Jing Li

John Sondey

English

Research Papers in Economics

            E C O N O M I C S 
   C O M M E N T A T O R 
South Dakota State University      No. 496                               February 27, 2008
 
ECONOMIC STIMULUS: 
CHARTING THE CAUTIOUS 
COURSE 
by 
John A. Sondey 
Professor of Economics 
 
Recent headlines have announced somber economic 
news: an unemployment rate that increased by three-
tenths of a percent in December 2007 to 5%; a 4
th 
quarter GDP growth rate of only 0.6%; a housing 
foreclosure rate in 2007 that doubled the 2006 figure; 
billion dollar write-downs by global investment banks 
of sub-prime mortgage backed assets, and ultimately, 
the fear of imminent US recession.  
 
Less than a year on the job, Federal Reserve chairman 
Ben Bernanke quickly has his plate full.  In following 
the footprints of his iconic predecessor, Alan 
Greenspan, Bernanke does not lack for free advice for 
righting the economy. Some in the financial sector 
have called for draconian rate cuts in the key fed 
funds rate (which acts as the bell cow which other 
rates follow) by 200 basis points, from its current 3% 
to 1% - its nadir under the Greenspan regime.  
Seeking a quick fix in heading off recession, 
Congress recently passed a fiscal stimulus package of 
approximately $150 billion, or, a bit more than 1% of 
current GDP.  As recessions tend to reduce GDP by 
1% - 2%, this would seem the classic prescription of 
“leaning against the prevailing economic wind” 
perhaps just enough to right the economy.  
 
However, Ben Bernanke’s primary charter as Fed 
chairman and author of monetary policy, maintaining 
stable prices, should be accorded a higher weighting.  
Unfortunately, this policy benchmark is sometimes 
overlooked, particularly when recession clouds build 
on the horizon. Like Homer’s Odysseus, Bernanke 
must chart a careful course between the Scylla of 
(Economics Stimulus …. Continued on page 2) 
 
 
ECONOMETRICS VERSUS 
REGRESSION ANALYSIS 
 
    by     
Jing Li *     
Assistant Professor of Economics 
 
Econometrics and regression analysis are different. 
In some sense all differences boil down to one 
thing: objectives. If interest is in the overall 
association between variables, use regression 
analysis. If a causal relationship or a ceteris paribus 
effect is the goal, use econometric models. For 
example, consider the relationship between 
students’ grades and their attendance. We start with 
a simple regression with grade on the left hand side 
and attendance on the right hand side. Then we hear 
the following talk between a statistician and an 
econometrician: 
 
Statistician: Gee, I am lucky. This regression is just 
what I want. Look at its high R
2. 
 
Econometrician: Forget about R
2. I am worrying 
about the coefficient of attendance, which says that 
on average a student attending one more class can 
earn 2 more points in exams. I don’t believe 
attendance is that important.  Maybe we forgot 
about controlling for something. How about using 
GPA as another independent variable?  
 
Statistician: That is a bad idea. Multicollinearity 
will arise if you add GPA. People with a high GPA 
are more likely to be motivated and thus have good 
(Econometrics …       Continued on page 3) 
_________________________ 
*Dr. Li joined the Department of Economics in January 
2008. He holds a teaching/research appointment and has 
a joint appointment with the Department of Mathematics 
and Statistics.  Econometrics is his primary field; other 
areas of interest include international finance, applied 
macroeconomics, and investment. 
  
  2
(Economics Stimulus …   Continued from page 1) 
rising unemployment and the Charybdis of  
unacceptable inflation.  Steer too hard to avoid one 
evil, and the other beckons. 
 
