## _wp16210

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---

### I. Introduction — key factual statements and historical magnitudes
- Real oil prices (WTI in 2015 U.S. dollar) have fluctuated between highs of $145 and lows of $15 per barrel over the period 1946M1 and 2016M6.
- Since 1986 there have been six episodes of sharp decline in oil prices (30% or more in each episode) within seven months.
- The fall in oil prices since June 2014 is large but not unprecedented.
- General empirical pattern reported:
  - Oil importers tend to benefit from a fall in oil prices (once demand conditions are controlled for).
  - Oil exporters tend to lose from a fall in oil prices.
- Factors potentially altering transmission in recent years:
  - Near-zero interest rates in most industrialized economies.
  - Renewed U.S. crude oil exports (U.S. began exporting crude oil in January 2016 after a 40-year ban).
  - Positive correlation between oil prices and equity markets in recent years interpreted by some as evidence of slowing global aggregate demand.
- Data limitation noted:
  - Quarterly macro series are not sufficiently long for reliable analysis over multiple sub-periods (motivating monthly U.S. bivariate analyses).

### II. GVAR-Oil model — scope, data, and technical structure
- Model and sample:
  - Model used: GVAR-Oil model developed in Mohaddes and Pesaran (2016).
  - Estimation sample for the GVAR-Oil model: quarterly data between 1979Q2 and 2013Q1.
  - Model coverage: 27 countries/regions (euro area treated as a single economy).
- Country-specific endogenous variables (maximum of six):
  - x_it = [q_o_it; y_it; π_it; ep_it; r_S_it; r_L_it] for major oil producers.
  - x_it = [y_it; π_it; ep_it; r_S_it; r_L_it] for other countries.
  - US model endogenous variables: x_US,t = [eq_t; q_o_US,t; y_US,t; π_US,t; r_S_US,t; r_L_US,t].
  - eq_t denotes log of world real equity prices included endogenously in US model and as weakly exogenous elsewhere.
- Construction of foreign ("star") variables:
  - x*_it = Σ_j w_ij x_jt where w_ij are bilateral trade weights; w_ii = 0 and Σ_j w_ij = 1.
  - Trade weights computed as three-year averages over 2007–2009.
- Oil price decomposition and notation:
  - ~p_o_t = Σ_i ω_i ~p_o_it
  - ~p_o_it = ln(P_o_t E_it / P_it) = p_o_t + (e_it − p_it)
  - p_o_t is US dollar oil price component; ep_it = e_it − p_it is real exchange rate component.
  - World aggregates: y_t = Σ_i w_i y_it; q_o_t = Σ_i w^o_i q_o_it; ep_t = Σ_i ω_i ep_it.
- Oil price equation (lag order unity form):
  - ~p_o_t = c_p + ϕ_1 ~p_o,t-1 + β_1 y_t-1 + γ_1 q_o,t-1 + u_o_t.
- Stacked system and reduced form:
  - G0 z_t = b_t + G1 z_t-1 + v_t.
  - Reduced form: z_t = a_t + F z_t-1 + ξ_t.
- Special modeling choices:
  - Foreign inflation excluded from the euro model because π*_EA,t could not be treated as weakly exogenous.
  - Foreign interest rates excluded from the US model; preliminary tests did not support weak exogeneity for r*_S,US,t and r*_L,US,t.
  - US model includes only two foreign variables: x*_US,t = [y*_US,t; ep*_US,t]'.
- Noted advantages of GVAR approach:
  - Identification of country-specific shocks and counterfactuals.
  - Accommodation of heterogeneities across countries (reserves, production capacities, geopolitical differences).
  - Accounting for economic interlinkages and spillovers across regions.

### III. Effects of a fall in oil prices — GVAR-Oil impulse-response evidence (quarterly)
- Summary impulse-response patterns (median responses to a one-standard-deviation decrease in oil prices, with 95 percent bootstrapped confidence bounds):
  - Global real equity prices: tend to increase following a negative oil price shock; effects typically within two quarters.
  - Global long-term interest rates: tend to fall following a negative oil price shock.
  - Global real GDP (PPP-GDP weighted responses of 27 countries): positive real output effects materialize more slowly, with median impulse responses generally manifesting in the medium-term, around four quarters after a negative oil price shock.
  - Inflation: strong disinflationary effects in many countries following oil price declines.
- Timing and heterogeneity:
  - Financial variables (equity prices and interest rates): effects typically within two quarters.
  - Real output effects: positive median responses around four quarters after shock.
  - Country-level magnitudes and timing vary; qualitative patterns (falling interest rates, rising equity prices) broadly similar.
- Interpretation caveats:
  - Benefits of lower oil prices likely delayed given uncertainties over global growth strength and potential surges in financial market volatility.
  - For some post-2008 analyses GVAR cannot be applied due to limited quarterly sample length.

