## _wp16102

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---

### Research objective and scope
- Assess transmission of business cycles and financial conditions from advanced and emerging market economies to Low-Income and Developing Countries (LIDCs).
- Key questions:
  - Are LIDCs more or less exposed to the international business cycle than emerging markets?
  - Which are the strong drivers of LIDCs business cycles, advanced, or emerging markets?
  - How are LIDCs exposed to the international financial cycle and credit contractions in various parts of the world?
- Model and data scope:
  - Estimate a Global Vector Autoregression model (GVAR).
  - Expand sample coverage to 24 LIDC countries.
  - Use non-linear interpolation methodology to obtain quarterly time series from annual data and available related higher frequency data, following Fernandes (1981).

### LIDC group definition and descriptive statistics
- The LIDC group includes all countries that a) fall below a modest per capita income threshold (US$2,500 in 2011, based on Gross National Income); and b) are not conventionally viewed as emerging market economies (EMs).
- There are 60 countries in this group, accounting for about one-fifth of the world’s population.
- Sub-Saharan Africa (SSA) accounts for some 57 percent of the LIDC population, with a further 28 percent living in Asia.
- Countries range in size from oil-rich Nigeria (174 million) to fisheries dependent Kiribati (0.1 million), and in 2013 per capita GDP terms from Mongolia (US$3,770) to Malawi (US$270).
- The 10 largest economies in the group account for two-thirds of total group output (as measured in PPP terms).
- LIDCs rely largely on foreign demand, with exports comprising between 30–40 percent of their GDP.

### Dataset scope, variables, and temporal disaggregation
- Sample coverage and key aggregates:
  - Initial dataset covers 40 countries: 24 LIDCs and 16 G20 countries.
  - Those countries account for 74 percent of world GDP.
  - The LIDCs in the sample account for 62 percent of LIDCs GDP.
  - Three G20 countries that joined the Euro Area on January 1st, 1999 (France, Germany, and Italy) are grouped together using the average 2006–2008 US real GDP; final number of countries considered is 38.
  - Quarterly frequency for period 1990Q1–2013Q4, except LIDCs’ GDP available only annually.
- Variables included (transformed into logs; seasonally adjusted where necessary):
  - Country-specific domestic variables: it y, it ex, it reer, it crd.
  - Country-specific foreign variables: weighted averages of counterpart country variables (trade-weighted).
  - Global variables: oil price (t oil), global credit (t loan), VIX index (t vix).
- Definitions / data sources and construction notes:
  - it GDP: nominal GDP in national currency; it CPI: consumer price index.
  - it EXP: nominal exports to the world in US dollars; exports deflated by US CPI.
  - Domestic private credit: "Claims on private sector from deposit money banks," IMF IFS series 22d.
  - Global credit: "External positions of banks: Claims on all sectors," BIS locational banking statistics, sum of claims on all sectors in counterparty locations of all BIS reporting banks.
  - VIX: derived from S&P 500 options; proxy for U.S. equity market volatility.
  - Final data sources: IMF DTTS, WEO, IFS, IFTS, BIS, Bloomberg (oil and VIX).
- Temporal disaggregation of annual LIDCs’ GDP:
  - Method: Fernandez (1981) temporal disaggregation (regression-based interpolation) using a high-frequency indicator series.
  - High-frequency benchmark chosen: "imports to the world" (IMF DTTS) — chosen for availability across LIDCs and high correlation with GDP (example: correlation between US GDP and imports for 1990Q1–2013Q3 is 0.984).
  - Validation exercise (Hodrick-Prescott filtered with λ=1600) — correlations between actual quarterly GDP and interpolated series (Fernandez (1981) vs linear interpolation):
    - USA: Imports (benchmark) 0.938; Ind.Prd. (benchmark) 0.940; Linear interp. 0.864
    - Germany: Imports 0.927; Ind.Prd. 0.951; Linear interp. 0.587
    - Japan: Imports 0.914; Ind.Prd. 0.929; Linear interp. 0.654
    - Mexico: Imports 0.850; Ind.Prd. 0.461; Linear interp. 0.834
    - UK: Imports 0.817; Ind.Prd. 0.915; Linear interp. 0.750
    - Canada: Imports 0.934; Ind.Prd. 0.903; Linear interp. 0.694

