Download NIPA Accounts and Econometric Intro

Business, Government, and the
World Economy
National Income Product Accounts
Econometrics
Macroeconomics
Core Ideas
All Major Components of Aggregate Demand,
(C, I, & NX), are negatively related to the real
interest rate
In the short-run, the economy is dominated
by changes in aggregate demand. In the
long-run it returns to a long-run growth path.
The long-run growth rate is determined by
ratio of investment to GDP, the degree to
which free trade is encouraged, and changes
in productivity.
Macroecomics
Core Ideas
The central bank controls nominal short interest
rates, but the real long-term rate impacts aggregate
demand and is in part based on expectations of
inflation.
Expectations of the future play a key role in how
economic agents behave.
Changes in monetary policy impact both output
and prices in the short run, but only prices in the
long run. There is no long run tradeoff between
unemployment and inflation.
Macroeconomics
Core Ideas
Real output is impacted quicker than inflation
by monetary policy. The short run impact of
monetary policy cannot be predicted
accurately.
Wages are slow to react to changes in the
economy in the short run.
Macroeconomics
Core Ideas
Generally, increases in the cyclically adjusted
federal government budget deficit, regardless of
their financing, reduce the long run growth
rate.
Markets “clear” in the long run and economic
agents maximize their utility subject to short
term rigidities, liquidity, and incorrect
expectations.
Measuring Macroeconomic Data
National Income and Product Accounts (NIPA)
Data prepared by the Bureau of Economic
Analysis (www.bea.gov).
Key Components of NIPA
Gross Domestic Product (GDP)
The final value of all goods and services
produced in a country during a given time
frame.
Excludes intermediate goods to avoid double
counting. It measures the value added at
each stage of the production process.
Excludes good and services produced
elsewhere even if they are consumed in the
economy.
Double Entry Bookkeeping
The total amount of final goods and services
purchased for final use (aggregate demand)
produced must equal the total amount of final
amount paid to the factors of production (value
added).
Components of GDP
Personal Consumption Expenditure (C)
Items purchased by consumers
Gross Private Domestic Investment (I)
Spending by business, construction and inventory
investment
Government Purchases (G)
Total federal state and local government purchases
Net Exports (F or NX)
Exports minus Imports
Personal Consumption Expenditures
Approximately 71.2% of GDP*
Durable Goods – 10.5% of GDP – Goods that
last more than 3 years. Cars appliances, etc.
Non Durable Goods – 20.6% of GDP – Food,
gasoline, etc
Services = 40.2% of GDP
www.bea.gov 2nd qrtr 2009 Preliminary
Trends
Services accounted for approximately 40% of
Personal consumption expenditure in the
1960s it now accounts for approximately 60%
of personal consumption expenditure.
Spending on durable goods is much more
sensitive than spending on the other
components of consumption
Volatility of Durable Goods
Gross Private Domestic Investment
Fixed Investment
Includes nonresidential expenditures and
residential building (single family homes)
recorded at time the home is built – not sold
Inventory Investment
GDP should account for everything produced
Change in inventories, not the level is an
important number to watch
Government Spending
Federal component is approximately 1/3 of the
total.
The federal component is divided into defense
spending and non-defense spending
Net Exports
Export levels as a % of GDP have doubled since
1980.
Since the 1970s imports have been much
greater than exports creating a negative entry
for net exports.
Other Useful Info
Final Sales of Domestic Product: GDP minus
inventories. Provides insight into the total
demand for goods and services produced
domestically
Gross Domestic Purchases: Total purchases of
Goods and Services by US consumers and
Businesses regardless of where it was
produced.
Interpreting GDP Data
Release date:
8:30 am EST the last Friday of the first month of
each quarter (Jan, April, July, and Oct)
Two rounds of monthly revisions follow as does
an annual revision each July
Interpreting the Tables*
Table 1 Real GDP % Change from preceding
period
Real Growth of 3% to 3.5% is considered the
benchmark that allows growth to absorb new
employment.