### IV. Focus on monthly U.S. analysis — motivation and data features
- Reasons for monthly U.S. focus:
  - Longer monthly sample (1946–2016) allows assessment of time-variation not feasible with quarterly global GVAR sample.
  - US oil production trends and policy changes: production first peaked in 1971; production rose from 5 million barrels per day (b/d) in January 2008 to 9.2 million b/d in January 2016 (around 84% increase); U.S. began exporting crude oil in January 2016.
  - Advances in hydraulic fracturing and directional drilling expanded US oil production; unconventional oil now forms around half of US oil output.
  - The US oil and gas sector attracted significant investment; small firms issuing large amounts of debt estimated over $350 billion between 2010 and 2014.
- Market-sector consequences from recent oil-price fall:
  - Losses for U.S. investors in equity and bond markets substantial; valuations of U.S. energy companies fell dramatically.
  - Since mid-2014, 59 US oil and gas companies have filed for bankruptcy; number expected to rise further, soon overtaking the 68 bankruptcies filed at the peak of the dot-com bust in 2002-2003.
  - Energy-related investments in the United States estimated to have fallen by 65% cumulatively since mid-2014; energy sector contribution to GDP growth overall negative.

### V. Stability and time-variation: oil prices, equity prices, and dividends (monthly evidence 1946–2016)
- Visual and simple correlation evidence:
  - Little evidence of a stable relationship between real oil prices and real equity prices over 1946–2016.
- Table 2 correlations (changes, monthly):
  - Full Period 1946M2–2016M3:
    - Real Oil and Equity Prices: 0.008 (0.035)
    - Real Oil Prices and Dividends: -0.105(0.034)
  - 1960M1–1980M1:
    - Real Oil and Equity Prices: 0.018 (0.063)
    - Real Oil Prices and Dividends: -0.071 (0.063)
  - 1981M1–2000M12:
    - Real Oil and Equity Prices: -0.139(0.064)
    - Real Oil Prices and Dividends: -0.163(0.064)
  - 2001M1–2016M3:
    - Real Oil and Equity Prices: 0.199(0.073)
    - Real Oil Prices and Dividends: -0.252(0.072)
  - 2001M1–2007M12:
    - Real Oil and Equity Prices: -0.144 (0.109)
    - Real Oil Prices and Dividends: -0.088 (0.110)
  - 2008M1–2016M3:
    - Real Oil and Equity Prices: 0.404(0.093)
    - Real Oil Prices and Dividends: -0.329(0.096)
  - Note: standard errors in parentheses; bold correlations in source indicate significance.
- Rolling-regression evidence (10-year windows, window size 120 months):
  - Coefficients of real oil price changes on real US equity-price changes:
    - Not statistically different from zero before 1990.
    - Became negative in 1991 and statistically significant from 1991 to 2001.
    - Rose and became positive since the 2008 financial crisis, statistically significant from 2012 onwards.
  - Coefficients of real oil price changes on real dividends:
    - Rolling estimates negative over whole sample and statistically significantly negative for most of the period.
    - Beneficial effects of lower oil prices on dividends became stronger post-2009; rolling estimates particularly large and statistically significant after 2009.

### VI. ARDL analyses — long-run coefficients and sub-sample behavior
- ARDL models and sample windows:
  - Two ARDL models estimated on full sample 1970M1–2016M4 and three sub-samples: 1970M1–1989M12, 1990M1–2007M12, and 2008M1–2016M4.
  - Lag order for ARDL regressions with equity prices selected by Akaike Information Criterion (AIC) with maximum lag order set to 12.
  - For ARDL models with real dividends the lag order was selected using the Schwarz Bayesian Criterion.
- Key ARDL findings summarized from Table 3 entries in the source:
  - (a) ARDL Model with Real Equity Prices — Oil Price Coefficient0:159  0:176  0:185  0:202  (0:073)(0:100)(0:039)(0:118)
  - (b) ARDL Model with Real Dividends — Oil Price Coefficient0:0160:046  0:092  0:111  (0:017)(0:014)(0:043)(0:048)
  - (c) ARDL Model with Industrial Production — Oil Price Coefficient0:053  0:084  0:0190:098 (0:025)(0:029)(0:014)(0:075)
  - (d) ARDL Model with Manufacturing Production — Oil Price Coefficient0:075  0:116  0:0220:067 (0:027)(0:036)(0:017)(0:063)
  - Notes: Symbols ***, **, and * denote significance at 1%, 5%, and 10% levels, respectively, in the source.
- Interpretation:
  - Equity-price ARDL coefficients: negative and significant for full sample and sub-samples 1970–1989 and 1990–2007; positive and significant for 2008–2016 sub-sample — confirming unstable oil–equity relationship.
  - Dividends ARDL coefficients: negative and statistically significant in all sub-samples, including post-2008 — consistent evidence that lower oil prices associate with higher dividends (proxy for real activity).
  - Industrial and manufacturing series used as robustness checks; coefficients discussed in source tables.

