## 7.  GFC exclusion

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---

### Introduction and motivation
- Research question: Do world cycles exist and how strong are they? Evidence points to world cycles driven by the US, especially US monetary policy, affecting both real and financial variables.
- Main data innovation: a new high-frequency (quarterly) macro-financial dataset for a large sample of advanced and emerging countries since 1950, assembled from IMF IFS paper archives and other official sources.
- Key methodological point: standard dynamic factor models are used to estimate world cycles and their contribution to domestic variance; results are robust to alternative factor extraction methods.

### Data, scope, variables, and compilation methods
- Variables constructed:
  - (i) GDP
  - (ii) Credit (domestic bank credit to the private non-financial sector, local currency)
  - (iii) Consumer Prices (CPI)
  - (iv) Stock Prices (main exchange index)
  - (v) Long-term Bond Yields (7 to 10 year government bonds)
- Temporal disaggregation: quarterly GDP series created using Chow-Lin (1971) methods based on annual GDP and historical quarterly Industrial Production (IFS line 67).
- Credit series: breaks documented in IFS volumes are chained where possible; follows BIS approach for break adjustments; uses IFS “claims on the private sector from domestic banks” (IFS line 32d).
- Single-source consistency: IFS paper volumes used to ensure consistent definitions across time and countries.
- Coverage (countries with “full coverage” going at least to 1957Q1; most extend to 1950Q1):
  - GDP: 37 countries (21 AEs and 16 EMs).
  - Credit: 45 countries (21 AEs – 24 EMs).
  - Prices (CPI): 50 countries (21 AEs – 29 EMs).
  - Stock prices: 27 countries (20 AEs and 7 EMs).
  - Bond yields: 17 countries (16 AEs and 1 EM).

### Empirical framework and estimation details
- Model: single-factor dynamic factor model in the spirit of Stock and Watson (1989, 1991):
  - Y_{i,t} = P_i F_t + u_{i,t}
  - F_t follows autoregressive dynamics (practically AR(1) used).
  - u_{i,t} also modelled with AR(1) errors.
- Estimation: Maximum Likelihood; variables computed in yearly growth rates except bond yields (computed in yearly absolute difference).
- Local projections (Jordà, 2005) for US shock identification:
  - ΔF_{t,t+h} = φ_h + Σ_{s=1}^l α_{s,h} ΔF_{t−s} + Σ_{s=1}^l β_{s,h} US_Shock_{t−s} + ε_{t,h}
  - l = 4; Newey and West (1987) standard errors used.
- Four US shocks analyzed:
  - (i) US monetary policy shocks (Coibion (2012); proxy: quarterly Fed discount rate 1950–1968).
  - (ii) US fiscal policy shocks (Romer and Romer, 2010).
  - (iii) US policy uncertainty shocks (changes in US Economic Policy Uncertainty Index).
  - (iv) US productivity shocks (utilization-adjusted US TFP series from Basu, Fernald and Kimball (2006) and Fernald (2014)).
- Robustness: results invariant to number of AR lags and alternative factor extraction approaches, including Bayesian methods and inclusion of regional factors.

### Key empirical findings (strength and evolution of world cycles)
- Existence and drivers:
  - World cycles (real and financial) exist and US shocks (monetary policy shocks, fiscal policy shocks, policy uncertainty shocks) drive real and financial world cycles.
  - The US is the main driver of global dynamics.
- Quantitative importance and sensitivity:
  - World business cycle impact on domestic output in normal times: around 15 percent for the median country.
  - World business cycle accounts for roughly 30 percent of domestic output fluctuations (median country, other specification).
  - World business cycle accounts for roughly 50 percent of inflation fluctuations (median country).
  - Global financial cycle synchronization (median country):
    - Stock and bond prices: around 50 percent synchronization.
    - Credit: around 10–15 percent synchronization.
  - Advanced economies drive most of these results; contributions of world cycles to the median emerging market economy have been small (around 10 percent across variables).
- Real vs financial cycles:
  - Output and credit cycles are strongly correlated: contemporaneous correlation is 0.87.
  - Correlations for other pairs of cycles range between 0.1 and 0.3.
  - Financial variables (domestic credit, equity prices, bond yields) do not always correlate; different financial global forces affect them.
  - Variations in asset prices (bond yields and stock prices) are generally more frequent than movements in quantities; credit cycles are more protracted than asset-price cycles.

