Daly, 2011, “China’s Banks Ownership and Performance.” Available via the Internet at http://dx.doi.org/10.2139/ssrn.1914894

### Empirical methods, stability, and measurement issues
- Carstensen, Kai, 2006, “Stock Market Downswing and the Stability of European Monetary Union Money Demand,” Journal of Business and Economic Statistics, Vol. 24, pp. 395‒402.
- Caporale, Guglielmo M. and Luis A. Gil-Alana, 2005, “Fractional Cointegration and Aggregate Money Demand Functions,” Vol. 73, No. 6, pp. 737–53 (Manchester: The Manchester School, University of Manchester).
- Maki, Daiki and Shin-ichi Kitasaka, 2006, “The Equilibrium Relationship among Money, Income, Prices, and Interest Rates: Evidence from a Threshold Cointegration Test,” Applied Economics, Vol. 38, No. 13, pp. 1585–92.
- Haug, Alfred. A., 1999, “Money Demand Functions: Data Span and Tests,” Conference Paper Presented at the New Zealand Econometric Study Group Meeting, Auckland July 1999. Available via the Internet at http://www.econ.canterbury.ac.nz/research/working_papers.shtml

### Databases and systemic risk
- Laeven, Luc and Fabian Valencia, 2013, “Systemic Banking Crises Database,” IMF Economic Review, Vol. 61, No. 2, pp. 225–70.
- Guidotti, Pablo and Carlos Rodriguez, 1992, “Dollarization in Latin America: Gresham’s Law in Reverse?” IMF Staff Papers, Vol. 39, No. 3, pp. 518–44.

*Reference list as provided in the source document.*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2014/_wp1475.pdf_