### VII. Oil exporters, welfare spending, fiscal adjustments, and global demand–supply effects
- Fiscal and welfare effects for oil exporters:
  - Major oil exporters hit hardest by fall in oil prices, particularly after substantial welfare program expansions during preceding high-price period.
  - Post-2011 GCC social spending increase around $150 billion (source citation in text).
  - Saudi Arabia increased government employees pay and benefits by $93 billion (source citation in text).
  - Iran initiated a subsidy reform increasing fuel prices and using revenue to finance a universal cash transfer.
  - Consequences: cuts to welfare programs, withdrawals from oil funds, attempts to diversify economies.
- Global demand–supply adjustment and medium-term outlook:
  - World-level expectation: increase in spending by oil importers expected to exceed decline in expenditure by oil exporters (due to different marginal propensities to consume/invest), implying lower oil prices should eventually be beneficial for the world economy.
  - GVAR-Oil model responses of global and country GDPs illustrate net positive effect following a fall in oil prices.

### VIII. How global oil supplies respond to lower oil prices — heterogeneity, investment, and Ricardian analogy
- Heterogeneous producer responses:
  - Non-OPEC exporters, particularly U.S. producers, respond quickly and positively to oil price rises and negatively to price falls.
  - U.S. production rose since 2008, peaked around April 2015 at 9.45 million b/d and had fallen to 8.80 million b/d in the first week of May 2016.
  - Since mid-2014, 59 U.S. oil and gas companies have filed for bankruptcy.
  - Unconventional oil behaves like a manufacturing process; high sensitivity to price declines.
- OPEC and political-economy behavior:
  - OPEC production likely less responsive; some major exporters set production to achieve target oil-revenue levels (the "target revenue model"), possibly producing a backward-bending supply curve.
  - Russia continued to increase production, consistent with revenue-target behavior.
  - Canada’s production became more volatile but trended upward; Norway and Mexico stabilized after downward trends.
- Investment implications:
  - IEA estimate: global upstream oil and gas investment fell by around 23% and 19% in 2014 and 2015, respectively.
  - BP reported oil and gas investments fell by $160 billion in 2015 and were expected to fall by another $50 billion in 2016.
  - These investment declines imply future-supply implications, with higher-cost unconventional fields most likely to be cut back if low prices persist.
- Ricardian analogy:
  - Lower prices first curtail high-cost (lower-quality) sources; as demand later rises, lower-quality sources brought back into production and earn rent.
  - Given heterogeneity of breakeven costs, high-cost unconventional production is most vulnerable.

### IX. Synthesis, medium-term outlook, and concluding interpretation
- Synthesis of quarterly GVAR-Oil and monthly U.S. evidence:
  - Quarterly GVAR-Oil model:
    - Short-term (few quarters): financial prices, long-term rates, and inflation react quickly to oil-price changes.
    - Medium-term: global GDP and country-level real GDP respond positively to an oil-price fall.
    - Empirical evidence supports view that an oil-price fall is good news for the U.S., other major economies, and the global economy.
  - Monthly U.S. evidence:
    - Oil–equity price relationship is time-varying and unstable; turned positive post-2008 for equities.
    - Real dividends and other monthly real-activity measures show a stable negative association with oil prices: lower oil prices associated with higher dividends, including post-2008.
    - ARDL results corroborate rolling-regression and correlation findings.
- Medium-term outlook and market equilibrium:
  - Lower oil prices will eventually increase demand and reduce supplies; net effect for the world economy likely positive due to stronger spending by importers outweighing exporters’ revenue losses.
  - U.S. production has been falling while OPEC production has risen (mainly Saudi Arabia and Iraq), reflecting revenue-compensation behavior by some exporters; markets equilibrate slowly.
  - Oil prices likely to fluctuate within a wide range; the ceiling is characterized by the marginal cost for U.S. shale oil producers (around $60 per barrel).
  - Episodic fluctuations will be influenced by new reserve discoveries, technological advances, and alternative energy sources.

