Real Business Cycle Models:
Past, Present, and Future∗
In this paper I review the contribution of real business cycles models
to our understanding of economic ﬂuctuations, and discuss open issues in
business cycle research.
∗I thank Martin Eichenbaum, Nir Jaimovich, Bob King, and Per Krusell for their comments,
Lyndon Moore and Yuliya Meshcheryakova for research assistance, and the National Science
Foundation for ﬁnancial support.
†Northwestern University, NBER, and CEPR.
Finn Kydland and Edward Prescott introduced not one, but three, revolutionary
ideas in their 1982 paper, “Time to Build and Aggregate Fluctuations.” The ﬁrst
idea, which builds on prior work by Lucas and Prescott (1971), is that business
cycles can be studied using dynamic general equilibrium models. These models
feature atomistic agents who operate in competitive markets and form rational
expectations about the future. The second idea is that it is possible to unify
business cycle and growth theory by insisting that business cycle models must be
consistent with the empirical regularities of long-run growth. The third idea is
that we can go way beyond the qualitative comparison of model properties with
stylized facts that dominated theoretical work on macroeconomics until 1982.
We can calibrate models with parameters drawn, to the extent possible, from
microeconomic studies and long-run properties of the economy, and we can use
these calibrated models to generate artiﬁcial data that we can compare with actual
It is not surprising that a paper with so many new ideas has shaped the
macroeconomics research agenda of the last two decades. The wave of models that
ﬁrst followed Kydland and Prescott’s (1982) work were referred to as “real business
cycle” models because of their emphasis on the role of real shocks, particularly
technology shocks, in driving business ﬂuctuations. But real business cyle (RBC)
models also became a point of departure for many theories in which technology
shocks do not play a central role.
In addition, RBC-based models came to be widely used as laboratories for
policy analysis in general and for the study of optimal ﬁscal and monetary pol-
icy in particular.1 These policy applications reﬂected the fact that RBC models
1 See Chari and Kehoe (1999) for a review of the literature on optimal ﬁscal and monetary
policy in RBC models.
represented an important step in meeting the challenge laid out by Robert Lucas
(Lucas (1980)) when he wrote that “One of the functions of theoretical economics
is to provide fully articulated, artiﬁcial economic systems that can serve as labora-
tories in which policies that would be prohibitively expensive to experiment with
in actual economies can be tested out at much lower cost. [...] Our task as I see
it [...] is to write a FORTRAN program that will accept speciﬁc economic policy
rules as ‘input’ and will generate as ‘output’ statistics describing the operating
characteristics of time series we care about, which are predicted to result from
In the next section I brieﬂy review the properties of RBC models. It would
have been easy to extend this review into a full-blown survey of the literature. But
I resist this temptation for two reasons. First, King and Rebelo (1999) already
contains a discussion of the RBC literature. Second, and more important, the
best way to celebrate RBC models is not to revel in their past, but to consider
their future. So I devote section III to some of the challenges that face the theory
ediﬁce that has built up on the foundations laid by Kydland and Prescott in 1982.
Section IV concludes.
2. Real Business Cycles
Kydland and Prescott (1982) judge their model by its ability to replicate the
main statistical features of U.S. business cycles. These features are summarized
in Hodrick and Prescott (1980) and are revisited in Kydland and Prescott (1990).
Hodrick and Prescott detrend U.S. macro time series with what became known as
the “HP ﬁlter.” They then compute standard deviations, correlations, and serial
correlations of the major macroeconomic aggregates.
Macroeconomists know their main ﬁndings by heart. Investment is about three
times more volatile than output, and nondurables consumption is less volatile
than output. Total hours worked and output have similar volatility. Almost all
macroeconomic variables are strongly procyclical, i.e. they show a strong con-
temporaneous correlation with output.2 Finally, macroeconomic variables show
substantial persistence. If output is high relative to trend in this quarter, it is
likely to continue above trend in the next quarter.
