Prepared for Members and Committees of Congress
Congress is concerned with preventing economic recessions and mitigating the effects of
recessions. Eight of the nine post-war recessions were accompanied by sharp increases in the
price of oil. The last four recessions followed this pattern: the 1973-1975 recession followed the
oil embargo; the double dip recession of 1980-1982 followed the second oil shock, which was
caused by the Iranian revolution and Iran-Iraq War; the 1990-1991 recession followed the oil
price spike induced by the Gulf War; and the 2001 recession followed a sharp rise in oil prices
from 1999 to 2000. Policymakers are concerned that the recent rise in oil prices could again
spillover into the wider macroeconomy.
The coincidence of recessions and oil shocks does not prove that oil price changes have any effect
on the economy. To make that case, statistical methods to hold other economic factors constant
must be used. This report surveys the econometric literature on oil shocks to provide quantitative
estimates of how large an effect oil price changes have on economic activity. It also reviews the
statistical robustness of these findings and discusses some of the limitations of these types of
Economic theory suggests that oil shocks lead to higher inflation, a contraction in output, and
higher unemployment in the short run. It is the rise in energy prices, rather than “high” energy
prices, that causes these macroeconomic problems. Effective policy responses are difficult
because expansionary policy would exacerbate the inflationary pressures whereas contractionary
policy would exacerbate the contraction in output.
There is a fair degree of consensus surrounding the range of estimates: for comparable studies,
the cumulative effect of a 10% increase in oil prices during a one-quarter (3 month) period would
be to reduce economic output by 0.2-1.1% over the next year from its baseline level. The
magnitude of these estimates suggests that normal fluctuations in the price of oil would cause
only minor fluctuations in economic growth. However, the estimates suggest that major oil
shocks, in which oil prices rise for several consecutive quarters, often by more than 10% per
quarter, could lead to recessions, all else equal. Some of the findings are not statistically robust. A
few studies dissent from these findings.
Many studies find that the effects of oil on economic activity are waning. For example, a 2004
study found that a 10% increase in oil prices would only reduce GDP by 0.2%. Surprisingly,
many studies found oil to have had stronger economic effects before the mid-1970s, although the
major post-war oil shocks occurred since the mid-1970s. The studies suggest that the relationship
between oil prices and economic activity is not a simple linear one (e.g., episodic oil price
declines have negligible economic effects), but there is no straightforward way to identify a more
This report will be updated as new research becomes available.
Introduction ..................................................................................................................................... 1
Theoretical Considerations.............................................................................................................. 1
Policy Implications.................................................................................................................... 3
Examining the Empirical Record .................................................................................................... 4
Early Studies ............................................................................................................................. 4
Extensions ................................................................................................................................. 5
Oil Shocks or Monetary Policy? ............................................................................................... 8
The Effects of Oil Shocks on Employment............................................................................. 10
Some Caveats ...........................................................................................................................11
Omitted Variable Bias ........................................................................................................11
Structural Misspecification ............................................................................................... 12
Problems with Endogeneity .............................................................................................. 13
Lucas Critique................................................................................................................... 13
Robustness of Results ....................................................................................................... 14
Table 1. Summary of Findings ...................................................................................................... 15
Author Contact Information .......................................................................................................... 17
Congress is concerned with preventing economic recessions and mitigating the effects of
recessions. Eight of the nine post-World War II recessions were preceded by or accompanied with
sharp increases in the price of oil. The last four recessions followed this pattern: the 1973-1975
recession followed the oil embargo; the double dip recession of 1980-1982 followed the second
oil shock, which was caused by the Iranian revolution and Iran-Iraq War; the 1990-1991 recession
followed the oil price spike induced by the Gulf War; and the 2001 recession followed a sharp
rise in oil prices from 1999 to 2000. This would seem to be persuasive evidence that oil prices
have a strong role influence on the business cycle. But the coincidence of recessions and oil
shocks does not, by itself, prove that oil price changes cause economic recessions. To make that
case, statistical methods to hold other economic factors constant must be used. This report
surveys the econometric literature on oil shocks to provide quantitative estimates of how large an
effect oil price changes have on economic activity. It also reviews the statistical robustness of
these findings and discusses some of the limitations of these types of statistical analyses. Before
examining the empirical record, it is useful to explore why economists believe oil shocks might
affect the economy, and explore the channels through which that effect is transmitted.
Due to the central role energy plays in the functioning of the economy and their unusual price
volatility, changes in energy prices are more important to the economy than changes in the price
of most other goods. Energy “shocks” can have macroeconomic consequences, in terms of higher
inflation, higher unemployment, and lower output. Historically, energy price shocks have proven
particularly troublesome for the U.S. economy. Sharp spikes in the price of oil have preceded nine
of the 10 post-war recessions, including the latest one. But since the current economic expansion
began in 2001, energy prices have spiked on several occasions without causing a recession.
Economic theory suggests that economies suffer from recessions due to the presence of “sticky
prices.” If markets adjusted instantly, then recessions could be avoided: whenever economic
conditions changed, price and wage changes would automatically bring the economy back to full
employment. In actuality, however, there are menu costs,1 information costs, uncertainty, and
contracts in our economy that make prices sticky. As a result, adjustment takes time, and
unemployment and economic contraction can result in the interim.
When oil prices rise suddenly, it directly raises the energy portion of inflation measures such as
the consumer price index (energy prices make up about 9% of the consumer price index). As a
result, the overall inflation rate is temporarily pushed up because other prices do not instantly
adjust and fall. If other energy prices rise at the same time, as has often been the case, then the
effect on overall inflation will be magnified.
Because energy is an important input in the production process, the price shock raises the cost of
production for many industries. Transportation accounts for a majority of oil consumption in the
United States, but it is also used for heating and industrial uses, such as plastics. Because other
Products with high “menu costs” are those which are costly to re-price, and therefore have sticky prices. Restaurant
menus, periodicals, and catalog items are examples of products with high menu costs.
prices do not instantly fall, the overall cost of production rises and producers respond by cutting
back production, which causes the contraction in output and employment, all else equal. There
may also be adjustment costs to shifting toward less energy intensive methods of production, and
these could temporarily have a negative effect on output. Typically, the effect on output occurs
over a few quarters.2
The magnitude of an oil shock’s effect on the economy should depend on how much oil that
economy uses. As the ratio of energy use to GDP in the United States has declined over time, one
would expect the economic effects of an oil shock to lessen. This may help explain why the
recent oil shock has had a smaller economic effect than in the past.
The effects described thus far can be thought of as occurring on the supply side of the economy.
Oil shocks may also affect aggregate demand. When energy prices rise they involve an income
transfer from consumers to producers. Since producers are also consumers, aggregate demand is
likely to fall only temporarily as producers adjust their consumption to their now higher incomes.