*Content derived from the provided IMF Working Paper excerpt.*

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

### _wp16210 - References .............................................................................................................

### Figures and Tables (from source)
- Figures listed:
  - 1. Nominal and Real (2015 U.S. dollars) WTI Oil Prices
  - 2. Effects of Lower Oil Prices on Global Real Equity Prices, Long-Term Interest Rates, and Real GDP
  - 3. Effects of Lower Oil Prices on Long-Term Interest Rates in Various Countries
  - 4. Effects of Lower Oil Prices on Inflation in Various Countries
  - 5. Effects of Lower Oil Prices on Real GDP in Various Countries
  - 6. U.S. Oil Production (1000 barrels/day)
  - 7. Real Oil Prices and Real US Equity Prices (S&P 500), 1946M1–2016M3
  - 8. Rolling Estimates of the Effects of Changes in Oil Prices on Equity Prices
  - 9. Real Oil Prices and Real Dividends (S&P 500), 1946M1–2016M3
  - 10. Rolling Estimates of the Effects of Changes in Oil Prices on Real Dividends
  - 11. Monthly Oil Production for Iran, Iraq, Russia, Saudi Arabia, and the US (1000 barrels/day)
- Tables listed:
  - 1. Countries and Regions in the GVAR-Oil Model
  - 2. Correlations between Changes in Real Oil Prices, Equity Prices and Dividends
  - 3. Estimates of the Long-run Coefficients of Real Oil Prices based on Various ARDL Regressions and Sub-samples, 1970M1–2016M4

### I. INTRODUCTION — key factual statements and historical magnitudes
- Real oil prices (WTI in 2015 US dollar) have fluctuated between highs of $145 and lows of $15 per barrel over the period 1946M1 and 2016M6.
- Since 1986 there have been six episodes of sharp decline in oil prices (30% or more in each episode) within seven months.
- The fall in oil prices since June 2014 is large but not unprecedented.
- Literature cited (examples noted in text): Hamilton (2009), Kilian (2009), Cashin et al. (2014), Mohaddes and Pesaran (2016), Mohaddes and Raissi (2015), Obstfeld et al. (2016), Bernanke (2016).
- General empirical pattern reported:
  - Oil importers tend to benefit from a fall in oil prices (once demand conditions are controlled for).
  - Oil exporters tend to lose from a fall in oil prices.
- Recent arguments and considerations:
  - Near-zero interest rates in most industrialized economies and renewed U.S. crude oil exports may have altered traditional transmission channels of lower oil prices.
  - Positive correlation between oil prices and equity markets in recent years has been interpreted as evidence of slowing global aggregate demand; hence some argue the recent oil price decline may not be unambiguously good news for the U.S. and industrialized economies.
- Net overall outcome for the global economy depends on:
  - Domestic political economy considerations.
  - Feedback effects on global energy demand, interest rates, financial markets, and world trade.
- Data limitations:
  - Quarterly macro series are not sufficiently long for reliable analysis over multiple sub-periods, particularly post-2008; therefore the GVAR-Oil model cannot be used for some sub-period assessments, motivating monthly bivariate analyses for the U.S.