Look at the combination of non-farm
productivity and growth of labor force as a cap
on non-inflationary growth.
* The Secrets of Economic Indicators by Berhard Baumohl 2005
Interpreting the Tables*
Table 1–
Keep an eye on net exports
Final Sales of Domestic Product provides a good early
warning. If the rise in final sales drops below the
growth rate of GDP for a long period of time
(inventories should be growing) it could be a sign of
trouble.
Don’t neglect the nominal number, since corporate
earnings are recorded this way. S&P 500 earnings
growth is constrained in the long run by economic
growth.
* The Secrets of Economic Indicators by Berhard Baumohl 2005
Interpreting the Tables*
Table 2 Contributions to Percent Change in
real GDP
Provides a breakdown of which components of
GDP are resulting in GDP growth (or decline).
* The Secrets of Economic Indicators by Berhard Baumohl 2005
Interpreting the Tables*
Appendix Table A GDP and Related
Aggregates
Computer Sales – a measure of spending by
business on technology and also as a possible
increase in productivity
Motor vehicle output – responds relatively
quickly to inventory changes on dealer lots –
an increase in inventories is reflected with a
slowdown in production.
* The Secrets of Economic Indicators by Berhard Baumohl 2005
Interpreting the Tables*
Other more frequent releases often
overshadow the GDP release.
However the growth rate can turn out to be
drastically different from the expectations.
It is essential to a full understanding of the
economies output
Revisions might be large enough to impact the
current outlook.
* The Secrets of Economic Indicators by Berhard Baumohl 2005
Interpreting the Tables*
Bond market reactions will likely be stronger
than stock market reactions. Especially if the
release does not correspond with
expectations.
* The Secrets of Economic Indicators by Berhard Baumohl 2005
Manager’s Briefcase*
2000.1 Real annual GDP growth of 2.6%
Inventory Investment fell $47 Billion
Net Exports fell $29 Billion
Government purchases fell $4billion
Final Sales rose $132 Billion
2000.2 Real annual GDP growth of 4.8%
Inventory Investment increased $46 Billion
Net Exports Fell $26 Billion
Government purchases rose $18 billion
Final Sales rose $70 Billion
*Macroeconomics for Managers by Michael K. Evans, Blackwell Publishing
Manager’s Briefcase
Which case points to a stronger future for the
economy?
Increase in real GDP at an annual rate of 4%
with most of the gain from inventory investment
Increase in real GDP at an annual rate of 2%
with a decline in inventories and an increase in
final sales
Manager’s Briefcase
Which case points to a stronger future for the
economy?
Increase in real GDP at an annual rate of 4%
with most of the gain from inventory investment
Increase in real GDP at an annual rate of 2%
with a decline in inventories and an increase in
final sales
GDP vs. GNP
Gross National Product (GNP)
Includes sales of all products by firms based in
the domestic country – even if the sales are
overseas.
Real vs. Nominal GDP
Nominal GDP is the current dollar value
Real GDP adjusts for inflation.
An increase in the price level could cause an
increase in nominal GDP even if the amount
produced did not change
Constant dollars – one approach to calculating
real GDP is keeping prices fixed based on a
base year. Any increase in “real GDP” is then
a function of an increase in the amount
produced.
Chain Weighted Dollars
Currently Real GDP is based on chain weighted
dollars.
Constant dollars are not effective due to
changes in the quality of goods such as
computers. The price of a basic computer 10
years ago is not comparable to the basic
computer today.
Chain Weighted Dollars
The goal is to allow prices to gradually evolve over
time.