### Methodology: GVAR specification and estimation
- GVAR framework:
  - Combine country-specific VARX(p i, q i) models with country-specific foreign variables constructed as trade-weighted averages.
  - Trade weights ωij = (sum of exports and imports of country i with country j) / (country i's total trade), averaged over 2008–2013.
  - GVAR assumption: countries behave as small open economies (no long-run feedback from domestic to foreign country-specific variables); weak exogeneity tested.
- Country-specific model features:
  - Four domestic variables per country: it y, it ex, it reer, it crd.
  - Three global variables: t oil, t loan, t vix.
  - Series seasonally adjusted where necessary (TRAMO/SEATS), deflated by CPIs, transformed into logs.
  - US treated differently: US VARX allows feedback from US macro variables to global variables (oil, global credit, vix); only country excluded from small-open-economy assumption.
- Unit root, cointegration, and lag selection:
  - Variables assumed I(1); tested with ADF and WS (Park and Fuller, 1995) using AIC for lag selection — tests support I(1) assumption in general.
  - Lag orders p i and q i selected by AIC; maximum lag allowed is two due to data limitations.
  - Cointegration rank for each country model computed using Johansen trace and maximum eigenvalue statistics.
- Weak exogeneity test summary (Table 4 F tests; significance denoted by “**” at 5 percent):
  - US F(4, 70): *it y 2.80**; *it ex 1.45; *it reer 0.42; *it crd 0.08; t oil - ; t loans - ; t vix -
  - EA F(3, 74): *it y 0.39; *it ex 1.44; *it reer 0.74; *it crd 0.49; t oil 0.85; t loans 2.18; t vix 1.07
  - UK F(3, 74): *it y 2.26; *it ex 1.17; *it reer 0.30; *it crd 0.79; t oil 1.07; t loans 1.07; t vix 1.31
  - China F(1, 76): *it y 0.19; *it ex 0.57; *it reer 0.53; *it crd 0.02; t oil 0.36; t loans 0.12; t vix 0.25
  - Implication: weak exogeneity assumption fails only for output in the US model (evidence of long run feedback), so foreign output variable excluded from US model.
- Estimation and inference:
  - Use Generalized Impulse Response Functions (GIRF) to measure shock effects (ordering-invariant).
  - Focus reported GIRFs on transmission of shocks arising in US, advanced economies excluding US (AE), and emerging market economies (EM) to LIDCs.
  - Shock experiments: one standard deviation negative shock to output and to private credit.
  - Country groupings: AE = Canada, Euro Area (France, Germany, Italy), Japan, UK; EM = Brazil, China, India; remaining countries in "other countries."
  - LIDC subgroup classifications: oil-exporting vs. oil-importing; commodity-exporting vs. importing; frontier economies vs. remaining LIDCs.