Kydland and Prescott (1982) ﬁnd that simulated data from their model show
the same patterns of volatility, persistence, and comovement as are present in
U.S. data. This ﬁnding is particularly surprising, because the model abstracts
from monetary policy, which economists such as Friedman (1968) consider an
important element of business ﬂuctuations.
Instead of reproducing the familiar table of standard deviations and correla-
tions based on simulated data, I adopt an alternative strategy to illustrate the
performance of a basic RBC model. This strategy is similar to that used by the
Business Cycle Dating Committee of the National Bureau of Economic Research
(NBER) to compare diﬀerent recessions (see Hall et al. (2003)) and to the meth-
ods used by Burns and Mitchell (1946) in their pioneer study of the properties of
U.S. business cycles.
I start by simulating the model studied in King, Plosser, and Rebelo (1988) for
5,000 periods, using the calibration in Table 2, column 4 of that paper. This model
is a simpliﬁed version of Kydland and Prescott (1982). It eliminates features that
are not central to their main results: time-to-build in investment, non-separable
utility in leisure, and technology shocks that include both a permanent and a
transitory component. I detrend the simulated data with the HP ﬁlter. I identify
recessions as periods in which output is below the HP trend for at least three
2 A notable exception is the trade balance which is countercyclical. See Baxter and Crucinni
3 Interestingly, applying this method to U.S. data produces recession dates that are similar to
Figure 1 shows the average recession generated by the model. All variables are
represented as deviations from their value in the quarter in which the recession
starts, which I call period zero. This ﬁgure shows that the model reproduces the
ﬁrst-order features of U.S. business cycles. Consumption, investment, and hours
worked are all procyclical. Consumption is less volatile than output, investment
is much more volatile than output, and hours worked are only slightly less volatile
than output. All variables are persistent. One new piece of information I obtain
from Figure 1 is that recessions in the model last for about one year, just as in
the U.S. data.
3. Open Questions in Business Cycle Research
I begin by brieﬂy noting two well-known challenges to RBC models. The ﬁrst is
explaining the behavior of asset prices. The second is understanding the Great
Depression. I then discuss research on the causes of business cycles, the role of
labor markets, and on explanations for the strong patterns of comovement across
The Behavior of Asset Prices
Real business cycle models are arguably suc-
cessful at mimicking the cyclical behavior of macroeconomic quantities. However,
Mehra and Prescott (1985) show that utility speciﬁcations common in RBC mod-
els have counterfactual implications for asset prices. These utility speciﬁcations
are not consistent with the diﬀerence between the average return to stocks and
those chosen by the NBER dating committee. The NBER dates for the beginning of a recession
and the dates obtained with the HP procedure (indicated in parentheses) are as follows: 1948-
IV (1949-I), 1953-II (1953-IV), 1960-II (1960-III), 1969-IV (1970-I), 1973-III (1974-III), 1981-III
(1981-IV), 1990-III (1990-IV), and 2001-I (2001-III). The NBER dates include 1980-I, which
is not selected by the HP procedure. In addition, the HP procedure includes three additional
recessions starting in 1962-III, 1986-IV, 1995-I. None of the latter episodes involved a fall in
bonds. This “equity premium puzzle” has generated a voluminous literature, re-
cently reviewed by Mehra and Prescott (2003).
Although a generally accepted resolution of the equity premium puzzle is cur-
rently not available, many researchers view the introduction of habit formation
as an important step in addressing some of the ﬁrst-order dimensions of the puz-
zle. Lucas (1978)-style endowment models, in which preferences feature simple
forms of habit formation, are consistent with the diﬀerence in average returns
between stocks and bonds. However, these models generate bond yields that are
too volatile relative to the data.4
Boldrin, Christiano, and Fisher (2001) show that simply introducing habit
formation into a standard RBC model does not resolve the equity premium puz-
zle. Fluctuations in the returns to equity are very small, because the supply of
capital is inﬁnitely elastic. Habit formation introduces a strong desire for smooth
consumption paths, but these smooth paths can be achieved without generating
ﬂuctuations in equity returns. Boldrin, et al. (2001) modify the basic RBC model
to reduce the elasticity of capital supply. In their model investment and con-
sumption goods are produced in diﬀerent sectors and there are frictions to the
reallocation of capital and labor across sectors. As a result, the desire for smooth
consumption introduced by habit formation generates volatile equity returns and
a large equity premium.