### Evolution over time and effects of exceptional periods (GFC exclusion)
- Time variation and GFC exclusion effects:
  - Strength of world cycles drops significantly when “extreme” observations are excluded (e.g., oil shocks, the 2008/2009 financial crisis).
  - Output and credit synchronization:
    - World output and credit cycles were as “strong” during Bretton Woods (1950–1971) as during the Globalization period (1984–2006).
    - World cycles explain roughly 15 percent of the variance in domestic credit and output for the median country, and below 10 percent for the median emerging market in both periods.
    - After the Global Financial Crisis (GFC), output and credit synchronization has reverted to relatively low historical levels once 2008/2009 are excluded.
  - Price synchronization:
    - Synchronization in prices (assets and goods) has roughly doubled since Bretton Woods (from 25 percent to 50 percent for the median country) and has not decreased since the GFC.
  - Co-movement increases during “exceptional” periods, especially the GFC period (2007–2009), when co-movement in all variables was at record high.

### Determinants of (de)synchronization: trade, finance, and exchange rate regimes
- Panel regression setup (sample i = 1,...,36; t = 1,2,3,4; N = 144):
  - Dependent variable: share of variance of domestic output accounted for by the world business cycle in each sub-sample period.
  - Regressors: Trade integration (average exports+imports to GDP), Financial integration (average foreign assets+liabilities to GDP), FX flexibility (average fine classification 1–14).
  - Controls: country fixed effects and time dummies; standard errors clustered by country.
- Main regression coefficients (preserved exactly):
  - Trade Openness:
    - Column (1): 0.13**
    - Column (2): 0.19***
    - Column (3): 0.153***
  - Financial Openness:
    - Column (1): -.07**
    - Column (2): -.09**
    - Column (3): -.07**
  - FX Flexibility:
    - Column (1): 0.01
    - Column (2): 0.01
    - Column (3): 0.007
  - OIL:
    - Column (1): 0.08**
    - Column (2): 0.08**
    - Column (3): 0.119
  - GFC:
    - Column (1): 0.40***
    - Column (2): 0.07
    - Column (3): 0.371***
  - Interaction terms and other coefficients:
    - GFC x Financial Openness: 0.06**
    - GFC x Trade Openness: 0.001
    - Oil Shock x Financial Openness: 0.01
    - Oil Shock x Trade Openness: -.002*
  - Model statistics:
    - Country FE: Yes (all columns)
    - N: 144
    - R - squared:
      - Column (1): 0.557
      - Column (2): 0.569
      - Column (3): 0.530
- Interpretation:
  - Trade integration tends to increase co-movement with the rest of the world.
  - Financial integration has, on average, a negative impact on synchronization of domestic output to the world business cycle; however, during the GFC more financially connected countries co-moved more (positive interaction), although the net (average) effect remains negative.
  - FX flexibility does not have a detectable impact in these estimations.

### US shocks and their identified impacts (including Kilian oil shocks)
- US productivity shocks: expansionary for the world — expansions in output, asset prices, credit, and consumer prices globally; response peaks after 8 quarters.
- US policy uncertainty shocks: an unanticipated rise is quickly followed by drops in output, share prices and credit around the world.
- US monetary policy contractions (unexpected tightenings): followed by decline in world output and prices; world business cycle response is negative and significant after two years; real equity prices drop on impact; effects on world credit cycle and real bond yields more muted.
- US fiscal consolidations (increases in tax rates): negative effect on the world business cycle; muted effect on financial variables.
- Oil shocks (exogenous oil supply shocks per Kilian, 2008) are included in robustness checks and interact with financial openness in some specifications.

### Robustness checks and limitations
- Robustness:
  - Results robust to alternative model specifications (lags, extraction methods, inclusion of regional factors).
  - Re-estimation using 15-year rolling windows confirms main findings and shows:
    - A stark impact of the GFC on output and credit synchronization measures (sudden rise when 2007–2009 enters the sample).
    - A secular rise in synchronization in asset prices beginning in the early 1990s for all countries in the sample.
  - Post-GFC (2010–2016) is not dramatically different from Pre-GFC for output and credit synchronization once 2008/2009 are excluded, while price synchronization remains elevated.
- Limitations:
  - Bond yield coverage limited in EMs (only one EM, South Africa, covered throughout the period).
  - The dataset construction and methodological choices (e.g., temporal disaggregation) are described; authors plan to make dataset and compilation guide available.

