- Real Business Cycle Theory
Real Business Cycle Theory (or RBC Theory) is a class of
macroeconomicmodels in which business cyclefluctuations to a large extent can be accounted for by real (in contrast to nominal) shocks. (The four primary economic fluctuations are secular (trend), business cycle, seasonal, and random.) Unlike other leading theories of the business cycle, it sees recessionsand periods of economic growthas the efficient response to exogenouschanges in the real economic environment. That is, the level of national output necessarily maximizes "expected" utility, and government should therefore concentrate on the long-run structural policy changes and not intervene through discretionary fiscal or monetary policydesigned to actively and discretionary smooth economic short-term fluctuations.
According to RBC theory, business cycles are therefore "real" in that they do not represent a failure of markets to clear, but rather reflect the most efficient possible operation of the economy, given the structure of the economy. It differs in this way from other theories of the business cycle, like
Keynesian economicsand Monetarism, which see recessions as the failure of some market to clear.
If we were to take snapshots of an economy at different points in time, no two photos would look alike. This occurs for two reasons:
#Many advanced economies exhibit sustained growth over time. That is, snapshots taken many years apart will most likely depict higher levels of economy activity in the later period
#There exist seemingly random fluctuations around this growth trend. Thus given two snapshots in time, predicting the later with the earlier is nearly impossible.
A common way to observe such behavior is by looking at a time series of an economy’s output, more specifically
gross national product(GNP). This is just the value of the goods and services produced by a country’s businesses and workers.
Figure 1 shows the time series of real GNP for the United States from 1954-2005. While we see continuous growth of output, it is not a steady increase. There are times of faster growth and times of slower growth. Figure 2 transforms these levels into growth rates of real GNP and extracts a smoother growth trend. A common method to obtain this trend is the
Hodrick-Prescott filter. The basic idea is to find a balance between the extent to which general growth trend follows the cyclical movement (since long term growth rate is not likely to be perfectly constant) and how smooth it is. The HP filter identifies the longer term fluctuations as part of the growth trend while classifying the more jumpy fluctuations as part of the cyclical component.
Observe the difference between this growth component and the jerkier data. Economists refer to these cyclical movements about the trend as business cycles. Figure 3 explicitly captures such deviations. Note the horizontal axis at 0. A point on this line indicates at that year, there is no deviation from the trend. All other points above and below the line imply deviations. By using log real GNP the distance between any point and the 0 line roughly equals the percentage deviation from the long run growth trend.
We call relatively large positive deviations (those above the 0 axis) peaks. We call relatively large negative deviations (those below the 0 axis) troughs. A series of positive deviations leading to peaks are booms and a series of negative deviations leading to troughs are
At a glance, the deviations just look like a string of waves bunched together -- nothing about it appears consistent. To explain causes of such fluctuations may appear rather difficult given these irregularities. However, if we consider other macroeconomic variables, we will observe patterns in these irregularities. For example, consider Figure 4 which depicts fluctuations in output and consumption spending, i.e. what people buy and use at any given period. Observe how the peaks and troughs align at almost the same places and how the upturns and downturns coincide.
We might predict that other similar data may exhibit similar qualities. For example, (a) labor, hours worked (b) productivity, how effective firms use such capital or labor, (c) investment, amount of capital saved to help future endeavors, and (d) capital stock, value of machines, buildings and other equipment that help firms produce their goods. While Figure 5 shows a similar story for investment, the relationship with capital in Figure 6 departs from the story. We need a way to pin down a better story; one way is to look at some statistics.
By eyeballing the data, we can infer several regularities, sometimes called
stylized facts. One is persistence. For example, if we take any point in the series above the trend (the x-axis in figure 3), the probability the next period is still above the trend is very high. However, this persistence wears out over time. That is, economic activity in the short run is quite predictable but due to the irregular long-term nature of fluctuations, forecasting in the long run is much more difficult if not impossible.
Another regularity is cyclical variability. Column A of Table 1 lists a measure of this with
standard deviations. The magnitude of fluctuations in output and hours worked are nearly equal. Consumption and productivity are similarly much smoother than output while investment fluctuates much more than output. Capital stock is the least volatile of the indicators.
Yet another regularity is the co-movement between output and the other macroeconomic variables. Figures 4 - 6 illustrated such relationship. We can measure this in more detail using
correlations as listed in column B of Table 1. Procyclical variables have positive correlations since it usually increases during booms and decreases during recessions. Vice versa, a countercyclicalvariable associates with negative correlations. Acyclical, correlations close to zero, implies no systematic relationship to the business cycle. We find that productivity is slightly procyclical. This implies workers and capital are more productive when the economy is experiencing a boom. They aren’t quite as productive when the economy is experiencing a slowdown. Similar explanations follow for consumption and investment, which are strongly procyclical. Labor is also procyclical while capital stock appears acyclical.