History provides evidence that previous pump-
priming to stave off or mitigate recessions has often 
generated inflation, of a severity to require strong 
intervention and consequent recession. In the 1970’s, 
the Fed, then under the command of Arthur F. Burns, 
intervened with stimulus to combat stagflation 
generated by the first oil crisis and rising commodity 
prices.  The inflationary fires were kindled and 
smoldered throughout the 1970’s – only to flare up 
wildly at decade’s end.  “Tall Paul” Volcker was 
appointed by President Carter in 1979 to quash 
double-digit inflation. Volcker was successful in 
ratcheting down inflation to 5% within several years – 
but at the cost of the deepest recession since the Great 
Depression.  In 2001, Alan Greenspan brought 
interest rates down (the federal funds rate fell to 1%) 
in the wake of  9/11, a short recession, and the 
bursting of the dot.com stock market bubble. This 
excess liquidity pumped into financial markets 
provided the genesis for cheap money, a soaring 
housing market, irrational lending practices, and the 
sub-prime lending crisis which in 2008 has affected 
nearly all sectors of the US economy and global 
markets as well. 
 
But, how to plot the careful course?  With the swirl of 
political winds and conflicting economic 
perspectives, this presents a daunting task for the 
Federal Reserve and Congress. While the Fed was 
chartered in 1913 to be completely independent of 
political influence, reality dictates otherwise.  There 
are two instruments used to counter macroeconomic 
destabilization, monetary (the Fed) and fiscal 
(Congress and President) policy. At the moment, it 
appears that, together, the policy that has been 
implemented may constitute “overkill” with inflation 
the primary fallout and future recession, a possible 
accompaniment. 
 
First, policymakers are vigorously attacking recession 
when none, yet, exists. Granted, growth in 4
th quarter 
of 2007 was puny indeed at 0.6 percent, but recession 
is defined as six consecutive months of declining 
GDP.  It is quite possible that measures already put in 
place, such as the 125 basis point decrease in the fed 
funds rate, may trigger sufficient easing in other 
critical rates to keep the economy out of recession, 
or, at the very least, help shorten and flatten any 
slump to less than a year and 6% unemployment.  
Second, neither tax rebates nor further interest rate 
cuts can possibly impact the present quarter, which 
very well may see negative GDP growth.  Monetary 
policy acts with a lag of 6 to 18 months and tax 
rebates will not be spent until late May or June at 
the earliest. Third, these very policy instruments 
may impact the economy at a time when inflation is 
becoming the larger problem. In 2007, the CPI-U, 
including food and energy prices, rose by 4.1%, the 
highest annual increase in 17 years. 
 
With stabilization policy, timing may be as 
important as the instruments used.  There are some 
signs that interest rate cuts already implemented are 
finding traction. Rates on the traditional 30 year 
mortgage have fallen a full percentage point over 
the past six months and refinancing activity has 
tripled since December. Also, lower fixed mortgage 
rates should exert downward pressure on adjustable 
mortgage rates and help the transition to a higher 
percentage of fixed rate financing.  Finally, recently 
enacted tax cuts may not prove the hoped-for 
stimulus to consumer spending. Polls indicate a 
substantial portion of the tax cuts may go to paying 
down consumer debt or into savings.  If these 
monies are not recycled one for one into new loans 
because banks are uneasy about extending new 
credit, then the positive impact is lessened. 
 
While the Federal Reserve is the most powerful 
force for economic stabilization in the US, the word 
“myopic” best describes its ability to foresee the 
ramifications of policy actions. Following the 
bursting of the dot.com bubble in 2000 and the 
shrinkage of the Nasdaq composite index to ¼ of its 
historical high, Chairman Greenspan pushed the fed 
funds rate down to one percent, but then held it too 
low for too long, contributing to the housing boom-
to-bust cycle we are experiencing. 
 
Summarily, the timing of stabilization initiatives are 
as important, if not more so, than the policy 
instruments selected.  Estimating when economic 
policy will impact is not an exact science, and its 
strongest effects are as likely to come at an 
inopportune time as at a propitious time.   
  3
Accordingly, the full legacy of any Fed chairman is 
revealed several years after departure, after the ripples 
of policy action have run their course. Thus, the nexus 
between a Chairman taking leave of the Fed and 
hagiography-in-progress should be an extended one. 
 
(Econometrics …      Continued from page 1) 
attendance. In other words, attendance and GPA are 
highly correlated. We better drop one of them.  
 