Assume a two good economy with two years worth
of data.
year
1
Quant
Pizza
2
Price of
Pizza
3
Quant
Beer
5
Price of
Beer
5
2
3
4
7
6
Two measures of GDP
Nominal GDP
Year 1
$2*3+$5*5 = $31
Year 2
$3*4+$7*6 = $54
Real GDP (Year 1 dollars)
Year 1
$2*3+$5*5 = $31
Year 2
$2*4+$5*6 = $38
Other measures of National Output
Factory Orders
Business Inventories
Industrial Production and Capacity Utilization
Institute for Supply Management Business
Survey (and non business survey)
Index of leading economic indicators
Forecasting
Ideally developing understanding of the
economic relationships will help provide us with
insight about what will happen in the future
Forecasting (using current data to predict the
future )
Econometric models
Noneconometric models
Forecasting with
Econometric Models
Regression results
By taking your established relationship using
past data and plugging in current data you get
a forecast.
Validity Problems
Example
Assume we from Jan 1950 to Dec 2005 on
the relationship between unemployment and
GDP.
D Unemployment = 1.321-.403(%Din GDP)
Regression Statistics
Multiple R
0.874772788
R Square
0.765227431
Adjusted R Square
0.764169897
Standard Error
0.553334069
Observations
224
Coefficients
Standard Error
t Stat
P-value
Intercept
1.32148697
0.062042257 21.2997887 1.44E-55
gdp (12 month % Change) -0.403276944
0.014991853 -26.8997396 8.47E-72
Forecast
D Unemployment = 1.321-.403(%Din GDP)
The % change in GDP for the first qtr of 2006
was 3.5577% Implying a change in
unemployment of 1.321-1.4337 = -.112 The
actual change in unemployment was -.5
However there is an error component we
have left out, the relationship is actually
D Unemployment = 1.321-.403(%Din GDP) + e
Regression Review
Equation of a line: Y = a + bX
Graphing combinations of X and Y form a
line.
X is the independent variable and placed
on the horizontal axis. Y the dependent
variable and placed on the vertical axis
(The value of Y depends upon X)
a is the Y intercept and b the slope of the
line.
Estimating the Regression
The slope of the line is then equal to
Cov(x, y)
Variance X
The Intercept is:
AverageY  slope ( AverageX )
The Line Estimated is the one that
minimizes the sum of the squared residuals
Actual vs. Regression
4.00
3.00
Regression Estimate
for March 2006
2.00
1.00
-4.0000
-2.0000
0.00
0.0000
2.0000
4.0000
6.0000
8.0000
10.0000
12.0000
14.0000
-1.00
-2.00
-3.00
Actual March 2006
Observation
-4.00
Unemployment (net 12 month Change)
Regression
Other problems
In our example we took the known change in
GDP to forecast the change in unemployment.
You would probably be more interested in using
an estimate of GDP (another forecast) to
predict the forecasted change in
unemployment…
The Bottom Line
In the best case scenario (extremely high tstatistic, high R squared, data fits assumptions
underlying regression, etc) with a relationship
that has been tested over time – you will still
have error in your forecast.
Most of the time, the relationship is not as
stable, the data not as consistent, and many
other real world problems exits
This creates a case where there is an even
higher chance for error
Assumptions about the error
For the model to be accurate some statistical
properties need to hold.
The error term has zero expected value
The error term has constant variance for all
observations
The random error are statistically independent
The error term is normally distributed
Constant Variance
If the error is heteroscedastic, the variance my
change over time
Large firms vs. Smaller firm (larger firms may
have higher variance) or a industry subset of
firms may exhibit this behavior.
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Error Terms are Independent
If the error terms from different observations
are correlated there is an issue termed serial
correlation
Time Series observations can exhibit this
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BLUE
If you add the assumptions about error to the
following assumptions
The relationship between Y and X is linear
The X’s are nonstochastic (nonrandom) variables
whose values are fixed
Then the regression line will be the Best Linear
Unbiased Estimator of the relationship
Some Other Sources of Error in
Econometric Models
Incorrect Underlying Theory
Instability of underlying relationships
Errors in Econometric Models
Inadequate or Incorrect Data
Tendency to Cluster around consensus forecast
Inability to predict exogenous events
Erroneous assumptions about policy variables.