What Caused the Great Depression?
The Great Depression was the most
important macroeconomic event of the 20th century. Many economists interpret
the large output decline, stock market crash, and ﬁnancial crisis that occurred
between 1929 and 1933 as a massive failure of market forces that could have been
4 Early proponents of habit formation as a solution to the equity premium puzzle include
Sundaresan (1989), Constantinides (1990), and Abel (1990). See Campbell and Cochrane (1999)
for a recent discussion of the role of habit formation in consumption-based asset pricing models.
prevented had the government played a larger role in the economy. The dramatic
increase in government spending as a fraction of GDP that we have seen since the
1930s is partly a policy response to the Great Depression.
In retrospect, it seems plausible that the Great Depression resulted from an
unusual combination of bad shocks compounded by bad policy. The list of shocks
includes large drops in the world price of agricultural goods, instability in the
ﬁnancial system, and the worst drought ever recorded. Bad policy was in abundant
supply. The central bank failed to serve as lender of last resort as bank runs forced
many U.S. banks to close. Monetary policy was contractionary in the midst of
the recession. The Smoot-Hawley tariﬀ of 1930, introduced to protect farmers
from declines in world agricultural prices, sparked a bitter tariﬀ war that crippled
international trade. The federal government introduced a massive tax increase
through the Revenue Act of 1932. Competition in both product and labor markets
was undermined by government policies that permitted industry to collude and
increased the bargaining power of unions. Using rudimentary data sources to sort
out the eﬀects of these diﬀerent shocks and diﬀerent policies is a daunting task,
but signiﬁcant progress is being made.5
What Causes Business Cycles?
One of the most diﬃcult questions in macro-
economics asks, what are the shocks that cause business ﬂuctuations? Long-
standing suspects are monetary, ﬁscal, and oil price shocks. To this list Prescott
(1986) adds technology shocks, and argues that they “account for more than half
the ﬂuctuations in the postwar period with a best point estimate near 75%”.
The idea that technology shocks are the central driver of business cycles is con-
troversial. Prescott (1986) computes total factor productivity (TFP) and treats
it as a measure of exogenous technology shocks. However, there are reasons to
5 See Christiano, Motto, and Rostagno (2005), Cole and Ohanian (1999, 2004), and the
January 2002 issue of the Review of Economic Dynamics and the references therein.
distrust TFP as a measure of true shocks to technology. TFP can be forecast us-
ing military spending (Hall (1988)), or monetary policy indicators (Evans (1992)),
both of which are variables that are unlikely to aﬀect the rate of technical progress.
This evidence suggests that TFP, as computed by Prescott, is not a pure exoge-
nous shock, but has some endogenous components. Variable capital utilization,
considered by Basu (1996) and Burnside, Eichenbaum, and Rebelo (1996); vari-
ability in labor eﬀort, considered by Burnside, Eichenbaum, and Rebelo (1993);
and changes in markup rates, considered by Jaimovich (2004a), drive important
wedges between TFP and true technology shocks. These wedges imply that the
magnitude of true technology shocks is likely to be much smaller than that of the
TFP shocks used by Prescott.
Burnside and Eichenbaum (1996), King and Rebelo (1999), and Jaimovich
(2004a) argue that the fact that true technology shocks are smaller than TFP
shocks does not imply that technology shocks are unimportant. Introducing mech-
anisms such as capacity utilization and markup variation in RBC models has two
eﬀects. First, these mechanisms make true technology shocks less volatile than
TFP. Second, they signiﬁcantly amplify the eﬀects of technology shocks. This
ampliﬁcation allows models with these mechanisms to generate output volatility
similar to the data with much smaller technology shocks.