### Key empirical findings and impulse responses
- Main empirical findings (verbatim):
  - Oil exporting countries have rather more attuned business cycles to advanced economies or emerging markets, compared to oil importers.
  - Frontier LIDCs, despite presenting lower trade integration, are also exposed to international business cycles.
  - Credit contractions in the US have significant impacts on exports and output of frontier LIDCs, but no such effect is found for credit conditions in the other regions of the world.
- Output shocks (one standard error negative shock to source-region output):
  - Negative shock to EM output:
    - Oil-exporting LIDCs exhibit a significant negative response of real GDP.
    - Oil-importing LIDCs show insignificant response.
  - Negative shock to AE output:
    - Oil-exporting LIDCs show a brief and smaller negative impact on output.
    - Oil-importing LIDCs show insignificant response.
  - Shock to US output:
    - Output in both oil-exporting and oil-importing LIDCs fails to respond significantly to shocks to US output.
  - Interpretation:
    - EMs have become more important trade partners for LIDCs; EMs have higher income elasticity of demand for oil, explaining stronger synchronicity with oil-exporting LIDCs.
- Export responses:
  - Negative shock to US output:
    - Exports of oil-exporting LIDCs respond significantly (after three quarters).
    - Exports of oil-importing LIDCs show insignificant response.
  - Export-to-GDP ratios:
    - oil-exporting LIDCs ≈ 38 percent
    - oil-importing LIDCs ≈ 33 percent
    - commodity-exporting LIDCs ≈ 38 percent
    - frontier LIDCs ≈ 31 percent
  - Frontier LIDCs show significant responses in output and exports to global output shocks due to stronger financial integration despite lower trade integration.
- Credit shocks (one standard error negative shock to private credit):
  - Negative shock to private credit in EM and AE:
    - Do not have a significant effect on LIDCs’ output and exports.
  - Negative shock to US private credit:
    - Significant negative effect on output of oil-exporting LIDCs.
    - Exports in both oil-exporting and oil-importing LIDCs react significantly to US credit shocks; responses more pronounced for oil-exporting LIDCs.
    - Frontier LIDCs are particularly hard hit by US credit contractions.
  - Puzzles / interpretation:
    - US credit shocks have stronger spillovers to LIDCs than European credit shocks despite larger European cross-border lending exposure to some LIDCs (e.g., sub-Saharan Africa). Possible explanations offered in the source:
      - European lending ties may reflect longer-term/colonial relations that dampen short-run transmission.
      - US credit shocks may be amplified by responses of other AEs; AE credit shocks might be offset by US monetary policy.
- Presentation of results:
  - GIRFs reported as bootstrap mean with 90 percent error bands; consolidated candlestick charts present responses after three quarters for GDP and exports to US private credit shocks.

### Model diagnostics and country-level details
- Maximum lag order allowed in GVAR due to data limitations: two.
- Example country VARX orders and cointegration relationships (Johansen trace at 95 percent critical value):
  - Argentina: p i 2; q i 1; #Coint. 2
  - Australia: p i 2; q i 1; #Coint. 2
  - Bangladesh: p i 2; q i 1; #Coint. 1
  - Bolivia: p i 1; q i 1; #Coint. 3
  - Burundi: p i 2; q i 1; #Coint. 0
  - Euro Area: p i 2; q i 1; #Coint. 3
  - India: p i 2; q i 1; #Coint. 2
  - Japan: p i 1; q i 1; #Coint. 1
  - USA: p i 2; q i 1; #Coint. 4
- Unit root tests: ADF and WS (Park and Fuller, 1995) — results support variables are generally I(1).
- Weak exogeneity tests summarized above.

### Main conclusions and policy implications (Section VI)
- Main empirical conclusions:
  - Different LIDC subgroups are exposed to shock transmission from the US, other advanced economies, and emerging markets along various dimensions.
  - Oil- and commodity-exporting countries and frontier markets exhibit greater business cycle synchronicity with emerging market economies.
  - US credit conditions have a notable effect on LIDCs’ output and exports, particularly for oil exporters, commodity exporters, and frontier markets; credit conditions in other parts of the world appear less significant in the estimates.
- Two highlighted policy implications:
  - Diversification of export components and export partners could help stabilize output fluctuations induced by business cycles in selected, fast-growing emerging market economies.
  - Broadening the spectrum of external financing channels could reduce exposure to US credit supply shocks.

*Source: _wp16102 - Section V outlines and discusses the estimation results; and Section VI concludes.*

### 1. Response of GDP to Negative Shock to EM’s Output ........................................................16

### 1. Response of GDP to Negative Shock to EM’s Output

### Research objective and scope
- Assess transmission of business cycles and financial conditions from advanced and emerging market economies to Low-Income and Developing Countries (LIDCs).
- Key questions:
  - Are LIDCs more or less exposed to the international business cycle than emerging markets?
  - Which are the strong drivers of LIDCs business cycles, advanced, or emerging markets?
  - How are LIDCs exposed to the international financial cycle and credit contractions in various parts of the world?
- Model and data scope:
  - Estimate a Global Vector Autoregression model (GVAR).
  - Expand sample coverage to 24 LIDC countries.
  - Use non-linear interpolation methodology to obtain quarterly time series from annual data and available related higher frequency data, following Fernandes (1981).