### II. ANALYZING THE OIL MARKET USING A MULTI-COUNTRY MODEL — model scope and data
- Model used: GVAR-Oil model developed in Mohaddes and Pesaran (2016).
- Estimation sample for the GVAR-Oil model: quarterly data between 1979Q2 and 2013Q1.
- Model coverage: 27 countries/regions (euro area treated as a single economy).
- Country grouping in the GVAR-Oil model (as listed in Table 1):
  - Major Oil Producers: Canada, Indonesia, Iran, Mexico, Norway, Saudi Arabia
  - Net Exporters (other): [as listed in Table 1 under "Other Countries" headings]
  - Net Importers and regional breakdowns for Europe, Asia Pacific, Latin America, Rest of the World (specific country list provided in Table 1).
- Country-specific endogenous variables (maximum of six):
  - x_it = [q_o_it; y_it; π_it; ep_it; r_S_it; r_L_it] for major oil producers
  - x_it = [y_it; π_it; ep_it; r_S_it; r_L_it] for other countries
  - US model endogenous variables: x_US,t = [eq_t; q_o_US,t; y_US,t; π_US,t; r_S_US,t; r_L_US,t]
  - eq_t denotes log of world real equity prices included endogenously in US model and as weakly exogenous elsewhere.
- Construction of foreign ("star") variables:
  - x*_it = Σ_j w_ij x_jt where w_ij are bilateral trade weights; w_ii = 0 and Σ_j w_ij = 1.
  - Trade weights computed as three-year averages over 2007–2009.
- Oil price decomposition and notation:
  - ~p_o_t = Σ_i ω_i ~p_o_it
  - ~p_o_it = ln(P_o_t E_it / P_it) = p_o_t + (e_it − p_it)
  - p_o_t is US dollar oil price component; ep_it = e_it − p_it is real exchange rate component.
  - World aggregates:
    - y_t = Σ_i w_i y_it
    - q_o_t = Σ_i w^o_i q_o_it
    - ep_t = Σ_i ω_i ep_it
- Oil price equation (simplified form with lag order unity):
  - ~p_o_t = c_p + ϕ_1 ~p_o,t-1 + β_1 y_t-1 + γ_1 q_o,t-1 + u_o_t
  - Rewritten in terms of p_o_t and ep_t to integrate with country-specific models.
- Model solution:
  - Stacked system yields G0 z_t = b_t + G1 z_t-1 + v_t
  - Reduced form: z_t = a_t + F z_t-1 + ξ_t
- Noted advantages of multi-country GVAR approach:
  - Identification of country-specific shocks and counterfactuals for supply disruptions in specific regions.
  - Accommodation of heterogeneities across countries (reserves, production capacities, geopolitical differences).
  - Accounting for economic interlinkages and spillovers across regions.

### A. The GVAR-Oil model — technical structure (selected components preserved)
- Country-specific VARX* model (lag order unity, general dynamics considered empirically):
  - x_it = a_i0 + a_i1 t + Φ_i x_i,t-1 + Γ_i0 x*_it + Γ_i1 x*_{i,t-1} + A_i0 p_o_t + A_i1 p_o,t-1 + u_it
- Integration of oil price equation into GVAR yields a two-way linkage: global economic conditions and oil supplies affect oil prices with a lag; oil prices influence country-specific variables.
- Special modeling choices:
  - Foreign inflation excluded from euro model because π*_EA,t could not be treated as weakly exogenous.
  - Foreign interest rates excluded from the US model; preliminary tests did not support weak exogeneity for r*_S,US,t and r*_L,US,t when included.
  - US model includes only two foreign variables: x*_US,t = [y*_US,t; ep*_US,t]'.

### B. Effects of a fall in oil prices — main empirical findings from GVAR-Oil
- Impulse-response evidence (median responses to a one-standard-deviation decrease in oil prices, with 95 percent bootstrapped confidence bounds):
  - Global real equity prices: tend to increase following a negative oil price shock; effects show up relatively quickly, typically within two quarters.
  - Global long-term interest rates: tend to fall following a negative oil price shock; decline observed across major economies.
  - Global real GDP (PPP-GDP weighted responses of 27 countries): positive real output effects materialize more slowly, with median impulse responses generally manifesting in the medium-term, around four quarters after a negative oil price shock.
  - Inflation: strong disinflationary effects are found in many countries following oil price declines.
- Timing summary:
  - Financial variables (equity prices and interest rates): effects typically within two quarters.
  - Real output effects: positive median responses around four quarters after shock, indicating a delay before real economy benefits are realized.
- Country-level heterogeneity:
  - Country-by-country impulse responses show similar qualitative patterns (falling interest rates, rising equity prices), but magnitudes and timing vary across countries.
- Caveats and interpretation:
  - Due to uncertainties over global growth strength and potential surges in financial market volatility, benefits of lower oil prices are likely to be delayed in their materialization.
  - For some post-2008 analyses, the GVAR-Oil model cannot be applied due to limited quarterly sample length; monthly bivariate analyses are used for U.S. equity and dividend relationships over 1946–2016 to assess longer-run patterns.

*Source: Content unit _wp16210 - References (extracted from the provided IMF PDF content).*

### Section 4.2 for estimates of the country-specific VARX* models including discussions about lag order selection,

### _wp16210 - Section 4.2 for estimates of the country-specific VARX* models including discussions about lag order selection,

### VARX* estimation, data, and methodological notes
- Section covers estimates of the country-specific VARX* models including discussions about lag order selection, cointegrating relations, and persistence profiles.
- Evidence for the weak exogeneity assumption of the foreign variables and discussion of structural breaks in the context of the GVAR-Oil model is given in Appendix B.
- Quarterly dataset covers 1979Q2 to 2013Q1; construction of variables and data sources described in Appendix A of Mohaddes and Pesaran (2016).