This adjustment is likely to be longer when the income recipients are foreigners than when they
are Americans. A second effect on demand can be expected to occur because the rise in energy
prices will probably push up the overall price level because other prices do not fall immediately
in the face of a decline in demand. The increase in the price level will reduce the real value of the
available amount of money in the hands of buyers, and this reduction in the real money stock will
also reduce spending. A third effect on demand can occur if the rise in energy prices increases
uncertainty and causes buyers to defer purchases. This effect is also likely to be of a short run
nature. The magnitude of all three effects will depend on how much energy prices rise and how
long they remain high.
Rising oil prices also affect the international balance of payments in the short run. If the cost of
U.S. oil imports increases following a price rise, this constitutes a transfer in purchasing power
from U.S. consumers to foreign oil producers. How this affects the current account deficit (a
measure that primarily consists of the trade deficit) depends, in turn, on how foreign oil producers
decide to use this purchasing power. If they use it to purchase U.S. goods, then U.S. exports
would increase and there would be little effect on the current account deficit. If they use it to
purchase U.S. assets—whether corporate stocks, Treasury bonds, or by simply leaving the
revenue in a U.S. bank account—then it would represent an inflow of foreign capital to the
United States, which would increase the current account deficit.
For those oil-exporting countries that maintain a floating exchange rate against the dollar, the
dollar would be likely to depreciate following an oil shock relative to their currencies. But many
of the major oil-exporting countries maintain a fixed exchange rate against the dollar; for this
reason, the value of the dollar would be unlikely to be greatly affected by an oil shock in the short
run. In the long run, however, the real value of the dollar must fall to pay for the more expensive
oil imports; against countries who fix their nominal exchange rate to the dollar, this will occur
through relative price adjustment (i.e., higher inflation in oil producing countries than in the
United States). Short-run exchange rate adjustment may also occur more slowly because world oil
market transactions are made using the U.S. dollar.
If rising energy prices affect the economy through this transmission mechanism, then falling energy prices should
have the opposite effect on the economy: they should temporarily lower inflation and raise output, all else equal. Many
of the studies to follow find that this is not true, however.
Both the inflation and output effects of energy shocks are temporary: that is, once prices adjust,
the economy returns to full employment and its sustainable growth path.3 This observation yields
an important insight: it is not the level of energy prices that affects economic growth and
inflation, but rather the change in energy prices. Thus, if policymakers wish to mitigate the effect
of energy prices on output and inflation, they should be concerned with rising energy prices and
should not be concerned with “high” energy prices, even if the high prices are permanent. The
only permanent macroeconomic effect of higher energy prices is their negative effect on the terms
of trade. The “terms of trade” is a measure of standard of living that refers to the labor and capital
embodied in U.S. exports that can be exchanged for the labor and capital embodied in foreign
imports.4 It means that the United States has to give up more of the goods it produces than
previously to obtain a barrel of oil. Permanently higher energy prices lead to a one-time
permanent decline in the terms of trade and the standard of living of U.S. consumers, all else
Historically, formulating an effective policy response to oil shocks has been difficult.
Expansionary fiscal or monetary policy increases aggregate demand and inflationary pressures. In
typical downturns, monetary and fiscal policy can safely become expansionary without triggering
a significant increase in inflation because the fall in demand reduces inflationary pressures. In oil
shocks, policymakers must be simultaneously concerned with the fall in economic activity and
the rise in prices. By tackling one problem, they risk exacerbating the other. For example, if
policymakers use expansionary fiscal or monetary policy to offset the fall in output, prices may
rise further and inflationary expectations could become embedded. A key concern for
policymakers is whether the rise in prices remains isolated in energy prices or whether they
spread to other goods (often referred to as “core inflation”). This was the problem in the 1970s,
when inflation, which was already rising before the oil shocks, continued to accelerate following
the oil shock of 1973 until it reached double digits in 1974. Once the public came to expect
higher inflation, the subsequent expansionary policy measures had less and less of a positive
effect on aggregate demand, making the purported tradeoff between inflation and unemployment
less and less favorable. Following the second oil shock of 1979, a Federal Reserve that was
determined to stamp out double-digit inflation chose instead to tackle the inflationary pressures
caused by the oil shock by raising interest rates. This decision exacerbated the effect on output,
contributing to the most severe economic contraction since the Great Depression.
Another reason why policy responses have been unable to prevent oil shocks from leading to
recessions historically is because policy changes are hampered by lags in policy recognition,
implementation, and effectiveness. Because oil shocks are typically unpredictable events, policy
cannot be modified far enough ahead of time to prevent a downturn.
This point is not always explicitly made in the time series analyses reviewed below, which tend to end their estimates
at the last time lag that yields statistically significant data or arbitrarily cut off the estimates after a few lags to meet a
statistical criterion concerning the limit on the number of variables allowed.
See CRS Report RL32591, U.S. Terms of Trade: Significance, Trends, and Policy, by Craig K. Elwell.
The remainder of this report reviews the econometric literature on oil shocks, which has
attempted to quantitatively estimate how much of an effect oil prices have on macroeconomic
variables such as GDP growth, inflation, and unemployment. Technical terms are defined in a
glossary at the end of the report.5
Darby had one of the earliest econometric studies that attempted to estimate the economic effects
of oil shocks.6 His study aimed to determine what had caused the 1973-1975 recession. He
hypothesized that it could have been due to four causes: the removal of the Nixon price control
regime (because GDP was overstated during the regime), the breakdown in the Bretton Woods
exchange rate regime, the slowdown in money growth (contractionary monetary policy), or the
oil shock. He estimated that the 1973 oil shock caused a total cumulative decrease in GNP of
2.5%. Although the oil shock’s effect on the economy was statistically significant, statistical tests
could not rule out the possibility that it was the removal of price controls, rather than the oil
shock, that caused the recession.
The next year, Hamilton published what many would consider to be the seminal study on oil
shocks.7 He drew attention to the fact that all but one of the post-war recessions had been
preceded by a sharp rise in the price of oil, and set out to demonstrate statistically that, contrary to
conventional wisdom, it was these oil price rises that caused the recessions. He demonstrated that
oil prices, to use a term from economics, Granger-caused GNP.8 To prove that oil prices and GNP
were not both being determined by some third variable, he demonstrated that no other
macroeconomic variable Granger-caused oil prices. He estimated that a 10% increase in the price
of oil in this quarter would increase GNP by 0.04% in the next quarter, then decrease it by 0.07%
after two quarters, another 0.5% after three quarters, and 0.6% after a year compared to the level
GNP would have reached had the price of oil been constant.9 Considering that during the major
oil shocks prices rose by 20% in some quarters and rose for several quarters in a row, these
estimates suggest the effect of oil shocks on the economy are quite large. However, statistical
tests suggest that his equation was mis-specified or the relationship changed over time, and
A broader literature review can be found in Donald Jones, Paul Leiby, and Inja Paik, “Oil Price Shocks and the
Macroeconomy: What Has Been Learned Since 1996,” Energy Journal, vol. 25, no. 2, 2004, p. 1.