### LIDC group definition and descriptive statistics (preserve source wording)
- The LIDC group includes all countries that a) fall below a modest per capita income threshold (US$2,500 in 2011, based on Gross National Income); and b) are not conventionally viewed as emerging market economies (EMs).
- There are 60 countries in this group, accounting for about one-fifth of the world’s population.
- Sub-Saharan Africa (SSA) accounts for some 57 percent of the LIDC population, with a further 28 percent living in Asia.
- Countries range in size from oil-rich Nigeria (174 million) to fisheries dependent Kiribati (0.1 million), and in 2013 per capita GDP terms from Mongolia (US$3,770) to Malawi (US$270).
- The 10 largest economies in the group account for two-thirds of total group output (as measured in PPP terms).
- LIDCs rely largely on foreign demand, with exports comprising between 30–40 percent of their GDP.

### Methodology
- GVAR methodology: combines country-specific models into a global model by relating domestic variables to country specific foreign variables in a consistent manner.
- Interpolation: non-linear interpolation methodology following Fernandes (1981) to expand quarterly coverage where official quarterly data are limited.

### Main empirical findings (verbatim)
- Oil exporting countries have rather more attuned business cycles to advanced economies or emerging markets, compared to oil importers.
- Frontier LIDCs, despite presenting lower trade integration, are also exposed to international business cycles.
- Credit contractions in the US have significant impacts on exports and output of frontier LIDCs, but no such effect is found for credit conditions in the other regions of the world.

### Implications for policymakers (inferred from findings and framing in source)
- Monitor exposure channels to advanced and emerging market cycles, especially for oil exporters and frontier LIDCs.
- Consider diversification of trade partner structures to reduce vulnerability to external demand shocks.
- Recognize that US credit contractions can materially affect frontier LIDCs’ exports and output even if other regions’ credit conditions do not show similar spillovers.
- Where quarterly data are sparse, apply interpolation methods (e.g., Fernandes (1981)) with caution and transparency to support timely policy analysis.

*Source: IMF working paper excerpt (sections and figures listing; introduction and main results as provided).*

### Section V outlines and discusses the estimation results; and Section VI concludes.

### _wp16102 - Section V outlines and discusses the estimation results; and Section VI concludes.

### Complement to existing literature
- Builds on Canales-Kriljenko, Hosseinkouchack, and Meyer-Cirkel (2014) by:
  - Examining export demand elasticities in more detail, explicitly incorporating the real effective exchange rate into the system of equations.
  - Improving underlying data quality by interpolating GDP using higher-frequency indicators.
  - Expanding and segmenting the panel of countries to identify impacts by economic-typology.
- Key comparative distinctions with Samake and Yang (2014):
  - Broader set of global shock sources: advanced economies, emerging markets, BRICS, and other countries.
  - Inclusion of the credit channel as a transmission conduit.
  - Use of quarterly data (rather than annual).
- Other literature summarized:
  - Berg et al. (2011): global financial crisis (2007–09) growth declines in LICs explained largely by decline in export demand.
  - Espinoza, Jahan, and Dabla-Norris (2012): VAR and dynamic panel evidence that LICs’ economic activity is significantly affected by foreign shocks; regional stratification matters.
  - Raddatz (2007): panel VAR finds external factors explain only a small fraction of LIC output volatility; internal factors dominate.
  - Mishra, Montiel, and Spilimbergo (2010): LICs exhibit impaired traditional monetary transmission channels; exchange rate channel undermined by central bank behavior.

