Macro Notes: Introduction to the Short Run

Transcription

1 Macro Notes: Introduction to the Short Run Alan G. Isaac American University

2 But this long run is a misleading guide to current affairs. In the long run we are all dead. Economists set themselves too easy, too useless a task if in tempestuous seasons they can only tell us that when the storm is long past the ocean is flat again. J.M. Keynes, 1923 (Tract on Monetary Reform) As an event, the Depression is largely synonymous with the birth of modern macroeconomics, and it continues to haunt successive generations of economists. R.A. Margo (1993 JEP)

3 Our long-run model explains potential output Ȳt and long-run inflation. But deviations from these long-run values are important too. Our short-run model explains short-run output and short-run inflation. Once we have separated out inflation from real growth, we can look at the behavior over time of real GDP. When we look at real income over time, we not only see the upward trend, we also see large fluctuations around that trend.

4 Trend and Cycle: Stylized Representation

5 The theory of business cycles tries to explain such deviations. Contraction (recession, bust): RGDP pc below trend (for enough time). Expansion (boom): RGDP pc above trend (for enough time). The National Bureau of Economic Research (NBER is a private, nonprofit, nonpartisan economic research organization. Many prominent economists are members of the NBER. NBER Business Cycle Dating Committee determines the official business cycle dates

6 Economists collect macroeconomic data on many variables. We call data collected at regular points in time time series data. Many macroeconomic time series trend upward. Can we detrend them? I.e., can we separate out the trends and cycles of a macroeconomic time series? Trend: low frequency component Cycle: high frequency component

7 When we detrend the log of a variable, we are taking out the average percentage growth rate. The best fitting line for the linear trend is called the low frequency (linear trend, or fitted) values. What is left over is the high frequency (linear cycle, or residuals). Linear detrending: best straight line through the points. When macroeconomic series have persistent average growth rates, as many do, we usually use log-linear detrending. (Some textbooks call this linear detrending.)

8 Detrending with a Linear Trend Source: Farmer 2000, Chapter 1, Figure 5

9 Detrending with a Flexible Trend Actual, Potential, and Cyclical Output (Flexible Trend)

10 Detrending with a Flexible Trend Actual, Potential, and Cyclical Output (Flexible Trend)

11 Great Depression US experience: Unemployment reaches 25% Output falls 60% (at least 20% below trend) Problem long lasting:

12 Coherence and Persistence Business cycles consist of persistent comovements in the deviations of certain macroeconomic variables from their trends. This comovement is called coherence. E.g., in a boom, RGDP pc above trend and unemployment below trend. Procyclical: rise above trend with RGDP pc. (E.g., consumption and investment.) Countercyclical: rise above trend when RGDP pc. falls below (E.g., unemployment.) Business cycles tend to persist: macroeconomic variables are autocorrelated.

13 Coherence and Business Cycles I The absolute value of the correlation coefficient between two arbitary time series measures their degree of coherence. Here X and Y are two aribtrary time series. Correlation Correlation Degree of Scatter plot Coefficient Coherence slope positive ρ XY > 0 +ρ XY positive negative ρ XY < 0 ρ XY negative A variable that is positively correlated with the cycle in real GDP is called procyclical. A variable that is negatively correlated with the cycle in real GDP is called countercyclical. Here X is an aribtrary time series and Y is the cyclical component of real GDP.

14 Coherence and Business Cycles II Correlation Scatter plot Correlation Coefficient Meaning slope positive ρ XY > 0 procyclical positive negative ρ XY < 0 countercyclical negative

15 Countercyclical Social Indicators Source: Farmer 2002, Chapter 1, Figure 8

16 When we look at the linear cycle in (log) real GDP and (log) real C, we find GDP and consumption are highly correlated, with consumption being slightly less volatile. You might have expected the high correlation between consumption and income, since many students first explore consumption in terms of a simple linear consumption function: C = a + by. However economists find this quite surprising, since they expect consumption to be much smoother than income. The permanent-income/life-cycle hypothesis, tells us that consumers smooth their consumption. (Just as you will not spend all your monthly paycheck on the day you receive it.)

17 Procyclical and Countercyclical Variables Source: Farmer 2000, Chapter 1, Figure 7

18 When we look at the the linear cycles in (log) real GDP and (log) real I, we find they are again highly correlated, but investment is much more volatile. The volatility in investment has led to a lot of scrutiny of investment as a causal factor in business cycles.

19 RBC economists emphasize technological change as the source of I fluctuations. Keynesians emphasize animal spirits as a source of I fluctuations. Note a commonality: both link changes in I to changes in the perceived profitability of I.

20 Recall that we tend to find inflation falling during recessions. (Cause or effect?) Stagflation: high inflation and high unemployment.

21 Is Inflation Countercyclical?

22 Is Inflation Countercyclical? Source: FRED

23 Shock: causes a deviation from the historical trend Examples of shocks: oil price changes, new technologies, fiscal policy innovations, natural disasters. Question: are shocks permanent or temporary? Context: we know the cycle component of real GDP is highly autocorrelated. The idea that shocks are permanent is closely associated with the real business cycle literature.

24 Suppose shocks are permanent: then if run a regression of real GDP on its lagged value we should find a coefficient of one. y t = ρy t 1 + ε t (1) This is something we can test for. Another way to run the same regression is to subtract y t 1 from each side to get y t = (ρ 1)y t 1 + ε t (2) and then test if our coefficient (ρ 1) differs from zero. This is the standard way of running what is known as a Dickey-Fuller test for a unit root (i.e., for ρ = 1).

25 This has huge implications for forecasting. If shocks are permanent (i.e., ρ = 1, the unit root case) then you should not forecast a return to trend even in the distant future. In contrast, if shocks are transitory (0 < ρ < 1) then you should forecast an eventual return to trend.

26 Phillips Curve: Output Version

27 Is there a trade-off between output and inflation? In the SR, it seems there is: the Phillips curve. In the LR, no. That is, if we try to keep output above Ȳ, inflation keeps rising. This is sometimes called the accelerationist hypothesis.

28 Phillips Curve Output 2% above Ȳ tends to raise inflation about 1 percentage point.

29 Empirical Phillips Curve: Output Version (US)

30 Empirical Okun s Law u ū = 1 2Ỹt

31 Empirical Okun s Law Note the unusually big rise in Ū in 2009.

32 Empirical Okun s Law Internationally also: note the unusually big rise in Ū in 2009 compared to most other OECD countries with similar GDP growth.

33