Michael Darby, “The Price of Oil and World Inflation and Recession,” American Economic Review, vol. 72, no. 4,
September 1982, p. 738. The study covered 1957:Q1-1976:Q4. The regression results had an R-squared of 0.9984 and
the oil price variables were jointly significant at the 5% level.
James Hamilton, “Oil and the Macroeconomy Since World War II,” Journal of Political Economy, vol. 91, no. 2,
1983, p. 228. The regression covered the period from 1948:Q2 to 1980:Q3 and the oil variables were jointly significant
at the 1% level.
Granger causation is a statistical test of causation based on the predictive power of past information. In this case, oil
Granger-causes GNP if past values of oil increase the predictive power of future values of GNP beyond what is
predicted by past values of GNP (and past values of any other variables included in the equation.) Oil is said to
Granger-cause GNP if the predictive power it adds is statistically significant. Of course, this test is not definitive, and is
subject to many of the shortcomings, such as omitted variable bias, described below in the Caveats section.
Quarterly data have not been annualized. Thus, cumulative effects for one year can be roughly calculated by adding
up quarterly effects. Technically, the estimates presented in the report are only mathematically accurate over small
changes, but this report uses 10% changes for illustrative purposes.
should be split in two at 1973. Surprisingly, although oil still Granger-caused GNP after 1973, he
estimated that it had a much smaller effect on GNP from 1973 to 1980, when the first two major
oil shocks occurred. Hamilton was one of the first to note that oil affected GNP with a lag—the
effect on GNP was nearly ten times larger after four quarters than it was after two quarters.
During the late 1980s and early 1990s, standard regression specifications no longer showed oil
shocks to have a substantial effect on economic growth. Several papers were written attempting
to explain why, using more sophisticated and complex mathematical relationships and statistical
Knut Mork was one of the first authors to find that in a standard regression, when extended
through 1988 and controlling for other macroeconomic factors, the effect of oil price changes on
the growth rate of gross national product (GNP) was now small sand statistically insignificant.10
In the mid-1980s, there had been a series of oil price declines, and Mork hypothesized that, unlike
oil price increases, price declines had little effect on the economy. His regressions confirmed his
hypothesis—when the distinction between price increases and decreases was made, the effect of
price increases on GNP growth doubled, whereas price declines had a small and statistically
insignificant effect. He estimated that a 10% temporary increase in the price of oil in this quarter
would lower the GNP growth rate by 0.31 percentage points after one quarter, another 0.15
percentage points after two quarters, 0.49 percentage points after three quarters, and 0.49
percentage points after four quarters.11
Lee, Ni, and Ratti re-confirmed that when newer data is added the effect of oil price increases on
economic growth using the standard linear relationship between oil price changes and economic
growth becomes statistically insignificant.12 They claimed that
the real oil price has not lost predictive power for growth in real GNP if appropriate account
is taken of oil shocks and the variability of real oil price movement. The basic idea is that an
oil shock is likely to have greater impact in an environment where oil prices have been stable
than in an environment where oil price movement has been frequent and erratic. (p. 42)
Knut Mork, “Oil and the Macroeconomy When Prices Go Up and Down,” Journal of Political Economy, vol. 97, no.
3, 1989, p. 740. The main regression covered 1949:Q1-1988:Q2 and had an R squared of 0.518. The oil price increase
variables were jointly significant at the 1% level, while the price decrease variables were jointly insignificant. These
results are extended through 1992 in Knut Mork, Oystein Olsen, and Hans Mysen, “Macroeconomic Responses to Oil
Price Increases and Decreases in Seven OECD Countries,” Energy Journal, vol. 15, no. 4, 1994, p. 19. The authors find
that both price increases and decreases reduce GDP growth, and these results are statistically significant. A 10%
increase in oil prices reduces growth by a cumulative 0.5 percentage points, and surprisingly a 10% price decrease
reduces growth by a cumulative 0.8 percentage points.
Balke, et al. (2002) used statistical tests to determine what third variable statistically explained why oil price
increases had a larger effect on growth than price decreases. They concluded that interest rates could explain the
asymmetry. They hypothesized that the role played by interest rates could reflect the “pricing in” of oil shock effects by
forward looking financial markets or delays in capital investment and balance sheet effects due to oil price uncertainty.
Nathan Balke, Stephen Brown, and Mine Yucel, “Oil Price Shocks and the U.S. Economy: Where Does the Asymmetry
Originate?” The Energy Journal, vol. 23, no. 3, 2002, p. 27.
Kiseok Lee, Shawn Ni, and Ronald Ratti, “Oil Shocks and the Macroeconomy: The Role of Price Variability,”
Energy Journal, vol. 16, no. 4, 1995, p. 39. Their regressions span 1950:Q3 to 1992:Q3. The variable real oil price
change was statistically insignificant but the oil price shock variable was significant at the 1% level.
When they include an “oil price shock” variable that “can be thought of as being a measure of
how different a given oil price movement is from the prior pattern”(p. 42) in the regression along
with an oil price change variable, their results become statistically significant.
Similarly, Ferderer believed that oil price volatility was the missing factor that could explain oil’s
macroeconomic effects and added a variable to capture volatility to his regressions that previous
studies lacked.13 He argued that volatility could be costly in terms of shifting resources across
sectors and causing investment uncertainty. He measured volatility as the standard deviation of
daily prices. Using industrial production growth as a proxy for economic growth (in order to
study the data over monthly intervals), he found that monthly oil price changes could statistically
“explain” 5.7%-18.5% of the fluctuations in industrial production, and oil price volatility could
explain an additional 11.7%-16.1% of the fluctuations. By contrast, monetary policy could
explain only 11.6%-12.0% of the fluctuations in industrial production. He confirmed Mork’s
findings that oil price increases had a greater effect on the economy than price decreases.
Hooker found that oil prices no longer Granger-cause economic growth or unemployment after
1973, even though all three oil shocks occurred during this period.14 His results held for a variety
of structural specifications. In reply to Hooker’s work, Hamilton suggested that the relationship
was not statistically significant after 1973 because many of the price increases since 1986 came
on the heels of even larger decreases.15 Hamilton doubted that these types of price increases
would affect the economy. He devised a net oil price increase variable to control for this
phenomenon, but still found smaller economic effects since 1973 and still did not find that his
new variable, “the net oil price increase,” Granger-caused economic growth.