Observing these similarities yet seemingly non-deterministic fluctuations about trend, we come to the burning question of why any of this occurs. It’s common sense that people prefer economic booms over recessions. It follows that if all people in the economy make optimal decisions, these fluctuations are caused by something outside the decision-making process. So the key question really is: "what main factor influences and subsequently changes the decisions of all actors in an economy?"
Real Business Cycle Theory
Economists have come up with many ideas to answer the above question. The one which currently dominates the academic literature was introduced by
Finn E. Kydlandand Edward C. Prescottin their seminal 1982 work “Time to Build And Aggregate Fluctuations.” They envisioned this factor to be technological shocks i.e., random fluctuations in the productivity level that shifted the constant growth trend up or down. Examples of such shocks include innovations, bad weather, imported oil price increase, stricter environmental and safety regulations, etc. The general gist is that something occurs that directly changes the effectiveness of capital and/or labour. This in turn affects the decisions of workers and firms, who in turn change what they buy and produce and thus eventually affect output. RBC models predict time sequences of allocation for consumption, investment, etc. given these shocks.
But exactly how do these productivity shocks cause ups and downs in economic activity? Let’s consider a good but temporary shock to productivity. This momentarily increases the effectiveness of workers and capital. Also consider a world where individuals produce goods they consume. The problem with this reasoning is that on aggregate level, this shock would average out.
Individuals face two types of trade offs. One is the consumption-investment decision. Since productivity is higher, people have more output to consume. An individual might choose to consume all of it today. But if he values future consumption, all that extra output might not be worth consuming entirety today. Instead, he may consume some but invest the rest in capital to enhance production in subsequent periods and thus increase future consumption. This explains why investment spending is more volatile than consumption. The life cycle hypothesis argues that households base their consumption decisions on expected lifetime income and so they prefer to “smooth” consumption over time. They will thus save (and invest) in periods of high income and defer consumption of this to periods of low income.
The other decision is the labor-leisure trade off. Higher productivity encourages substitution of current work for future work since workers will earn more per hour today and less tomorrow. More labor and less leisure results in higher output today. More output means greater using the variation in the level of output over a time period, it has been argued that this method of creating simulated paths is a tautology, (using variation in output to explain variation in output) and thus logically unsound.
estimation, which is usually used for the construction of economic models, cailbration only returns to the drawing board to change the model in the face of overwhelming evidence against the model being correct; this inverts the burden of proof away from the builder of the model. Since RBC models explain data ex post, it is very difficult to falsify any one model that could be hypothesised to explain the data. RBC models are highly sample specific, leading some to believe that they have little or no predictive power.
Crucial to RBC models, "plausible values" for structural variables such as the discount rate, and the rate of capital depreciation are used in the creation of simulated variable paths. These tend to be estimated from econometric studies, with 95% confidence intervals. If the full range of possible values for these variables is used, correlation coefficients between actual and simulated paths of economic variables can shift wildly, leading some to question how successful a model which achieves a coefficient of 80% really is.
Monetary Disequilibrium Theory
Dynamic stochastic general equilibrium
New classical economics
New Keynesian economics
Austrian Business Cycle Theory
*Cooley, Thomas. F. 1995. "Frontiers of Business Cycle Research". Princeton University Press.
*Gomes, Joao, Greenwood, Jeremy, and Sergio Rebelo. 2001. "Equilibrium Unemployment." "Journal of Monetary Economics", 48, 109-152.
*Hansen, Gary D. 1985. "Indivisible labor and the business cycle." "Journal of Monetary Economics", 16, 309-327.
*Kydland, Finn E. and Edward C. Prescott. 1982. “Time to Build and Aggregate Fluctuations.” "Econometrica", 50, 1345-70.
*Lucas, Robert E., Jr. 1977. “Understanding Business Cycles.” "Carnegie-Rochester Conference Series on Public Policy", Volume 5, 1977, Pages 7-29.
*Plosser, Charles I. 1989. “Understanding real business cycles.” "Journal of Economic Perspectives", 3, 51-77.
*"Real Business Cycles" (with John B. Long, Jr.), Journal of Political Economy, 1983.
*"Real Business Cycles" John B. Long, Jr., Charles Plosser, Journal of Political Economy, 1983.
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