### Impulse-response results from the GVAR-Oil model (quarterly)
- Financial prices and inflation:
  - Global equity prices, long-term interest rates, and inflation respond relatively quickly to oil price changes, within a few quarters (Figures 3 and 4).
- Real output:
  - Effects of oil price changes on real output take longer to manifest.
  - Impulse responses for global GDP following an oil price fall are positive in the medium-term (Figure 2).
  - Individual-country responses in Figure 5 also show positive medium-term effects on real GDP.
- Overall interpretation:
  - Empirical evidence based on the GVAR-Oil model supports the view that an oil price fall is good news for the US, the other major economies, as well as for the global economy.

### Focus on monthly analysis for the US (Section III)
- Reasons for focusing on the US:
  - US not as dependent on oil imports historically; oil production first peaked in 1971.
  - US began exporting crude oil in January 2016 after a 40-year ban.
  - Advances in hydraulic fracturing and directional drilling expanded US oil production over the past 10 years.
  - The US oil and gas sector attracted significant investment over the past decade, including small firms issuing large amounts of debt (estimated over $350 billion between 2010 and 2014).
- US oil production statistics:
  - US oil production rose from 5 million barrels per day (b/d) in January 2008 to 9.2 million b/d in January 2016, around 84% increase (Figure 6).
- Market-sector consequences:
  - Losses for US investors in equity and bond markets have been substantial following the recent fall in oil prices; valuations of US energy companies fell dramatically and bankruptcies in the sector increased.

### Stability of the relationship between real oil prices and real equity prices (1946–2016)
- Visual and correlation evidence:
  - Long historical perspective (1946–2016) shows little evidence of a stable relationship between oil prices and real equity prices (Figure 7).
  - Table 2: Correlations between changes in real oil prices and equity prices and dividends
    - Full Period 1946M2–2016M3:
      - Real Oil and Equity Prices: 0.008 (0.035)
      - Real Oil Prices and Dividends: -0.105(0.034)
    - Sub-Periods:
      - 1960M1–1980M1:
        - Real Oil and Equity Prices: 0.018 (0.063)
        - Real Oil Prices and Dividends: -0.071 (0.063)
      - 1981M1–2000M12:
        - Real Oil and Equity Prices: -0.139(0.064)
        - Real Oil Prices and Dividends: -0.163(0.064)
      - 2001M1–2016M3:
        - Real Oil and Equity Prices: 0.199(0.073)
        - Real Oil Prices and Dividends: -0.252(0.072)
    - Sub-Sub-Periods:
      - 2001M1–2007M12:
        - Real Oil and Equity Prices: -0.144 (0.109)
        - Real Oil Prices and Dividends: -0.088 (0.110)
      - 2008M1–2016M3:
        - Real Oil and Equity Prices: 0.404(0.093)
        - Real Oil Prices and Dividends: -0.329(0.096)
    - Note: Bold correlations in the source indicate significance; standard errors in parentheses.
- Rolling-regression evidence (10-year windows, window size 120 months):
  - Rolling estimates of the coefficient of the rate of change of real oil prices on the rate of change of real US equity prices (Figure 8):
    - Coefficients were not statistically different from zero before 1990.
    - Became negative in 1991 and statistically significant from 1991 to 2001.
    - Eventually rose and became positive since the 2008 financial crisis, statistically significant from 2012 onwards.

### Explanations for time-varying oil–equity relations
- Potential drivers of the changing relationship:
  - Periods when equity prices disconnect from fundamentals can produce perverse oil–equity correlations not reflective of real-economy links.
  - Sovereign Wealth Funds (SWFs) activity:
    - Examples with market values in billions in June 2015 (numbers in parentheses as in source):
      - Norway’s Government Pension Fund ($830)
      - Abu Dhabi Investment Authority ($773)
      - Saudi Arabia’s Fund (SAMA) ($685)
      - Kuwait Investment Authority ($592)
      - Qatar Investment Authority ($256)
    - On average 65% of SWF assets are held in public and private equities (61% Norway; 72% SAMA; 65% Kuwait; 68% Qatar; 62% Abu Dhabi–figures based on 2014).
    - During rising oil prices SWFs buy equities; when oil prices fall, major oil exporters may withdraw money to maintain expenditures, inducing an unintended positive correlation between oil and equity prices.