Hamilton has posited that the reason standard regressions do not find that oil has a strong effect
on economic growth is due to mis-specification. If the effect of oil on the economy is best
represented by a non-linear mathematical relationship, then standard linear regressions may pick
up very weak and misleading effects. In a later paper, Hamilton demonstrated that non-linear
specifications suggest that oil has stronger effects than linear specifications.16 Unfortunately,
since there is an infinite number of non-linear specifications to choose from, there is no easy way
to identify the correct one. Hamilton also noted that regression results may be hampered because
the oil price can no longer be treated as exogenous, that is, it can now be driven by demand or
supply. Using the net oil price increase measure proposed in his earlier work (1996), he found that
a 10% increase in the price of oil (when it is not following a prior price decrease) in the current
quarter will lower GDP in the next quarter by 0.13%, another 0.13% two quarters later, 0.22%
J. Peter Ferderer, “Oil Price Volatility and the Macroeconomy,” Journal of Macroeconomics, vol. 18, no. 1, winter
1996, p. 1. His regression covered the period January 1970 to December 1990. His oil price volatility measure was
statistically significant at the 1% level and his measure for the level of oil prices was statistically insignificant. For
more recent work, see Hui Huo and Kevin Kleissen, “Oil Price Volatility and U.S. Macroeconomic Activity,” Federal
Reserve Bank of St. Louis Review, vol. 87, no. 6, November/December 2005, p. 669. They find that a 10% increase in
oil price volatility would reduce GDP growth by 0.2 percentage points in the next quarter. Their results are statistically
significant at the 5% level.
Mark Hooker, “What Happened to the Oil Price-Macroeconomy Relationship?” Journal of Monetary Economics,
vol. 38, 1996, p. 195. His regressions cover the period 1948:Q1-1994:Q2.
James Hamilton, “This is What Happened to the Oil Price-Macroeconomic Relationship,” Journal of Monetary
Economics, vol. 38, 1996, p. 215. His regressions cover the period 1948:Q1-1994:Q2.
James Hamilton, “What is an Oil Shock?” National Bureau of Economic Research working paper 7755, June 2000.
His regressions cover the period 1949:Q2-1999:Q4. Oil’s effects on growth were statistically insignificant after one and
two quarters, significant at the 10% level after three quarters, and at the 1% level after four quarters.
three quarters later, and 0.45% four quarters later. The sum of these effects is 0.2 percentage
points smaller than in Hamilton (1983).17
A recent paper by Jimenez-Rodriguez and Sanchez updates Mork’s, Hamilton’s, and Lee et al’s
respective work.18 Using standard vector autoregression methods, the authors find that a 10%
increase in the oil price reduces GDP growth in the U.S. by a cumulative 0.39 percentage points
after eight quarters. Using Mork’s variation, they find that a 10% oil price increase reduces
growth by 0.46 percentage points after eight quarters, but a 10% decrease increases growth by
only 0.11 percentage points. Using Hamilton’s net oil price measure increases the effect of a 10%
price increase to 0.54 percentage points after eight quarters. Using Lee’s method, which focuses
on price volatility, yields the largest results: the 10% price increase now reduces growth by 0.61
percentage points after eight quarters. These results are somewhat smaller than the earlier studies
A problem with many of these time-series studies is that they assume that the relationship
between oil prices and GDP is constant over time. But since energy use as a share of GDP has
fallen over time, one would expect oil prices to have a smaller effect on GDP as time passes.
Huntington takes this into account and, using panel data for 14 countries, estimates that a 10%
increase in oil prices in 1998 (latest year) would reduce U.S. GDP by 0.23%.19
Hooker attempted to estimate how oil shocks affect inflation when controlling for other
macroeconomic variables such as unemployment and price controls.20 He found that the effects of
oil price increases were much greater before 1981 than after that date. After 1981, he found that
oil price increases had only a small effect on the core inflation rate (excluding food and energy)
as measured by the personal consumption expenditures deflator. He estimated that a 10% increase
in the price of oil in the current quarter would lower core inflation by 1 percentage point in the
next quarter and raise it by 0.5 percentage points two quarters after the increase. Thus, he finds no
positive net effect on inflation.
An International Monetary Fund study uses vector autoregressions to estimate the effects of an oil
shock on a number of economic variables. It estimates that a $10 per barrel increase in oil prices
would reduce GDP by 0.25% after one year, with the negative effect eventually peaking at 0.5%
of GDP, relative to its baseline. The effect is not statistically significant, however. It would raise
inflation by 0.3 percentage points immediately, with the effect falling by half after two years. It
These results are updated in Keith Sill, “The Macroeconomics of Oil Shocks,” Federal Reserve Bank of Philadelphia,
Business Review, 2007:Q1, p. 21. He finds that a 10% increase in the net oil price reduces GDP, an effect that peaks a
year and a half later at about 1.5% of GDP. The effects are only statistically significant and negative for some quarters,
however. The author also finds that the net oil price does not have a statistically significant effect on headline inflation.
Applying these estimates to recent oil prices, he calculates that GDP was 3.2% lower than it would have been in the
absence of rising oil prices between 2004 and 2006.
Rebeca Jimenez-Rodriguez and Marcelo Sanchez, “Oil Price Shocks and Real GDP Growth: Empirical Evidence for
Some OECD Countries,” European Central Bank working paper 362, May 2004. Their results cover the period 1972:II2001:4 and the oil variables are jointly significant, with the exception of price decreases in the Lee model.
Hillard Huntington, “Shares, Gaps, and the Economy’s Response to Oil Disruptions,” Energy Economics, vol. 26,
2004, p. 415.
Mark Hooker, “Are Oil Shocks Inflationary? Asymmetric and Nonlinear Specifications versus Changes in Regime,”
Journal of Money, Credit, and Banking, vol. 34, no. 2, May 2002, p. 540. His regressions cover the period 1962:Q22000:Q1 and had an adjusted R squared of 0.92. The oil variables are independently significant at the 1% level, but
would also increase the U.S. trade deficit by 0.25% of GDP, but this effect shrinks and becomes
statistically insignificant within a year.21
Despite the remarkable historical coincidence between oil shocks and recessions, a strain of
research has suggested that there might nonetheless be some third force responsible for the
recessions. In particular, the research has tried to separate the effects of the oil shocks on the
economy from the effects of simultaneous changes in monetary policy. Some of the research has
concluded that had it not been for the changes in monetary policy, the oil shocks would have had
little effect on economic growth.