### Policy implications and interpretation
- World cycles exist and are driven by the US, but their strength varies widely across variables and countries.
- World cycles disproportionately affect advanced economies and asset prices; asset price synchronization is a durable feature of the global financial system.
- Synchronization of output and credit at the world level has been relatively low and stable over the post-war period during normal times; the GFC is a unique period of unprecedented synchronization.
- Policy implications highlighted:
  - The coexistence of strong co-movement in asset prices but modest co-movement in key policy targets (output and credit) points to greater policy autonomy than expected, especially in emerging markets.
  - Local credit conditions for the private sector are not directly tied to the external environment in many countries; pass-through depends on domestic institutional and financial landscapes.
  - High net interest margins, financial repression, and dominance of domestic bank lending (median EM: more than 85 percent of total credit to the private sector goes through domestic banks; advanced countries: more than 75%) can dampen the impact of external interest rate changes on domestic private sector credit.
- The paper leaves comprehensive investigation of mechanisms behind these empirical facts for future research.

*Source: IMF working paper chapter titled "7.  GFC exclusion" from wpiea2019202-print-pdf.*

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

### wpiea2019202-print-pdf - References

### Tables
- 1. Determinants of World Business Cycle Synchronization ...........................................21

### Figures
- 1.  World Cycles .............................................................................................................13
- 2.  Local Projections .......................................................................................................16
- 3.  Strength – Full Sample...............................................................................................17
- 4.  Strength - Sub-Samples..............................................................................................18
- 5.  Output synchronization with the World - Then and Now..........................................19
- 6.  Rolling Windows .......................................................................................................23

*Source: wpiea2019202-print-pdf - References*

### 7.  GFC exclusion ......................................................................................................

### 7.  GFC exclusion

### Introduction and motivation
- Research question: Do world cycles exist and how strong are they? Evidence points to world cycles driven by the US, especially US monetary policy, affecting both real and financial variables.
- Main data innovation: a new high-frequency (quarterly) macro-financial dataset for a large sample of advanced and emerging countries since 1950, assembled from IMF IFS paper archives and other official sources.
- Key methodological point: standard dynamic factor models are used to estimate world cycles and their contribution to domestic variance; results are robust to alternative factor extraction methods.

### Major contributions of the paper
- Assembles the first quarterly dataset with such long and wide coverage for output, credit, inflation, stock prices, and long-term bond yields.
- Corrects coverage, frequency, and quality issues in existing macro-financial datasets (notably for GDP and credit), including break adjustments and temporal disaggregation.
- Provides long credit series for many countries, improving the assessment of global financial cycles.

### Data: scope, variables, and compilation methods
- Variables constructed: (i) GDP, (ii) Credit (domestic bank credit to the private non-financial sector, local currency), (iii) Consumer Prices (CPI), (iv) Stock Prices (main exchange index), (v) Long-term Bond Yields (7 to 10 year government bonds).
- Temporal disaggregation: quarterly GDP series created using Chow-Lin (1971) methods based on annual GDP and historical quarterly Industrial Production.
- Credit series: breaks documented in IFS volumes are chained where possible; follows BIS approach for break adjustments.
- Single-source consistency: IFS paper volumes used to ensure consistent definitions across time and countries.

### Coverage (quantitative)
- GDP: 37 countries (21 AEs and 16 EMs).
- Credit: 45 countries (21 AEs – 24 EMs).
- Prices (CPI): 50 countries (21 AEs – 29 EMs).
- Stock prices: 27 countries (20 AEs and 7 EMs).
- Bond yields: 17 countries (16 AEs and 1 EM).
- Coverage note: these numbers correspond to countries with “full coverage” (going at least to 1957Q1); most extend to 1950Q1.

### Empirical framework and estimation details
- Model: single-factor dynamic factor model in the spirit of Stock and Watson (1989, 1991):
  - Y_{i,t} = P_i F_t + u_{i,t}
  - F_t follows autoregressive dynamics (practically AR(1) used).
  - u_{i,t} also modelled with AR(1) errors.
- Estimation: Maximum Likelihood; variables computed in yearly growth rates except bond yields (computed in yearly absolute difference).
- Robustness: results invariant to number of AR lags and alternative factor extraction approaches, including Bayesian methods and inclusion of regional factors.