### Relationship between oil prices and real economic activity (dividends) — monthly evidence
- Motivation for using dividends:
  - Quarterly GDP series too short for reliable sub-period analysis; no reliable monthly aggregate real-activity series.
  - Real dividends on S&P 500 used as a proxy for economic activity: sustained dividends imply long-run profitability and reflect economic climate.
- Correlation and rolling-regression evidence:
  - Figure 9 and Table 2 show generally lower (higher) oil prices associated with higher (lower) dividends.
  - Correlations in Table 2 indicate negative relationships between changes in real oil prices and dividends across sub-periods; statistically significant for full sample (1946–2016), and sub-samples 1981–2000 and 2011–2016, but not for 1960–1980.
  - Rolling regressions of the rate of change of real dividends on the rate of change of real oil prices (window size 120 months, Figure 10):
    - Rolling estimates of the coefficient have been negative over the whole sample and statistically significantly negative for most of the period.
    - Beneficial effects of lower oil prices on dividends became stronger post-2009; rolling estimates particularly large and statistically significant after 2009.

### ARDL analyses (full sample and sub-samples)
- Models and sample windows:
  - Two ARDL models estimated on full sample 1970M1–2016M4 and three sub-samples: 1970M1–1989M12, 1990M1–2007M12, and 2008M1–2016M4.
  - Lag order for ARDL regressions with equity prices selected by Akaike Information Criterion (AIC) with maximum lag order set to 12.
- Key ARDL findings:
  - Equity-price ARDL (long-run coefficient of real oil prices; panel (a) of Table 3 in source):
    - Coefficients are negative and statistically significant for the full sample and sub-samples 1970–1989 and 1990–2007.
    - Coefficient is positive and significant based on the 2008–2016 sub-sample.
    - Confirms unstable relationship between oil and equity prices and matches rolling-regression results.
  - Dividends ARDL (panel (b) of Table 3 in source):
    - In all cases the coefficient of the oil price variable is negative and statistically significant in all sub-samples, including the post-2008 period.
    - Consistent with rolling regressions and simple correlations: lower oil prices are associated with higher dividends.
- Additional monthly activity measures:
  - ARDL models also estimated with US industrial production and manufacturing indices (results reported in panels (c) and (d) of Table 3 in the source).
  - These alternative monthly activity measures were used for robustness checks (details and coefficient values reported in the source tables).

### Synthesis and implications
- Quarterly GVAR-Oil model:
  - Short-term (few quarters): financial prices, long-term rates, and inflation react quickly to oil-price changes.
  - Medium-term: global GDP and country-level real GDP respond positively to an oil-price fall.
  - Conclusion: oil-price falls are empirically supported as beneficial for the US, other major economies, and the global economy.
- Monthly US evidence:
  - Relationship between oil prices and equity prices is time-varying and unstable; turns positive post-2008 for equities but remains negative for dividends.
  - Real dividends (proxy for real economic activity) consistently show negative association with oil-price changes—i.e., lower oil prices are associated with higher dividends, including post-2008.
  - ARDL results corroborate rolling-regression and correlation findings: dividends consistently benefit from lower oil prices; equity-price relationship reversed post-2008.
- Concluding interpretation:
  - The recent perverse positive correlation between oil-price falls and equity returns should not be interpreted as evidence that lower oil prices are bad for the real economy; dividends and GDP indicators point to overall beneficial effects from lower oil prices.

*Source: _wp16210 - Section 4.2 for estimates of the country-specific VARX* models including discussions about lag order selection,*

### 3. The coefficient of the oil price variable is negative in all sample periods and for both

### _wp16210 - 3. The coefficient of the oil price variable is negative in all sample periods and for both

### Key empirical findings on oil price coefficients and real activity
- The coefficient of the oil price variable is negative in all sample periods and for both activity measures, but they are statistically significant only for the full sample and the first sub-sample, 1970M1–1989M12, thus supporting the results in panel (b) of Table 3.
- Summary conclusion: unlike the relationship between equity and oil prices, there is a stable negative relationship between oil prices, dividends and monthly real activity measures such as industrial production; this supports the results from the GVAR-Oil model (see Figure 5) and does not support the view that lower oil prices have not been good for the US economy since the 2008 financial crises.
- Table 3 (selected entries as presented in source):
  - (a) ARDL Model with Real Equity Prices — Oil Price Coefficient0:159  0:176  0:185  0:202  (0:073)(0:100)(0:039)(0:118)
  - (b) ARDL Model with Real Dividends — Oil Price Coefficient0:0160:046  0:092  0:111  (0:017)(0:014)(0:043)(0:048)
  - (c) ARDL Model with Industrial Production — Oil Price Coefficient0:053  0:084  0:0190:098 (0:025)(0:029)(0:014)(0:075)
  - (d) ARDL Model with Manufacturing Production — Oil Price Coefficient0:075  0:116  0:0220:067 (0:027)(0:036)(0:017)(0:063)
- Notes from Table 3: Symbols ***, **, and * denote significance at 1%, 5%, and 10% levels, respectively. The lag order of ARDL regressions with real equity prices, industrial and manufacturing production indices were selected using the Akaike Information Criterion with a maximum lag order set to 12. For the ARDL models with real dividends the lag order was selected using the Schwarz Bayesian Criterion.