In an early paper, Gisser and Goodwin tried to capture the effects of monetary policy, fiscal
policy, and oil price changes on economic growth, inflation, and unemployment.22 They measure
monetary policy by the growth rate of the money supply and fiscal policy by the full employment
measure of federal expenditures. They estimated that a 10% increase in the price of oil in the
current quarter would reduce GNP growth by 0.2% in this quarter, another 0.01% in the next
quarter, 0.02% after two quarters, 0.3% after three quarters, and 0.5% after four quarters.
Similarly, a 10% increase in the price of oil is estimated to increase the inflation rate (as measured
by the GDP deflator) by 0.1% this quarter, and an additional 0.2% after one, two, three, and four
quarters. A 10% increase in the price of oil is estimated to increase the unemployment rate by
1.6% this quarter, decrease unemployment by 0.4% after one quarter, increase unemployment by
0.2% after two quarters, 2.4% after three quarters, and 3.2% after four quarters. (The
unemployment estimates seem questionably large, given the much milder estimated effects on
growth.) Monetary policy is estimated to have a much larger effect than the oil shocks: the effect
of a 10% change in the money supply is estimated to be about twice as large as a 10% change in
the oil price for GNP and about six times as large for the price level and unemployment. The
effects of fiscal policy on GNP and unemployment are smaller than the effects of oil price
changes, although larger than the effects on the price level. The authors also demonstrated that oil
Granger-caused GNP, the price level, and unemployment. Contrary to other studies, they also
found that after controlling for monetary and fiscal policy, there was no structural break in the oilGNP relationship after 1973. However, they do confirm that there was a break in the oil-price
level and oil-unemployment relationship.
Dotsey and Reid attempted to synthesize the work of Romer and Romer, which claimed that
contractionary monetary policy was the cause of post-war recessions, with the work of Hamilton,
surveyed above, which claimed that the recessions were caused by oil shocks.23 They estimated
International Monetary Fund, “How Will the Current Oil Price Shock Affect Global Imbalances?”, World Economic
Outlook, April 2006, Ch. 2. The estimates span the period 1979:Q2-2003:Q4. The study also found statistically
insignificant effects on the dollar exchange rate and interest rates.
Micha Gisser and Thomas Goodwin, “Crude Oil and the Macroeconomy: Tests of Some Popular Notions,” Journal
of Money, Credit, and Banking, vol. 18, no. 1, February 1986, p. 95. Their regressions results span the period 1961:Q11982:Q4 and had an R squared of 0.32 for GNP growth, 0.58 for inflation, and 0.23 for unemployment. The oil
variables are jointly significant at the 1% level in all three cases. The oil, monetary, and fiscal variables are jointly
significant at the 1% level for GDP growth, but statistically insignificant for inflation and unemployment.
Michael Dotsey and Max Reid, “Oil Shocks, Monetary Policy, and Economic Activity,” Federal Reserve Bank of
Richmond Economic Review, vol. 78, no. 4, July 1992, p. 14. For GNP, the regression covered the period 1955:31991:3 and the R squared was 0.32. The sum of oil price increase variables was statistically significant at the 1% level;
however, the sum of oil price decrease variables was statistically insignificant. For unemployment, the regression
that a 10% increase in the price of oil lowered GNP growth by a total of 0.7 percentage points
over the next four quarters. (Similar to Mork, they estimated the effects of price increases and
decreases separately, and found that price decreases had a smaller and statistically insignificant
effect on growth.) By contrast, a 1 percentage point increase in the federal funds rate was
estimated to reduce GDP growth by 0.1 percentage points over the next four quarters. They also
estimated the effect of oil price increases on unemployment and found that a 10% increase in oil
prices would increase the unemployment rate by a total of 0.4 percentage points over the next 24
Bernanke, et al. were interested in finding out what effects monetary policy changes had when
they were unanticipated.24 They chose to study oil shocks because these are one of the only
macroeconomic phenomena that most economists would agree are both unanticipated and
exogenous. First, they estimated the effect of a 10% increase in the price of oil when monetary
policy responds as it has historically. They estimated that over 24 months, GDP would fall by
3.1% and prices would rise by 0.09% relative to a baseline. To separate the effects of the oil
shock from the effects of the change in monetary policy, they then estimated a counter-factual
example where monetary policy does not respond to the oil price increase, which they represented
with a constant federal funds rate. In this case, GDP was estimated to rise by 1.3% and prices by
0.13%. They therefore concluded that oil price shocks have very little negative effect on the
economy; rather it is the monetary response to oil shocks that leads to the historical coincidence
between oil shocks and recessions.25
The work of Bernanke, et al. raises an interesting conceptual question: while the effects of oil
shocks and monetary policy can be statistically separated, can they be separated in reality?
Bernanke, et al. attribute the tightening of monetary policy following oil shocks as the Fed’s
response to the increase in inflationary pressures that oil shocks are commonly believed to cause.
Commenting on the Bernanke paper, Sims points out that the assumption that monetary policy
could remain unchanged in response to an increase in inflationary pressures is not a sustainable
policy, and thus falls prey to the Lucas critique (see below). It is unlikely that private individuals
would have no reaction to the implementation of an unsustainable policy, making the statistical
separation of oil price effects from monetary effects problematic.26 This would suggest that one
can reasonably question whether there is a practical distinction between attributing a recession to
an oil shock or attributing it to the monetary response to an oil shock.
Hamilton and Herrera pursue this line of reasoning in a critique of the Bernanke paper.27 While
Bernanke’s regressions can mechanically be interpreted to imply that monetary policy could
covered the period 1950:1-1990:12 and had an R squared of 0.977. The sum of the oil variables was significant at the
Ben Bernanke, Mark Gertler, and Mark Watson, “Systematic Monetary Policy and the Effects of Oil Price Shocks,”
Brookings Papers on Economic Activity 1, 1997, p. 91. Their regressions cover the period 1965-1995. None of their
results are statistically significant.
Using similar methods, Ferderer (1996) found the opposite results: the effects of oil shocks were larger than the
effects of monetary policy. See the section titled “Extensions.”
Christopher Sims, “Comments,” Brookings Papers on Economic Activity 1, 1997, p. 146. To address this criticism,
Bernanke, et al. also run simulations in which the federal funds rate is held constant but expectations are assumed to
adjust more quickly. Under this scenario, output still rises and inflation rises slightly more quickly.
