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What will recovery look like?

Source: http://www.econbrowser.com/archives/2009/03/what_will_recov.html
Posted on Sunday, March 15th, 2009 | In Economics
Contributed by: James Hamilton (http://www.econbrowser.com) -

When good news comes, what should we expect to see?

The graph below plots the quarterly percentage change (at an annual rate) for real GDP and some of its key components since 1947. Calculated Risk has noted that the typical recession pattern is for nonresidential fixed investment to begin its decline after residential fixed investment and consumption, and not begin its recovery until after housing and consumption have begun their recovery.



Quarterly percent change (at an annual rate) of real GDP, 1947:Q2 to 2008:Q4, and three of its components. Data source: BEA Table 1.1.3. Shaded areas denote NBER recession dates.
gdp_dynamics_mar_09.gif



This pattern emerges more clearly when you look at the average behavior of each component over the course of the earlier historical recessions.



Average cumulative change in 100 times the natural log of real GDP or its respective component beginning from the business cycle peak for the 10 recessions between 1947 and 2001. Horizontal axis denotes quarters after the peak.



Here’s the severe 1981-82 recession in isolation:



Cumulative change in 100 times the natural log of real GDP or its respective component beginning in 1981:Q3. Horizontal axis denotes quarters after 1981:Q3.



And here’s what we’re seeing so far this time. True to form, nonresidential fixed investment had been holding up reasonably well during the early part of the downturn, but has now started to decline significantly. That process has enough momentum that it is hard to see nonresidential fixed investment picking back up before we see some recovery from consumption spending.



Cumulative change in 100 times the natural log of real GDP or its respective component beginning in 2007:Q4. Horizontal axis denotes quarters after 2007:Q4.



And how reasonable a prospect is that? Plunging output and employment are big negatives for consumption spending, as is the huge drop in stock market and real estate wealth. The home equity that remains has become harder to extract, and a general contraction in household debt relative to GDP seems unavoidable. These latter factors presumably have played an important role in the surge in private saving as a percentage of disposable personal income.



Personal saving as a percentage of disposable personal income, 1959:M1 to 2009:M1. Data source:
BEA Table 2.6



But a couple of other factors are likely also contributing to these higher saving rates where the near-term dynamics could favor an increase in consumption spending. The first is the fact that gas prices are about $2/gallon lower than they were last spring, which has freed $280 billion for consumers to save or spend on other things.



US average retail gasoline price. Source: NewJerseyGasPrices.com.



Pessimism itself is another potentially changeable factor that is making a separate contribution to what we see going on. When sentiment turns around, spending will pick up, and I don’t see why we’d rule out the possibility of some improvement in sentiment. The preliminary March reading of the Reuters/Michigan index of consumer sentiment was at least slightly up rather than down.



Reuters/Michigan index of consumer sentiment. Data source: FRED and
MarketWatch.



With all eyes on consumers, it’s interesting that the average value for retail sales in January and February was 1.8% above the dismal number for December.



Source: FRED.



WSJ Real Time Economics relates Morgan Stanley economist David Greenlaw’s concerns about seasonal adjustment problems with the January and February sales figures.

But I nevertheless take some encouragement from these numbers.



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About James Hamilton (http://www.econbrowser.com)
James Hamilton received his Ph.D. in Economics from the University of California at Berkeley in 1983. He has been a professor at the University of California, San Diego since 1990 and served as Chair of the Economics Department from 1999 to 2002. He is the author of Time Series Analysis, the leading text on forecasting and statistical analysis of dynamic economic relationships. He has done extensive research on business cycles, monetary policy, and oil shocks, and has been a research adviser and visiting scholar with the Federal Reserve System for 20 years.

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