Another controversial aspect of RBC models is the role of technology shocks
in generating recessions. The NBER business cycle dating committee deﬁnes a
recession as “a signiﬁcant decline in economic activity spread across the economy,
lasting more than a few months, normally visible in real GDP, real income, em-
ployment, industrial production, and wholesale-retail sales” (Hall et al. (2003)).
Figure 2 shows a histogram of annualized quarterly growth rates of U.S. real GDP.
In absolute terms, output fell in 12 percent of the quarters between 1947 and 2005.
Most RBC models require declines in TFP in order to replicate the declines in
output observed in the data.6 Macroeconomists generally agree that expansions
in output, at least in the medium to long run, are driven by TFP increases that
derive from technical progress. In contrast, the notion that recessions are caused
by TFP declines meets with substantial skepticism because, interpreted literally,
it means that recessions are times of technological regress.
Gali (1999) has fueled the debate on the importance of technology shocks as
a business cycle impulse. Gali uses a structural VAR that he identiﬁes by as-
suming that technology shocks are the only source of long-run changes in labor
productivity. He ﬁnds that in the short run, hours worked fall in response to a
positive shock to technology. This ﬁnding clearly contradicts the implications of
basic RBC models. King, Plosser, and Rebelo (1988) and King (1991) discuss in
detail the property that positive technology shocks raise hours worked in RBC
models. Gali’s results have sparked an animated, ongoing debate. Christiano,
Eichenbaum, and Vigfusson (2003) ﬁnd that Gali’s results are not robust to spec-
ifying the VAR in terms of the level, as opposed to the ﬁrst-diﬀerence, of hours
worked. Chari, Kehoe, and McGrattan (2004) use a RBC model that fails to
satisfy Gali’s identiﬁcation assumptions. Their study shows that Gali’s ﬁndings
can be the result of misspeciﬁcation.7 Basu, Fernald, and Kimball (1999) and
Francis and Ramey (2001) complement Gali’s results. They ﬁnd them robust to
using diﬀerent data and VAR speciﬁcations.
Alternatives to Technology Shocks
The debate on the role of technology
shocks in business ﬂuctuations has inﬂuenced and inspired research on models in
which technology shocks are either less important or play no role at all. Generally,
6 One exception is the model proposed in King and Rebelo (1999), which minimizes the need
for TFP declines in generating recessions. This model requires strong ampliﬁcation properties
that result from a highly elastic supply of labor and utilization of capital.
7 See Gali and Rabanal (2005) for a response to the criticisms in Chari, Kehoe, and McGrattan
these lines of research have been strongly inﬂuenced by the methods and ideas
developed in the RBC literature. In fact, many of these alternative theories take
the basic RBC model as their point of departure.
Movements in oil and energy prices are loosely associated with U.S. recessions
(see Barsky and Killian (2004) for a recent discussion). Kim and Loungani (1992),
Rotemberg and Woodford (1996), and Finn (2000) have studied the eﬀects of
energy price shocks in RBC models. These shocks improve the performance of
RBC models, but they are not a major cause of output ﬂuctuations. Although
energy prices are highly volatile, energy costs are too small as a fraction of value
added for changes in energy prices to have a major impact on economic activity.
Christiano and Eichenbaum (1992), Baxter and King (1993), Braun (1994),
and McGrattan (1994), among others, have studied the eﬀect of tax rate and
government spending shocks in RBC models. These ﬁscal shocks improve the
ability of RBC models to replicate both the variability of consumption and hours
worked, and the low correlation between hours worked and average labor pro-
ductivity. Fiscal shocks also increase the volatility of output generated by RBC
models. However, there is not enough cyclical variation in tax rates and govern-
ment spending for ﬁscal shocks to be a major source of business ﬂuctuations.
While cyclical movements in government spending are small, periods of war are
characterized by large, temporary increases in government spending. Researchers
such as Ohanian (1997) show that RBC models can account for the main macro-
economic features of war episodes: a moderate decline in consumption, a large
decline in investment, and an increase in hours worked. These features emerge