James Hamilton and Ana Maria Herrera, “Oil Shocks and Aggregate Macroeconomic Behavior: The Role of
Monetary Policy,” Journal of Money, Credit, and Banking, forthcoming.
prevent a recession, Hamilton and Herrera point out that these regressions would imply that the
federal funds rate would have to have been an improbable 9 percentage points lower in 1973 to
prevent a recession. Using the Lucas critique, it is unlikely that private individuals’ expectations
would have remained unchanged in light of such a significant policy change. Hamilton and
Herrera also argue that Bernanke et al. underestimate the effects of oil shocks because they use
too short a lag length. Bernanke et al. assume that changes in oil prices affect the economy for the
next seven months, whereas Hamilton and Herrera suggest a lag length of at least 12 months
would be more appropriate since many works find the largest economic effects of oil price
changes to come after three and four quarters. In particular, by using a longer lag than Bernanke,
they find that countering oil shocks with expansionary monetary policy has much larger effects
on inflation since monetary policy affects inflation with a significant lag.
A related strain of research studied the effects of oil shocks on employment. Since economic
growth has a strong effect on unemployment in the short run, one would expect oil shocks to
affect unemployment if they affect economic growth.28
Carruth, Hooker, and Oswald estimated that oil shocks had a larger effect on unemployment than
economic growth.29 They showed that oil shocks Granger-caused unemployment (unlike GDP30)
up to the present (1995) and estimated that a 10% increase in the price of oil would increase the
unemployment rate by 0.2 percentage points. Although this is a relatively small effect, they found
that the effect of oil price changes on unemployment before 1978 was more than three times
larger. They found that their model, based on oil prices and interest rates, forecasts unemployment
more accurately than commercial forecasts.
Davis and Haltiwanger focused on oil’s effect on employment in the manufacturing sector, broken
down by industry.31 They hypothesize that oil price changes have distinct “aggregate” effects and
“allocative” effects on manufacturing employment. The aggregate effects on employment are
caused by the slowdown in GDP growth that oil price increases cause. The allocative effects on
employment come from the fact that some industries are harmed more than others—and some are
actually helped—by a price increase. Thus, some jobs are shifted from one industry to another so
that the net allocative effect of an oil price change on employment is zero. They found that
a unit standard deviation positive oil shock triggers the destruction of an extra 290,000
production worker jobs and the creation of an extra 30,000 jobs in the first two years after
the shock.... After four years, the net employment response to a unit positive oil shock is
Two papers already reviewed in this report investigated the effects of oil shocks on unemployment. See Gisser and
Goodwin (1986) and Dotsey and Reid (1992).
Alan Carruth, Mark Hooker, and Andrew Oswald, “Unemployment Equilibria and Input Prices: Theory and
Evidence from the United States,” The Review of Economics and Statistics, vol. 80, no. 4, 1998, p. 621. The regression
covered the period from 1955:Q4 to 1995:Q2. The R squared was 0.837 and the oil variable was statistically significant
at the 1% level.
These results are curious since short-term fluctuations in unemployment are usually thought to be caused by
fluctuations in GDP. Otherwise, the fluctuations would represent changes in the natural rate of unemployment, and
there has not been any well-known link established between the price of oil and the natural rate of unemployment.
Steven Davis and John Haltiwanger, “Sectoral Job Creation and Destruction Responses to Oil Price Changes,”
National Bureau of Economic Research, working paper 7095, April 1999. The regression spanned from 1972:2 to
1988:4 and the oil variables were individually insignificant at the 5% level, except for the seven-quarter lag variable.
only 60,000 fewer jobs, but the gross reallocation response amounts to 410,000 jobs or more
than 3 percent of employment.
By comparison, they estimated that a unit standard deviation tightening in monetary policy leads
to a net loss of 140,000 manufacturing jobs after two years. Looking at the data on an industry
level, they estimated that the effects differ greatly by industry depending on the characteristics of
that industry. For example, categorized by the energy intensity of production, the decline in
employment was almost twice as large for industries in the 90th percentile than industries in the
10th percentile. Because their study excluded the service sector, which accounts for most
employment, their results cannot be meaningfully extrapolated to judge the effects of oil price
changes on overall unemployment.32
Quantitatively estimating the effect of oil shocks on the economy is more difficult than it sounds.
In the sciences, statistical robustness is obtained by running numerous controlled randomized
experiments in order to sift out randomness in the data to identify the true relationships between
variables. If uncertainty emerges concerning the role one factor plays, one can change the
experiment to isolate that factor’s effect. In macroeconomics, experiments are not controlled and
they cannot be run over and over again. Since World War II, we have had only 10 “experiments”
with recession, and it is highly doubtful that the U.S. economy is the same test case today as it
was in, say, 1957. Instead, economists must hypothesize the relationships between different
economic factors expressed through mathematical relationships and compare the historical
correlation of those variables to see if the hypothesis holds. If the mathematical relationship
chosen to represent the relationship is incorrect or changes over time, or other variables that have
an effect are missing from the regression, then the estimates will be incorrect. Studies that attempt
to identify more sophisticated relationships than the simple linear one can be accused of “data
mining” to find the biggest (or smallest) effect possible.
There are some common pitfalls that lead to econometric studies giving “biased” or incorrect
estimates for the relationship between variables. Some of these pitfalls are nearly impossible to
avoid, particularly in macroeconomics. This report will review four such pitfalls—omitted
variable bias, structural misspecification, problems with endogeneity, and the Lucas Critique.
These problems suggest that econometric estimates, while useful, should always be considered
with caution. In addition, even when measured accurately, there is a question of statistical
A common problem that econometricians try to avoid is omitted variable bias. If a regression does
not control for a factor that affects one’s dependent variable, then the estimated effects of the
explanatory variables that are included will be biased. The importance of explanatory variables
positively correlated with the missing variable will be overstated, the importance of negatively
correlated variables will be understated. This is a particularly important problem in economics
In an earlier paper, Loungani demonstrated that the reallocation of employment across industries led to an increase in
unemployment only when the reallocation was caused by oil shocks. Prakash Loungani, “Oil Price Shocks and the
Dispersion Hypothesis,” Review of Economics and Statistics, vol. 68, no. 3, August 1986, p. 536.
precisely because experiments cannot be re-run or truly randomized. In macroeconomics, it is
often infeasible to include all of the relevant factors in a regression—there are simply too many
factors influencing the economy to capture them all. Most of the regressions reviewed in this
report used oil and only a few other key economic variables as explanatory variables. Yet a look
at the 2001 recession points to many additional factors—many unquantifiable in nature—that
hampered the U.S. economy. These include the effects of September 11, the corporate accounting
scandals, the large decline in the stock market, and so on. If these factors were not included in a
regression, the regression would attribute their effects instead to the recent run-up in oil prices
(and any other correlated explanatory variables), overstating oil’s importance.