### Dataset: scope, variables, and construction
- Sample coverage and key aggregates:
  - Initial dataset covers 40 countries: 24 LIDCs and 16 G20 countries.
  - Those countries account for 74 percent of world GDP.
  - The LIDCs in the sample account for 62 percent of LIDCs GDP.
  - Three G20 countries that joined the Euro Area on January 1st, 1999 (France, Germany, and Italy) are grouped together using the average 2006–2008 US real GDP; final number of countries considered is 38.
  - Quarterly frequency for period 1990Q1–2013Q4, except LIDCs’ GDP available only annually.
- Variables included (transformed into logs; seasonally adjusted where necessary):
  - Country-specific domestic variables: real output (it y), real exports (it ex), real effective exchange rate (it reer), domestic private credit (it crd).
  - Country-specific foreign variables: weighted averages of counterpart country variables (trade-weighted).
  - Global variables: oil price (t oil), global credit (t loan), VIX index (t vix).
- Definitions / data sources and construction notes:
  - it GDP: nominal GDP in national currency; it CPI: consumer price index.
  - it EXP: nominal exports to the world in US dollars; exports deflated by US CPI.
  - Domestic private credit: "Claims on private sector from deposit money banks," IMF IFS series 22d.
  - Global credit: "External positions of banks: Claims on all sectors," BIS locational banking statistics, sum of claims on all sectors in counterparty locations of all BIS reporting banks.
  - VIX: derived from S&P 500 options; proxy for U.S. equity market volatility.
  - Final data sources: IMF DTTS, WEO, IFS, IFTS, BIS, Bloomberg (oil and VIX).
- Temporal disaggregation of annual LIDCs’ GDP:
  - Method: Fernandez (1981) temporal disaggregation (regression-based interpolation) using a high-frequency indicator series.
  - High-frequency benchmark chosen: "imports to the world" (IMF DTTS) — chosen for availability across LIDCs and high correlation with GDP (example: correlation between US GDP and imports for 1990Q1–2013Q3 is 0.984).
  - Validation exercise: correlation between actual quarterly GDP and interpolated series (Hodrick-Prescott filtered with λ=1600) shows Fernandez (1981) interpolation (both imports and industrial production benchmarks) outperforms linear interpolation for all tested countries. Table 2 correlations:
    - USA: Imports (benchmark) 0.938; Ind.Prd. (benchmark) 0.940; Linear interp. 0.864
    - Germany: Imports 0.927; Ind.Prd. 0.951; Linear interp. 0.587
    - Japan: Imports 0.914; Ind.Prd. 0.929; Linear interp. 0.654
    - Mexico: Imports 0.850; Ind.Prd. 0.461; Linear interp. 0.834
    - UK: Imports 0.817; Ind.Prd. 0.915; Linear interp. 0.750
    - Canada: Imports 0.934; Ind.Prd. 0.903; Linear interp. 0.694

