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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. l l l l l l l l l l l l l l l l l l l l l l l l l l 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 l l l l l l l l l l l l l l l l 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.