### Key empirical findings (strength and evolution of world cycles)
- Existence and drivers:
  - World cycles (real and financial) exist and US shocks (monetary policy shocks, fiscal policy shocks, policy uncertainty shocks) drive real and financial world cycles.
- Quantitative importance and sensitivity:
  - World business cycle impact on domestic output in normal times: around 15 percent for the median country.
  - Global financial cycle synchronization depends on variable:
    - Stock and bond prices: around 50 percent synchronization for the median country.
    - Credit: around 10–15 percent synchronization.
  - World cycles disproportionately affect advanced economies and asset prices rather than quantities.
- Time variation and GFC exclusion effects:
  - Strength of world cycles drops significantly when “extreme” observations are excluded (e.g., oil shocks, the 2008/2009 financial crisis).
  - Output and credit synchronization:
    - World output and credit cycles were as “strong” during Bretton Woods (1950–1971) as during the Globalization period (1984–2006).
    - World cycles explain roughly 15 percent of the variance in domestic credit and output for the median country, and below 10 percent for the median emerging market in both periods.
    - After the Global Financial Crisis (GFC), output and credit synchronization has reverted to relatively low historical levels.
  - Price synchronization:
    - Synchronization in prices (assets and goods) has roughly doubled since Bretton Woods (from 25 percent to 50 percent for the median country) and has not decreased since the GFC.
- Disentangling real vs financial cycles:
  - Financial variables (domestic credit, equity prices, bond yields) do not always correlate; different financial global forces affect them.
- Role of US: US is the main driver of global dynamics, confirming the central role of the US in global real and financial cycles.

### Drivers of (de)synchronization: trade, finance, and exchange rate regimes
- Trade integration: countries that increased trade integration more have synchronized their domestic output with the world business cycle.
- Financial integration: greater financial integration reduced output synchronization in the long run (de-synchronizing effect), except during the GFC when financially open countries experienced more output synchronization with the world.
- Exchange rate flexibility: has not affected the extent to which domestic economies react to global dynamics.
- Policy implication of these drivers: average level of output synchronization over the last 70 years is the net result of opposing forces—trade integration (synchronizing) and financial integration (de-synchronizing).

### Policy implications and interpretation
- Historical perspective: the Bretton Woods period was also affected by world cycles despite widespread capital controls and regulated financial systems.
- Policy autonomy: outside periods of global shocks, the modest impact of world cycles on domestic output and credit suggests more room for domestic policy autonomy, particularly in emerging markets.
- Financial integration nuance: a high level of financial integration does not necessarily imply stronger output co-movement; contagion can dominate during crises (e.g., GFC), biasing perceptions if attention focuses only on crisis episodes.
- Credit as a macro policy target: credit conditions for the private sector are not directly tied to external environment in normal times, providing perspective relative to existing literature emphasizing strong external spillovers.

### Robustness, limitations, and dataset availability
- Robustness: findings robust to alternative model specifications (lags, extraction methods, inclusion of regional factors).
- Limitations: bond yield coverage limited in EMs (only one EM, South Africa, covered throughout the period).
- Dataset note: authors will make the dataset, a compilation guide, and comparisons with other datasets available on authors’ websites.

*Source: IMF working paper chapter titled "7.  GFC exclusion" from wpiea2019202-print-pdf (dataset and analysis described in the chapter).*

### conclusions.

### conclusions.

### Globalization and synchronization
- The comparison of world synchronization focuses on periods before and after the kink in the “hockey-stick” of globalization, usually dated around 1985 (Jorda et al, 2017).
- The paper compares co-movement under “normal” macroeconomic fluctuations (i.e. without extreme shocks) between low integration (1951 to 1971) and deep integration (1985 to 2006).
- Isolating periods of global shocks allows testing whether the effect of trade or financial integration varies with the type of shocks hitting the world economy (real shocks in the 1970s or financial shock in 2008–2009).