### Methodology: GVAR specification and estimation
- Model framework:
  - Global Vector AutoRegressive (GVAR) model combining individual country VARX(p i, q i) models with country-specific foreign variables constructed as trade-weighted averages.
  - Trade weights ωij calculated as ratio of sum of exports and imports of country i with country j to country i's total trade; weights averaged over 2008–2013.
  - GVAR assumption: countries behave as small open economies (no long-run feedback from domestic to foreign country-specific variables); weak exogeneity tested.
- Country-specific model specification:
  - Four domestic variables per country: it y, it ex, it reer, it crd.
  - Three global variables: t oil, t loan, t vix.
  - All series seasonally adjusted where necessary (TRAMO/SEATS), deflated by CPIs, transformed into logs.
  - US treated differently: US VARX allows feedback from US macro variables to global variables (oil, global credit, vix); only country excluded from small-open-economy assumption.
- Unit root and cointegration:
  - Variables assumed I(1); tested with ADF and WS (Park and Fuller, 1995) using AIC for lag selection — tests support I(1) assumption in general.
  - Lag orders p i and q i selected by AIC; maximum lag allowed is two due to data limitations.
  - Cointegration rank for each country model computed using Johansen trace and maximum eigenvalue statistics; cointegration ranks and p i, q i summary presented in Appendix B (country-level table).
- Weak exogeneity tests (summary for major economies; F-statistics):
  - Table 4 F tests for Weak Exogeneity (F test *it y, *it ex, *it reer, *it crd, t oil, t loans, t vix)
    - US F(4, 70): *it y 2.80**; *it ex 1.45; *it reer 0.42; *it crd 0.08; t oil - ; t loans - ; t vix -
      - “**” denotes significance at 5 percent.
    - EA F(3, 74): *it y 0.39; *it ex 1.44; *it reer 0.74; *it crd 0.49; t oil 0.85; t loans 2.18; t vix 1.07
    - UK F(3, 74): *it y 2.26; *it ex 1.17; *it reer 0.30; *it crd 0.79; t oil 1.07; t loans 1.07; t vix 1.31
    - China F(1, 76): *it y 0.19; *it ex 0.57; *it reer 0.53; *it crd 0.02; t oil 0.36; t loans 0.12; t vix 0.25
  - Implication: weak exogeneity assumption fails only for output in the US model (evidence of long run feedback), so foreign output variable excluded from US model.
- Estimation and inference:
  - Generalized Impulse Response Functions (GIRF) used to measure shock effects (advantages: ordering-invariant).
  - Focus of reported GIRFs: transmission of shocks arising in US, advanced economies excluding US (AE), and emerging market economies (EM) to LIDCs.
  - Shock experiments: one standard deviation negative shock to output and to private credit.
  - Country groupings: AE = Canada, Euro Area (France, Germany, Italy), Japan, UK; EM = Brazil, China, India; remaining countries (Korea, Australia, Argentina, Mexico, Turkey, Indonesia, etc.) in "other countries."
  - LIDC subgroup classifications used in analysis: oil-exporting vs. oil-importing; commodity-exporting vs. importing; frontier economies vs. remaining LIDCs (Table 5 lists countries by subgroup).

### Key estimation results and impulse response findings
- General patterns:
  - LIDCs show greater business cycle synchronization with emerging markets (EMs) than with other countries; effect strongest for oil-exporting LIDCs.
  - GIRFs presented are responses to a one standard error negative shock to output and to private credit in source regions.
- Output shocks:
  - Negative shock to EM output:
    - Oil-exporting LIDCs exhibit a significant negative response of real GDP.
    - Oil-importing LIDCs show insignificant response.
  - Negative shock to AE output:
    - Oil-exporting LIDCs show a brief and smaller negative impact on output.
    - Oil-importing LIDCs show insignificant response.
  - Shock to US output:
    - Output in both oil-exporting and oil-importing LIDCs fails to respond significantly to shocks to US output (figures omitted).
  - Interpretation:
    - EMs have become more important trade partners for LIDCs; EMs have higher income elasticity of demand for oil, explaining stronger synchronicity with oil-exporting LIDCs.
- Export responses:
  - Negative shock to US output:
    - Exports of oil-exporting LIDCs respond significantly (after three quarters).
    - Exports of oil-importing LIDCs show insignificant response.
    - Export-to-GDP ratios: oil-exporting LIDCs ≈ 38 percent; oil-importing LIDCs ≈ 33 percent.
    - Commodity-exporting LIDCs have similar vulnerability: exports-to-GDP ratio ≈ 38 percent.
    - Frontier LIDCs have exports-to-GDP ratio ≈ 31 percent but still show significant responses in output and exports to global output shocks due to stronger financial integration.
- Credit shocks:
  - One standard error negative shock to private credit in EM and AE:
    - Do not have a significant effect on LIDCs’ output and exports (charts available upon request).
  - One standard error negative shock to US private credit:
    - Significant negative effect on output of oil-exporting LIDCs.
    - Exports in both oil-exporting and oil-importing LIDCs react significantly to US credit shocks; responses more pronounced for oil-exporting LIDCs.
    - Frontier LIDCs are particularly hard hit by US credit contractions.
  - Interpretation / puzzles:
    - US credit shocks have stronger spillovers to LIDCs than European credit shocks despite larger European cross-border lending exposure to some LIDCs (e.g., sub-Saharan Africa). Possible explanations:
      - European lending ties may reflect longer-term/colonial relations that dampen short-run transmission.
      - US credit shocks may be amplified by responses of other AEs; AE credit shocks might be offset by US monetary policy.
- Summary quantitative display:
  - Figures reported show impulse responses (bootstrap mean with 90 percent error bands) for GDP and exports by subgroup; consolidated candlestick charts present responses after three quarters for GDP and exports to US private credit shocks.