Econometrician: I disagree with you. If you drop 
GPA, basically you put it into the error term. In that 
case, how can you make sure the coefficient of 
attendance measures its ceteris paribus effect? 
By definition, the ceteris paribus effect of attendance 
is obtainable only if all other factors are held 
constant. In this case, if attendance changes, the error 
term (containing GPA) must change at the same time. 
Your results without GPA on the right hand side will 
have omitted variable bias. 
 
Statistician: I don’t care about the ceteris paribus 
effect. I just want to find a linear relationship between 
grades and attendance.  
 
Do you see my points?  Statisticians and 
econometricians have different goals, and therefore 
pay attention to different details. If problems arise 
they have different remedies. A statistician running 
regression analysis may get worried if he sees the 
following: 
 
1.   A low R
2, which means a good linear fit between 
variables has not been found or the linear 
association between variables is not strong.  
2.   Multicollinearity between independent variables, 
which leads to inflated standard errors and 
unstable estimation results.  
3.   Patterns in residuals signifying heteroskedasticity 
and serial correlation, which suggests that there 
are estimators (such as generalized least square) 
better than ordinary least square (OLS).  
 
On the other hand, an econometrician’s job is not 
done if he finds the following: 
 
1.   The key assumption of zero correlation between 
the independent variable and error term is 
violated. That means he has not controlled for 
enough other variables in order to estimate the 
ceteris paribus effect of the key variable. If data 
of those variables are available, he may 
explicitly use those variables as explanatory 
variables in a multiple regression.  
2.  Heteroskedasticity and serial correlation in error 
terms. Instead of using generalized least square, 
an econometrician is more likely to stick with 
OLS, but with an adjusted formula for standard 
errors of coefficients (called heteroskedasticity 
and autocorrelation consistent, or HAC, 
estimator of standard errors).  
3.   A sample not representative of the population. 
This may be caused by sample selection. An 
econometrician may use the procedure of 
Heckman to correct the bias. Similarly, special 
models (such as Tobit models) are necessary if 
the data are cornered, censored or truncated.  
4.  Nonstationary data, which means that the 
estimation results may be misleading (called 
spurious regression). The regression using 
nonstationary data makes sense only if those 
variables are co-integrated, or related in the long 
term. 
 
Different goals give rise to different methodologies. 
As an illustration, let’s compare two different 
procedures for variable selection. 
 
In regression analysis, the variable selection is 
based on R
2. The variable with the highest simple 
correlation with the dependent variable is used first. 
Then the second independent variable is chosen 
based on its marginal contribution to R
2, or 
equivalently, its partial correlation with the 
dependent variable after the effect of the first 
variable is netted out. This process can continue 
until a satisfactory R
2 is achieved. The F test plays a 
key role in this process. 
 
By contrast, the variable selection in econometrics 
is largely guided by economic theory, or common 
sense if theory is absent. The variable appearing in 
the theory should also appear in the econometric 
model, regardless of its contribution to R
2 and its 
correlation with other independent variables. 
Typically an econometrician drops one variable not 
because of multicollinearity concern, but because of 
its insignificant t ratio. An econometrician dislikes 
the ideas of forward or backward selection 
procedures and would call it data mining.  
  4
 
Specification tests, such as the Hausman test, are 
important in econometrics.  
 
The list for the differences between regression 
analysis and econometrics is long. Here I only touch 
on part of it. The lesson is that you should always 
make it clear what you want to do before selecting 
the appropriate statistical methods.  
********************************************************** 
ECONOMICS COMMENTATOR 
******************************************************************************** 
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South Dakota State University        Phone: 605-688-4141 
Box 504   Scobey Hall              Fax:     605-688-6386 
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ECONOMIC STIMULUS: CHARTING THE CAUTIOUS COURSE; ECONOMETRICS VERSUS REGRESSION ANALYSIS

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ECONOMIC STIMULUS: CHARTING THE CAUTIOUS COURSE; ECONOMETRICS VERSUS REGRESSION ANALYSIS

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