### World Cycles (results)
- World cycles are estimated for: (i) Output, (ii) Credit, (iii) Stock Prices, (iv) Bond Yields, and (v) Prices (inflation). All variables are expressed as yearly growth rates except bond yields (yearly absolute changes) and are in real terms.
- Values are deviations from the (long run) sample mean and can take negative values for extended periods.
- Key empirical findings:
  - All factors are well estimated over the whole period; peaks and troughs align with major real and financial expansions (or crisis).
  - Output and credit cycles are strongly correlated: contemporaneous correlation is 0.87.
  - Correlations for other pairs of cycles range between 0.1 and 0.3.
  - Variations in asset prices (bond yields and stock prices) are generally more frequent than movements in quantities.
  - Credit cycles are much more protracted than asset prices cycles.
- Note on sub-periods:
  - Bretton Woods sample (1950-1971) is notable for steady growth and stable business cycle dynamics.
  - Globalization period (1984-2006) captures most of the Great Moderation; both periods are almost of equal length.

### The US and World Cycles (identification and empirical approach)
- To assess whether the US “drives” world cycles, the paper follows Miranda-Agrippino and Rey (2015) and uses externally identified US shocks to trace responses of estimated world cycles via Jorda’s local projection framework (Jorda, 2005).
- Empirical specification (local projections):
  - ΔF_{t,t+h} = φ_h + Σ_{s=1}^l α_{s,h} ΔF_{t−s} + Σ_{s=1}^l β_{s,h} US_Shock_{t−s} + ε_{t,h}
  - h denotes the horizon (quarter) of projection.
  - ΔF_{t,t+h} reports the cumulative change in the world factor between quarter t and t+h.
  - l = 4 (four lags used for all variables; results are not sensitive to the number of lags).
  - Standard errors corrected using Newey and West (1987) estimator because the error term follows a moving average process by construction.
- Four types of US shocks analyzed (data available at quarterly frequency since early 1950’s):
  - (i) US monetary policy shocks (taken from Coibion (2012)); note data starts in 1968 so quarterly changes in the Fed discount rate are used as a proxy between 1950 and 1968.
  - (ii) US fiscal policy shocks (Romer and Romer’s exogenous tax shocks, Romer and Romer, 2010).
  - (iii) US policy uncertainty shocks (changes in the US Economic Policy Uncertainty Index, Bloom and Davis (2016)).
  - (iv) US productivity shocks (changes in utilization-adjusted US TFP series from Basu, Fernald and Kimball (2006) and Fernald (2014)).
- Procedure:
  - First confirm the shocks imply a “textbook” response of US variables.
  - Then estimate how each shock individually affects estimated world cycles.
  - Recognize that many shocks occur simultaneously and alongside other global shocks (e.g., oil shocks); robustness checks include using all US shocks simultaneously.

*wpiea2019202-print-pdf - conclusions.*

### introduction of measure of exogenous oil supply shocks (Kilian, 2008)). key results

### Introduction of measure of exogenous oil supply shocks (Kilian, 2008) — key results

### US shocks: impacts on world real and financial cycles
- US productivity shocks are expansionary for the world: followed by expansions in output, asset prices, credit, and consumer prices globally; the response is gradual and peaks after 8 quarters.
- US policy uncertainty shocks: an unanticipated rise in US policy uncertainty is quickly followed by drops in output, share prices and credit around the world.
- US monetary policy contractions (unexpected tightenings) are followed by a decline in world output and prices; the world business cycle response is negative and significant after two years. Real equity prices drop on impact, while effects on the world credit cycle and real bond yields are more muted.
- US fiscal consolidations (increases in tax rates) have a negative effect on the world business cycle; their effect on financial variables is muted.
- Validation note: on the sample 1950-2015, US-specific variables (output, prices, credit etc.) show the expected responses to US shocks in the first-stage checks. Some alternative US fiscal shock measures (e.g., Ramey’s military news shocks) did not generate a positive response in US output using the post-war period and were therefore not used in the second stage.

### Strength and composition of world cycles
- World cycles account for a significant share of variance in domestic variables for the median country:
  - World business cycle accounts for roughly 30 percent of domestic output fluctuations.
  - World business cycle accounts for roughly 50 percent of inflation fluctuations.
- Synchronization is much higher in prices (assets and goods) than in quantities (output and credit).
- For the global financial cycle, synchronization is three to four times higher in asset prices (bond yields and stock prices) than in credit.
- Advanced economies drive these results; contributions of world cycles to the median emerging market economy have been small (around 10 percent across variables).
- Implication: world cycles disproportionately affect advanced economies and asset prices.