### Model estimation diagnostics and country-level model orders
- Maximum lag order allowed in GVAR due to data limitations: two.
- VARX(p i, q i) orders and cointegration relationships (sample from Appendix B; p i and q i and # cointegrating relationships based on Johansen’s trace at 95 percent critical value). Examples:
  - Argentina: p i 2; q i 1; #Coint. 2
  - Australia: p i 2; q i 1; #Coint. 2
  - Bangladesh: p i 2; q i 1; #Coint. 1
  - Bolivia: p i 1; q i 1; #Coint. 3
  - Burundi: p i 2; q i 1; #Coint. 0
  - Euro Area: p i 2; q i 1; #Coint. 3
  - India: p i 2; q i 1; #Coint. 2
  - Japan: p i 1; q i 1; #Coint. 1
  - USA: p i 2; q i 1; #Coint. 4
  - (Full country-level table of p i, q i, and cointegration relationships reported in Appendix B.)
- Unit root tests: ADF and WS (Park and Fuller, 1995) — results support variables are generally I(1). Weak exogeneity tests summarized above.

### Main conclusions and policy implications (Section VI)
- Main empirical conclusions:
  - Different LIDC subgroups are exposed to shock transmission from the US, other advanced economies, and emerging markets along various dimensions.
  - Oil- and commodity-exporting countries and frontier markets exhibit greater business cycle synchronicity with emerging market economies.
  - US credit conditions have a notable effect on LIDCs’ output and exports, particularly for oil exporters, commodity exporters, and frontier markets; credit conditions in other parts of the world appear less significant in the estimates.
- Two highlighted policy implications:
  - Diversification of export components and export partners could help stabilize output fluctuations induced by business cycles in selected, fast-growing emerging market economies.
  - Broadening the spectrum of external financing channels could reduce exposure to US credit supply shocks.

*Source: _wp16102 - Section V outlines and discusses the estimation results; and Section VI concludes. (IMF working paper content provided in the input)*

### References

### References

### Global shocks, low-income countries, and spillovers
- Berg, Andrew, Chris Papageorgiou, Catherine Pattillo, Martin Schindler, Nikola Spatafora, and Hans Weisfeld, 2011, “Global Shocks and Their Impact on Low-Income Countries: Lessons from the Global Financial Crisis,” IMF Working Paper No. 11/27 (Washington: International Monetary Fund).
- Espinoza, Raphael A., Sarwat Jahan, and Era Dabla-Norris, 2012, “Spillovers to Low-Income Countries: Importance of Systemic Emerging Markets,” IMF Working Paper No. 12/49 (Washington: International Monetary Fund).
- International Monetary Fund, 2014, “Macroeconomic Developments in Low-Income Developing Countries” (Washington).
- Raddatz, Claudio, 2007, "Are External Shocks Responsible for the Instability of Output in Low-Income Countries?" Journal of Development Economics, Elsevier, Vol. 84 (1), pp. 155–87.
- Samake, Issouf, and Yongzheng Yang, 2014, “Low-Income Countries’ Linkages to BRICS: Are There Growth Spillovers?” Journal of Asian Economics, Vol. 30, pp. 1–14.