The fact that a study finds no relationship between two variables does not mean that no
relationship exists in reality. Likewise, studies can identify relationships where no relationship in
fact exists. Regressions relate data series to one another according to some mathematical
function; if that mathematical function does a poor job of describing the relationship in reality,
then the results will be artificially weak. The problem is that there is an infinite number of
mathematical equations that can be used to express a relationship and often there is not a strong
theoretical reason for favoring any one. As a result, econometricians most frequently assume a
linear relationship between the variables (often after taking the natural logarithm of the data); this
assumption is made more out of convention than due to any strong reason for preferring a linear
relationship over any other.33
Thus, in most of the studies surveyed above, a 10% change in the oil price is estimated to have an
effect that is 10 times greater than a 1% change. This assumption seems unlikely to represent
reality accurately. Experience and common sense suggest that while the major oil shocks have
had significant effects on the economy, small and fleeting movements in the oil price have
virtually no impact. Yet a linear relationship would scale these two events equally. While theory
can point to alternatives, unfortunately, representing the statement “oil price changes only matter
if they are steep, sudden, long-lasting, and do not reverse previous price movements in the
opposite direction” in mathematical form is neither simple nor straightforward.
All of the studies reviewed in this report used time series analysis to estimate the effects of oil
prices. Time series analysis is vulnerable to a special type of structural misspecification: it must
assume that the relationship between the explanatory variables and the dependent variable is
constant over time. For example, a 10% increase in the oil price must have the same economic
effects in 1952 as it has in 2002. But if oil affects the economy through the production process,
oil shocks would be expected to have a smaller effect on the economy if the economy became
more energy efficient in terms of energy use relative to GDP. Historically, energy consumption
per dollar of GDP has dropped significantly over the past three decades, with the economy now
using less than half as much energy per dollar as it did in the 1970s. In order to correct for such
changes, statistical tests are used to look for structural breaks. The time series can then be divided
into sub-samples, each estimated separately. But if the series is divided, the study loses a degree
of statistical robustness because it is based on fewer observations.
There are many other problems of this type that make econometric estimates problematic. For example, the standard
regression method is valid only if the error terms are assumed to be normally distributed. Otherwise, different methods
must be used.
Virtually all important macroeconomic phenomena are interrelated, usually in complex ways.
This makes econometric estimation difficult. In the simple regression, causation runs from the
explanatory variables to the dependent variable. For the simple regression method to be valid,
neither the dependent variable, nor another explanatory variable, nor some missing or unobserved
variable can influence any explanatory variable. This qualification seldom if ever holds in
macroeconomics, which means that more complex, and less straightforward, econometric
methods must be used or the estimations will be invalid. Such methods exist, but have
shortcomings of their own, and are not always used by the econometrician.
For example, the exchange rate, interest rates, and fiscal policy all influence phenomena such as
economic growth, inflation, and unemployment. But, in turn, their values are determined by the
very phenomena that they influence; this is how markets reach equilibrium. Thus, to determine
the effects of a change in monetary or fiscal policy on economic growth, one cannot simply run a
regression in which economic growth is the dependent variable and the budget deficit and interest
rates are independent variables. Even oil prices, which might seem to be a good candidate for
exogeneity because of the role played by OPEC, are likely to be endogenously determined.
Although OPEC has some control over the supply side of the market, price is also determined on
the demand side, which is influenced by factors such as growth in domestic income.34
One way of avoiding endogeneity problems in time series analysis is by using a statistical method
called vector autoregression, which simply assumes that all variables affect each other. Two
shortcomings have been raised with this method that are worth mentioning here. First, critics
complain that vector autoregression is atheoretical: the method eschews any attempt to identify
relationships between variables in theoretical terms. While this makes it a more flexible method,
its critics argue that no theory is not the same as the right theory. Econometrics will only lead to
accurate results if the underlying theory accurately describes reality. Second, there is a tradeoff
between the number of explanatory variables one can have and the number of observations with
which one is working. (With time series data, the number of observations is limited to how far
back in time one is willing to go, which raises another set of problems discussed in the previous
sub-section.) Because all of the variables must be regressed on each other and because the use of
time lags creates more variables, in practice vector autoregressions can have only a few
explanatory variables, as opposed to traditional macroeconomic models which have had as many
Many econometric estimations of macroeconometric phenomena fall prey to the “Lucas Critique”
set out by Nobel Laureate Robert Lucas. Econometric estimates derived from historical data,
particularly when they include variables that policymakers can influence, implicitly assume that
the future will be similar to the past. One of Robert Lucas’ main contributions to economics was
the development of the theory of “rational expectations,” in which he argued that, contrary to
much mainstream economic theory of the time, economic theory should always be based on the
Kilian argues that oil supply shocks are likely to have a different effect on the economy than demand shocks, and
uses statistical methods to try to differentiate between the two. He argues that the recent rise in price has been driven by
demand, not supply. Lutz Killian, “Not All Oil Price Shocks Are Alike,” University of Michigan, working paper, May
assumptions that private individuals are fully informed and act rationally to maximize their selfinterests. If people adjust their expectations when circumstances change, the future is unlikely to
be the same as the past. Thus, many econometric estimates are inconsistent with rational
expectations. For example, by looking at historical patterns, one can use econometric analysis to
estimate the effects of changes in the federal funds rate on economic growth. For the estimated
effect to be valid in forecasting future behavior, one must assume that individuals do not change
their behavior or learn from past mistakes. Taken literally, this would lead to perverse predictions.
For example, if individuals were fooled by a “monetary surprise” (i.e., an unanticipated and
opportunistic change in monetary policy) in the past, this type of econometric modeling would
predict that they would continue to be fooled indefinitely in the future.
Furthermore, when making predictions about the effects of policy alternatives, one can assume a
policy variable to take any value. Yet if this value had occurred historically, because of the role of
expectations, it could have changed all of the other estimated parameters in the regression. If this
were true, the model would have little predictive power. For example, if policymakers had always
responded to oil shocks by sharply tightening monetary policy, an econometric model might
suggest that oil shocks have little effect on inflation. An econometrician could then use his model
to demonstrate that an expansionary monetary policy could be employed to cope with oil shocks
with little effect on inflation. Yet had this policy been employed historically, expectations might
have adjusted (when people saw oil prices rise, they would anticipate all other prices to rise) to
make oil shocks far more inflationary.
Even if an econometric study avoided all the problems discussed above and perfectly represented
reality, simply taking estimates at face value tells only half of the story. As well as worrying about
the size of an estimate, statisticians are concerned with its statistical robustness. A study may find
that oil shocks have a very large effect on the economy, but if there is significant unexplainable
variation in the sample, one should be skeptical about the results. This report has reported two
common measures of robustness: the statistical significance of specific explanatory variables and
the R-squared of the study as a whole. Statistical significance is determined by how much the
sample data varies from the best estimate of the relationship between the dependent and
explanatory variables. An estimate is statistically significant at, say, the 1% confidence level if 99
out of 100 samples will be different from zero. The R-squared measures how much of the
variation in the dependent variable can be explained by the explanatory variable; if none of the
variation can be explained the R-squared would be zero, if all can be explained it would be one.