### Evolution over time and exceptional periods
- Co-movement increases during “exceptional” periods of global real and financial shocks, especially the GFC period (2007–2009), during which co-movement in all variables was at record high.
- Once exceptional periods are isolated, average co-movement is modest, especially for output and credit (between 10 and 20 percent).
- World synchronization has not increased uniformly:
  - Synchronization in asset prices has been on a secular increase since Bretton Woods.
  - Median impact of world cycles on domestic output or credit has not changed materially between Bretton Woods and the Globalization period.
- The GFC years represent an outlier when it comes to credit and output synchronization; excluding 2008/2009 shows that output and credit synchronization measures have almost reverted to their pre-crisis level, while synchronization in prices remains high.

### Cross-country patterns and changing participants
- Many countries display little change in how their output co-moves with the world over the last 70 years:
  - Examples of consistent high synchronization: Netherlands, Finland, Belgium.
  - Examples of consistent low synchronization: Norway, Denmark, and most emerging markets.
  - Examples of decreased synchronization since Bretton Woods: Uruguay, Japan, New-Zealand.
  - Examples of re-synchronization with the world: France, Italy, Spain, and to a smaller extent the US.
- The identity of countries that co-move with the world has shifted even if median co-movement remained similar.

### Determinants of world business cycle synchronization (panel regression results)
- Estimation setup:
  - Dependent variable: share of variance of domestic output accounted for by the world business cycle in each of four periods (period 1 = Bretton Woods; period 2 = oil shock; period 3 = Globalization; period 4 = GFC).
  - Regressors: Trade integration (average exports+imports to GDP per sub-sample), Financial integration (average foreign assets+liabilities to GDP per sub-sample), FX flexibility (average fine classification 1–14 per sub-period).
  - Controls: country fixed effects (αi) and time dummies (dt). Sample: i = 1,...,36; t = 1,2,3,4.
  - Standard errors clustered by country; number of observations N = 144.
- Main empirical findings (Table 1 coefficients preserved exactly):
  - Trade Openness:
    - Column (1): 0.13**
    - Column (2): 0.19***
    - Column (3): 0.153***
  - Financial Openness:
    - Column (1): -.07**
    - Column (2): -.09**
    - Column (3): -.07**
  - FX Flexibility:
    - Column (1): 0.01
    - Column (2): 0.01
    - Column (3): 0.007
  - OIL:
    - Column (1): 0.08**
    - Column (2): 0.08**
    - Column (3): 0.119
  - GFC:
    - Column (1): 0.40***
    - Column (2): 0.07
    - Column (3): 0.371***
  - Interaction terms and other reported coefficients:
    - GFC x Financial Openness: 0.06**
    - GFC x Trade Openness: 0.001
    - Oil Shock x Financial Openness: 0.01
    - Oil Shock x Trade Openness: -.002*
  - Model statistics:
    - Country FE: Yes (all columns)
    - N: 144
    - R - squared:
      - Column (1): 0.557
      - Column (2): 0.569
      - Column (3): 0.530
- Interpretation:
  - Trade integration tends to increase co-movement with the rest of the world.
  - Financial integration has, on average, a negative impact on synchronization of domestic output to the world business cycle; however, there is an asymmetric effect during the GFC: more financially connected countries co-move more during financial crisis (positive interaction), although the net (average) effect remains negative.
  - FX flexibility does not have a detectable impact on domestic output’s connection to the world cycle in these estimations.

### Robustness checks
- Re-estimation using 15-year rolling windows (central year notation: e.g., year 2000 refers to 1992–2008) yields key findings that do not depend on specific windows.
- The stability in the strength of world cycles for output and credit is not driven by very low values in the early 1980s.
- Rolling-window evidence confirms:
  - A stark impact of the GFC on output and credit synchronization measures (sudden rise when 2007–2009 enters the sample).
  - A secular rise in synchronization in asset prices beginning in the early 1990s for all countries in the sample.
- Post-GFC (2010–2016) is not dramatically different from Pre-GFC for output and credit synchronization once 2008/2009 are excluded, while price synchronization remains elevated.