### Global financial transmission, risk episodes, and trade
- Canales-Kriljenko, Jorge, Mehdi Hosseinkouchack, and Alexis Meyer-Cirkel, 2014, “Global Financial Transmission into Sub-Saharan Africa – A Global Vector Autoregression Analysis,” IMF Working Paper No. 14/241 (Washington: International Monetary Fund).
- Berman, Nicolas, and Philippe Martin, 2010, "The Vulnerability of Sub-Saharan Africa to the Financial Crisis: The Case of Trade," CEPR Discussion Papers No. 7765 (C.E.P.R. Discussion Papers).
- De Bock, Reinout, and Irineu de Carvalho Filho, 2015, “The Behavior of Currencies During Risk-off Episodes,” Journal of International Money and Finance, Vol. 53 (C), pp. 218–34.
- Manova, Kalina, 2013, "Credit Constraints, Heterogeneous Firms, and International Trade," Review of Economic Studies, Vol. 80 (2), pp. 711–44 (New York: Oxford University Press).
- Xu, Teng Teng, 2012, "The Role of Credit in International Business Cycles," Cambridge Working Papers in Economics 1202, Faculty of Economics, University of Cambridge.

### Global VAR, regional interdependencies, and macroeconometric modeling
- Chudik, Alexander, and M. Hashem Pesaran, 2009, "Infinite-Dimensional VARs and Factor Models," Working Paper Series No. 998 (European Central Bank).
- Chudik, Alexander, and M. Hashem Pesaran, 2014, "Theory and Practice of GVAR Modeling," Globalization and Monetary Policy Institute Working Paper No. 180 (Federal Reserve Bank of Dallas).
- Dees, Stephane, Filippo di Mauro, M. Hashem Pesaran, and L. Vanessa Smith, 2007a, "Exploring the International Linkages of the Euro Area: A Global VAR Analysis," Journal of Applied Econometrics, Vol. 22 (1), pp. 1–38.
- Pesaran, M. Hashem, Til Schuermann, and Scott Weiner, 2004, “Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model,” Journal of Business & Economic Statistics, Vol. 22 (2), pp. 129–62.
- Pesaran, M. Hashem, and Ron Smith, 2006, "Macroeconometric Modelling with a Global Perspective," Cambridge Working Papers in Economics No. 0604, Faculty of Economics, University of Cambridge.

### Econometric methods, cointegration, impulse responses, and unit root testing
- Johansen, Soren, 1992, "Cointegration in Partial Systems and the Efficiency of Single-Equation Analysis," Journal of Econometrics, Elsevier, Vol. 52 (3), pp. 389–402.
- Harbo, Ingrid, Soren Johansen, Bent Nielsen, and Anders Rahbek, 1998, "Asymptotic Inference on Cointegrating Rank in Partial Systems," Journal of Business & Economic Statistics, Vol. 16 (4), pp. 388–99.
- Pesaran, M. Hashem, and Yongcheol Shin, 1996, "Cointegration and Speed of Convergence to Equilibrium," Journal of Econometrics, Elsevier, Vol. 71 (1–2), pp. 117–43.
- Pesaran, M. Hashem, and Yongcheol Shin, 1998, “Generalized Impulse Response Analysis in Linear Multivariate Models,” Economics Letters, Vol. 58 (1), pp. 17–29.
- Koop, Gary, M. Hashem Pesaran, and Simon M. Potter, 1996, "Impulse Response Analysis in Nonlinear Multivariate Models," Journal of Econometrics, Elsevier, Vol. 74 (1), pp. 119–47.
- Park, Heon Jin, and Wayne A. Fuller, 1995, ”Alternative Estimators and Unit Root Tests for the Autoregressive Process,” Journal of Time Series Analysis, Vol. 16 (4), pp. 415–29.
- Fernandez, Roque, 1981, “A Methodological Note on the Estimation of Time Series,” The Review of Economics and Statistics, Vol. 63 (3), pp. 471–76.
- Senhadji, Abdelhak S., and Claudio E. Montenegro, 1999, “Time Series Analysis of Export Demand Equations: A Cross-Country Analysis,” Palgrave Macmillan, Vol. 46 (3), pp. 259–73.

### Foundational macroeconomic and sectoral studies
- Sims, Christopher A, 1980, "Macroeconomics and Reality," Econometrica, Vol. 48 (1), pp. 1–48.
- Hamilton, James, 2009, “Causes and Consequences of the Oil Shock of 2007–08” (San Diego: University of California, Department of Economics).

*Source: _wp16102 - References*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2016/_wp16102.pdf_