All of the studies reviewed in this report found oil shocks to have some effect on the economy.
There is a fair degree of consensus surrounding the range of estimates: for comparable studies,
the cumulative effect of a one-quarter, 10% increase in oil prices was to lower economic growth
by 0.2-1.1% over the next four quarters compared with GDP under a baseline in which the oil
price does not change (see Table 1). The effect takes place over a number of quarters, with
research typically finding weaker effects at first. The magnitude of these estimates suggests that
normal fluctuations in the price of oil would cause only minor fluctuations in economic growth.
However, the estimates suggest that major oil shocks, in which oil prices rise for several
consecutive quarters, often by more than 10%, could lead to recessions, all else equal. Only the
study by Bernanke et al. dissents from this conclusion by claiming that while oil shocks have
historically had a large negative effect on economic growth, the effect is attributable to the
monetary response to the shock, rather than the shock itself. Hooker (2002) was unable to find oil
to have any effect on core inflation.
Although the magnitude of the estimates is large enough to make oil shocks a policy concern, the
results are not statistically robust enough to silence all doubts. Oil prices no longer Granger-cause
economic growth in straightforward ways. The effect of oil price changes on the economy was
statistically insignificant in many studies. Some studies had low R-squared values, which means
that many of the determinants of economic activity remain unexplained. Studies which attempted
to identify more sophisticated relationships than the simple linear one could be accused of “data
mining” to find the biggest (or smallest) effect possible.
Furthermore, every study that explored the issue found that oil’s broad effects on the economy
were waning, and more recent studies tended to find oil to have smaller effects. Particularly
puzzling was that many studies found oil to have stronger economic effects before the mid-1970s,
despite the fact that all of the major oil shocks occurred since the mid-1970s.
This report was limited to surveying studies that specifically focused on oil’s impact on the
economy. Many macroeconometric studies not reviewed have focused on other determinants of
economic activity, such as monetary policy, and have neglected the role of oil entirely. It is fair to
say that some economists remain unconvinced that oil plays a crucial role in the business cycle.
All macroeconometric studies are prone to a number of unavoidable pitfalls. If they were reliable
and if our understanding of the economy were better, macroeconomic policy concerns would
vanish. It is not merely a question of developing more sophisticated statistical techniques; some
pitfalls stem from the unpredictability of human nature that ultimately determines economic
outcomes. Nevertheless, these studies contribute valuable insight into important phenomena such
as oil shocks.
. Summary of Findings
Gisser and Goodwin
Dotsey and Reid
Lee, Ni, Ratti (1995)
Bernanke et al (1997)
1973 oil shock reduced cumulative GNP by 2.5%
GNP responds to oil price change with lag; oil had larger effect on GDP before 1973; 10%
oil price increase reduces GNP by 1.1% over the next year
controls for monetary policy and fiscal policy; finds monetary policy to have larger effect
than oil price change; 10% oil price increase reduces GNP by 1.0% over the next year
only oil price increases have significant effect on output, price decreases have negligible
effect; 10% oil price increase reduces GNP growth by 0.36 percentage points over the next
oil price increase had larger effect on GNP than monetary policy; 10% oil price increase
reduces GNP growth by 0.7 p.p. over the next year
oil price changes only affect GNP if they persist
controlling for oil price volatility helps explain relationship between oil price increases and
argues that oil shocks do not cause recessions; rather, the response of monetary policy to
oil shocks causes recessions
Carruth et al (1998)
oil prices do not have significant effect on GNP, but they have a significant effect on
unemployment; 10% oil price increase increases unemployment by 0.2 p.p. over the next
Davis and Haltiwanger oil price increases both destroy manufacturing jobs and shift jobs across industries; one
standard deviation oil price increase destroys 290,000 and creates 30,000 manufacturing
demonstrates that non-linear models do a better job explaining the relationship between oil
prices and output than linear models; 10% oil price net increase reduces GNP by 0.9% over
the next year
does not find oil price increases to have any significant effect on core inflation; 10% oil price
increase reduces core inflation by 0.5% over the next two quarters
effect of oil price on economy is greater if economy uses more energy; 10% oil price
increase reduces GDP by 0.2% in 1998
Jimenez-Rodriguez and updates Hamilton’s, Mork’s, and Lee et al’s findings using recent data; 10% oil price increase
reduces GDP growth by 0.4-0.6 p.p. over next two years
Notes: p.p.= percentage points. Unless otherwise noted, all estimates are compared to the economic variable
under a baseline scenario in which the oil price does not change.
the percentage of samples that will contain the true unobservable value. For example, at
the 1% confidence level, the sample will contain the true value 99% of the time. (For a
discussion, see section titled “Robustness of Results.”)
using the same data set to estimate several different models in a search to find the “best”
model, resulting in biased estimates.
the variable whose behavior the regression is attempting to explain in terms of other
variables which influence it.
using statistical methods to explain economic phenomena by relating a variable to other
an explanatory variable that is determined by another variable in the equation or
correlated with the equation’s error term. (For a discussion, see section titled “Problems
an explanatory variable that is not determined by any other variable in the equation and is
not correlated with the equation’s error term. (For a discussion, see section titled
“Problems with Endogeneity”)
a measure of the economic output generated within the United States.
GDP (gross domestic
GNP (gross national a measure of the economic output generated by American citizens.
Granger causation— a variable is said to Granger
-cause another variable if the first variable has predictive
power for future values of the latter variable at statistically significant levels when past
values of the latter variable have been taken into account.
This glossary draws heavily on Jeffrey Woolridge, Introductory Econometrics, South-Western College Publishing
a set of variables are said to be jointly significant if they jointly differ from zero at a given
a statistical method to “explain” changes in a variable by comparing changes in that
variable to changes in independent variables.
the percentage of the variation in the dependent variable that can be explained by the
explanatory variables. (For a discussion, see section titled “Robustness of Results.”)
a common measure of variance in a sample; “one standard deviation” is a useful
measurement standard when estimating changes in variables because it always represents
the same value when a sample has a normal distribution.
a variable is said to be statistically significant if it differs from zero at a given confidence
level. (For a discussion, see section titled “Robustness of Results.”)
a situation where the relationship between dependent and explanatory variables is not
constant over time (For a discussion, see section titled “Structural Misspecification.”)
choosing a mathematical function that is the best representation of the relationship in
reality. (For a discussion, see section titled “Structural Misspecification.”)
data for a set of variables that spans a time period.
Specialist in Macroeconomic Policy