### Conclusions and policy-relevant implications
- World cycles exist and are driven by the US, but their strength varies widely across variables and countries.
- World cycles (real and financial) affect mostly advanced economies and asset prices; asset price synchronization is a durable feature of the global financial system.
- Synchronization of output and credit at the world level has been relatively low and stable over the post-war period during normal times; the GFC is a unique period of unprecedented synchronization.
- Policy implications highlighted in the analysis:
  - The coexistence of strong co-movement in asset prices but modest co-movement in key policy targets (output and credit) points to greater policy autonomy than expected, especially in emerging markets.
  - Local credit conditions for the private sector are not directly tied to the external environment (or US conditions) in many countries; the pass-through from external conditions to local credit depends on domestic institutional and financial landscapes.
  - High net interest margins, financial repression, and the dominance of domestic bank lending (more than 85 percent of total credit to the private sector goes through domestic banks in the median EM; more than 75% in advanced countries, per BIS statistics in the sample) can dampen the impact of external interest rate changes on domestic private sector credit.
- The paper leaves comprehensive investigation of mechanisms behind these empirical facts for future research.

*Italic: Source — wpiea2019202-print-pdf (introduction of measure of exogenous oil supply shocks (Kilian, 2008)).*

### References

### References and Appendix I – Data

### References (selected entries from the source)
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- Auer, R. A., A. Levchenko, and P. Sauré, 2017. “International Inflation Spillovers through Input Linkages.” NBER Working Paper 23246, National Bureau of Economic Research, Cambridge, MA.
- Barakchian, S. Mahdi, and C. Crowe, 2013. “Monetary policy matters: Evidence from new shocks data” Journal of Monetary Economics 60.8 : 950–966.
- Basu, S., Fernald, J.G., Kimball, M.S., 2006. “Are technology improvements contractionary?” American Economic Review. 96, 1418–1448.
- Bernanke, Ben S., and I. Mihov, 1998. “Measuring monetary policy.” The Quarterly Journal of Economics 113.3 (1998): 869–902.
- Bloom, Nicholas, 2014. “Fluctuations in Uncertainty”, Journal of Economic Perspectives, 28, 153–176
- Bräuning, Falk, and V. Ivashina, (forthcoming) "U.S. Monetary Policy and Emerging Market Credit Cycles." Journal of Monetary Economics.
- Calvo, Guillermo A., and C. M. Reinhart, 2002. “Fear of floating.” The Quarterly Journal of Economics 117.2: 379–408.
- Carriere-Swallow, Yan and L. Cespedes, 2013. “The Impact of Uncertainty Shocks in Emerging Economies”, Journal of International Economics, 90, 316–325
- Cerutti, Eugenio, S. Claessens, and A. K. Rose, 2017. “How Important is the Global Financial Cycle? Evidence from Capital Flows,” IMF Working Paper No. 17/193, International Monetary Fund, Washington, DC.
- Cesa-Bianchi, Ambrogio, 2013. “Housing Cycles and Macroeconomic Fluctuations: A Global Perspective” Journal of International Money and Finance, Vol. 37, pp. 215–238.
- Cesa-Bianchi, Ambrogio, L. Cespedes, and A. Rebucci, 2015. “Global Liquidity, House Prices, and the Macroeconomy: Evidence from Advanced and Emerging Economies,” Journal of Money, Credit and Banking, Vol. 47: S1, pp. 301–335.
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### Appendix I – Data construction and methods
- Variables constructed at quarterly frequency for a large cross section of countries:
  - (i) GDP
  - (ii) Credit
  - (iii) Prices
  - (iv) Stock Prices
  - (v) Long-term Bond Yields
- General procedure:
  - Collect the whole universe of official statistics provided by local authorities for each variable.
  - Use the IFS archives to extend all series into the past after making sure definitions match (note 42: check that definitions match on paper and that IFS statistics and Official Statistics match de facto when both are available).
- Prices:
  - Consumer prices reconstructed using the “cost of living” index (line 66 in IFS).
- Stock prices:
  - Based on the “share price index” (line 61 or above in IFS).
  - If unavailable, use the “Industrial share price” as a proxy for the overall index.
- Bond yields:
  - Average yields to maturity on (central) government bonds issues with lives of least 7 years (line 62 in IFS).
- Credit:
  - Use IFS “claims on the private sector from domestic banks” (IFS line 32d) as definition of domestic credit.
  - Equivalent to “Bank credit to the Private Non-Financial Sector” assembled by the BIS; excludes foreign credit and credit from other institutional sectors.
  - Breaks in credit series are often well-documented; when both old and new definitions are reported in different IFS vintages for a couple quarters, series can be chained to create long series without breaks.
  - Resulting series are very close to the BIS long credit dataset (Dembiermont, 2013).
- Gross Domestic Product (GDP):
  - Long quarterly GDP series are often unavailable; official quarterly GDP series typically start in mid 80’s for a few countries and early 90’s for most.
  - Use temporal disaggregation (Chow-Lin, 1971) to create synthetic quarterly GDP series based on:
    - (i) annual GDP series from Penn World Tables
    - (ii) quarterly Industrial Production data from historical IFS volumes (IFS line 67)
  - Temporal disaggregation allocates annual GDP into quarters using the quarterly IP as a guide while ensuring the sum of quarters matches the annual GDP.
  - The method leverages Industrial Production accuracy at high frequency and imposes annual GDP constraints; widely used to create long GDP statistics.
  - Validation: synthetic GDP growth rates correlate extremely well with official quarterly data (examples: US (BEA), France (INSEE)) and with OECD data (examples: Japan, Mexico); historical IP from IFS helps eliminate GDP series based on simple linear interpolations.
- Validation and alignment:
  - For stock price growth, bond yields, and inflation, the IFS-based quarterly series move in tandem with sources reporting the same data at annual frequency (examples: Norway, Denmark, Spain, Italy).
  - The 35 years added using quarterly data (between 1950 and 1985) align well with annual data.