### Effects on oil exporters, welfare spending, and fiscal adjustment
- Major oil exporters have been hit hardest by the fall in oil prices, particularly because many substantially expanded welfare programs during the preceding period of unusually high oil prices.
- Examples and magnitudes cited:
  - Post-2011, the GCC countries increased their social spending by around $150 billion.
  - Saudi Arabia increased government employees pay and benefits by $93 billion.
  - Iran, facing declining oil revenue, initiated a subsidy reform predicated on increasing fuel prices and using the revenue to finance a universal cash transfer.
- Consequences observed:
  - Oil exporters have been forced to cut back welfare programs, withdraw from oil funds, and attempt to diversify their economies.

### Global demand–supply adjustment and medium-term outlook
- At the world level, the increase in spending by oil importers is expected to exceed the decline in expenditure by oil exporters (given different marginal propensities to consume/invest), implying that lower oil prices should eventually be beneficial for the world economy.
- Within the GVAR-Oil framework (Section B), responses of global and country level GDPs following a fall in oil prices illustrate the net positive effect (see Figures 2 and 5).
- Demand for energy is expected to start to rise as the world economy adjusts; given smoothness of the real dividend series and a large number of lags selected by AIC, some dividend-based estimates were not reliable and Schwarz Bayesian Criterion was used for lag order selection for dividends.

### How global oil supplies respond to lower oil prices
- Heterogeneous producer responses:
  - Non-OPEC oil exporters, particularly US oil producers, tend to respond quickly and positively to oil price rises and negatively to oil price falls.
  - US production rose since 2008, peaked around April 2015 (at 9.45 million b/d) and had fallen to 8.80 million b/d in the first week of May 2016.
  - Unconventional oil now forms around half of US oil output and behaves like a manufacturing process; this explains the sensitivity of US production to price declines.
  - Since mid-2014, 59 US oil and gas companies have filed for bankruptcy, and the number was expected to rise further, soon overtaking the 68 bankruptcies filed at the peak of the dot-com bust in 2002-2003.
  - Energy-related investments in the United States were estimated to have fallen by 65% cumulatively since mid-2014, with the energy sector contribution to GDP growth in the US being overall negative.
- OPEC response and political economy:
  - OPEC production is likely to be less responsive to price changes; political factors often lead some major oil exporters to set production to achieve target oil-revenue levels (the "target revenue model"), producing a backward-bending supply curve where sustained price falls can induce higher production by some low-cost producers.
  - Russia continued to increase production, behaving as predicted by the target revenue model.
  - Canada’s production became more volatile but trended upward; Norway and Mexico stabilized after downward trends since early 2000.
- Investment implications for future supply:
  - International Energy Agency (IEA) estimate: global upstream oil and gas investment fell by around 23% and 19% in 2014 and 2015, respectively.
  - BP reported oil and gas investments fell by $160 billion in 2015 and were expected to fall by another $50 billion in 2016.
  - These investment declines imply implications for future supply, with higher-cost unconventional fields (e.g., US shale) most likely to be cut back if low prices persist.

### Ricardian analogy and implications for future production
- Analogy: Ricardian rent theory — as demand rises, lower-quality (higher-cost) sources are brought into production and earn rent; conversely, when prices fall, high-cost fields are the first to be curtailed.
- Given heterogeneity of breakeven production costs across fields and regions, high-cost unconventional oil production is most vulnerable; further production cutbacks from such fields are expected if low prices persist.

### Concluding remarks and medium-term outlook
- Lower oil prices will eventually increase demand and reduce supplies; net effect for the world economy is likely to be positive due to stronger spending by importers outweighing exporters’ revenue losses.
- On supply: the US production has been falling while OPEC production has risen (mainly Saudi Arabia and Iraq), reflecting revenue-compensation behavior by some exporters; markets equilibrate slowly.
- Oil prices are likely to fluctuate within a wide range; the ceiling is characterized by the marginal cost for US shale oil producers (around $60 per barrel).
- Episodic fluctuations will be further influenced by new reserve discoveries, technological advances in oil production, and alternative energy sources.

*Italic line: Content derived from the provided IMF Working Paper excerpt.*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2016/_wp16210.pdf_