Incorrect Theory
What are you trying to measure – which variables
are independent dependent etc…
Example: Assume you are interested in estimating
college enrollment and you know that it has
increased over time. You have a large number of
possible variables and estimate the relationship
between the variables and enrollment.
It is possible to get “spurious results” evidence of a
relationship that does not exist…
Regression results
How confident would you be using the yearly
data below to estimate changes in college
enrollment?
Regression Statistics
Multiple R
0.943256937
R Square
0.889733648
Adjusted R Square 0.879709435
Standard Error
311438.1733
Observations
13
Intercept
X varible
Coefficients
Standard Error
t Stat
P-value
6174869.356
791962.2492 7.796924 8.33E-06
2484828.353
263749.4402 9.42117 1.34E-06
Spurious Relationships and
Data Mining
You should not develop a theory to explain your
observed relationship. Just because there
appears to be a strong correlation does not
mean that there is economic meaning.
How is that different from the current trend in
marketing – Data mining?
Instability of underlying relationships
The basic regression model assumes that there
exists a long term stable relationship between
the variables – it does not change over time
Much of Economics is based on human behavior
– it can and does change over time
Inadequate and Incorrect data
How important are revisions of economic data?
Does the economy respond to the actual level
of an indicator or to the announced level of the
indicator?
Recent revisions in GDP as an example.
“Herd behavior” and
consensus forecasts
As a forecaster the cost of being wrong and
much different from the average forecast is
much greater than the cost associated with a
being wrong with a forecast slightly different
than the average.
Exogenous Shocks
Outside influences impact the relationship – we
know that the estimation is telling only part of
the answer – much of what happens is
unexplained by the other variable.
Interpretation of Policy
Impact of policy only happens after a long lag
Data (preliminary vs. final data)
Recognition (moving average vs. month to
month data)
Reaction (length of time before policy makers
recommend change)
Legislative (Fiscal policy is slow)
Economic (Once policy is implemented –
economy is slow to respond)
Non-econometric Models
Naïve Models and consensus surveys (long term
trends
Leading Indicators (Developing an index based
upon a number of other indicators)
Survey Methods (measuring consumer attitudes
and business conditions)
Forecast Error
Equation of a line: Yt = a + bXt + e e~ N(0,s2)
Given Xt+1 it is easy to find a forecast of YT+1
The forecast error is the difference between
the actual observation of Y and the predicted
value.
By definition (if all assumptions hold) the error
has an expected value of zero so the forecast
is unbiased
Forecast error
The error should be N(0,s2) so it is possible to
determine a confidence interval for the forecast
error. This provides for the ability to test the
forecasting ability of the regression
95% confidence
bands
Economic Indicator Example:
Measuring Durable Goods
Durable Goods Orders (Advance Report on
Durable Goods Manufacturers Shipments ,
Inventories and Orders)
Where: www.census.gov (Census Bureau)
Released 3-4 weeks after the end of the
reporting month
Revisions for each of the 2 preceding months –
revisions can be large.
Is a volatile and unpredictable indicator
Measuring Durable Goods
Survey of 3,500 manufacturers from 89
industries ($500 million in annual shipments
each)
Asked for numbers of New orders, shipments,
unfilled orders and inventories
What to look for
Table 1
New Orders – Leading indicator. Be careful a
single large aircraft or military order can inflate
this number.
Orders excluding transportation and Defense.
Aircraft orders come in large quantities (both
civilian and Defense)
Other broad headings such as Primary metals,
fabricated metals, Computers, all other durables.
Capital goods (business spending)
What to look for in the report
Table 2
Unfilled orders – If high can slow down the
production process and cause inflationary
pressure.
Market Impact
More response in bond market than stock
market.
Both markets interpret information including
current unused capacity with the level of new
orders.