### Final coverage (as reported in the source table)
- CountryGDPCredit Stock PricesBond YieldsPrices
- Argentina1957q11950 Q21950 Q2
- Australia1957q11950 Q11950 Q11955 Q11950 Q1
- Austria1950q11950 Q11950 Q11950 Q1
- Belgium1950q11950 Q41951 Q11957 Q11950 Q1
- Bolivia1950 Q41950 Q4
- Brazil1957q11950 Q41950 Q4
- Canada1950q11950 Q11950 Q11951 Q11950 Q1
- Chile1950q11950 Q41953 Q11950 Q1
- Colombia1952 Q41952 Q4
- Costa Rica1950 Q41950 Q4
- Cyprus1958 Q11957 Q1
- Denmark1950q11950 Q11950 Q11955 Q11950 Q1
- El Salvador1957 Q1
- Finland1950q11950 Q41951 Q11950 Q1
- France1950q11950 Q11950 Q11955 Q11950 Q1
- Germany1950q11950 Q11953 Q11957 Q11950 Q1
- Greece1950q21953 Q41950 Q1
- Guatemala1954 Q11954 Q1
- Honduras1950 Q41950 Q4
- Iceland1957q21955 Q11955 Q1
- India1950q11950 Q11950 Q11950 Q1
- Ireland1950q11950 Q11955 Q11957 Q11950 Q1
- Israel1957q11951 Q41955 Q11951 Q4
- Italy1950q11950 Q11950 Q11955 Q11950 Q1
- Japan1950q11950 Q11950 Q11950 Q1
- Korea1957q11951 Q41950 Q1
- Luxembourg1950q11957 Q1
- Malaysia1952 Q41980 Q11950 Q1
- Malta1957 Q1
- Mexico1950q11950 Q11950 Q11950 Q1
- Morocco1957q11959 Q11957 Q1
- Netherlands1950q11950 Q11950 Q11955 Q11950 Q1
- New Zealand1957q11950 Q11950 Q11957 Q11950 Q1
- Norway1950q11950 Q11950 Q11957 Q11950 Q1
- Pakistan1950q11950 Q41950 Q1
- Peru1950 Q41950 Q41950 Q4
- Philippines1963q11950 Q41953 Q11950 Q4
- Portugal1955q11950 Q11955 Q11950 Q1
- South Africa1957q11950 Q41950 Q11955 Q11950 Q1
- Spain1950q11953 Q41953 Q41950 Q1
- Sweden1950q11950 Q11950 Q11955 Q11950 Q1
- Switzerland1955q11950 Q11950 Q11955 Q11950 Q1
- Taiwan1957q11957 Q11957 Q1
- Thailand1950 Q41950 Q1
- Turkey1957q11950 Q41950 Q4
- United Kingdom1950q11950 Q11950 Q11955 Q11950 Q1
- United States1950q11950 Q11950 Q11953 Q21950 Q1
- Uruguay1957q11950 Q41950 Q4

*Source: wpiea2019202-print-pdf - References*

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_Source: https://www.imf.org/-/media/files/publications/wp/2019/wpiea2019202-print-pdf.pdf_