BUSINESS ECONOMICSClass 9

9 December, 2009

Recap

Cost Curves Marginal Cost Curve Average Cost Curve Fixed and Variable Costs Short-run and Long-run Costs

Price Control Ceiling and floor pricing Support price Monopoly restrictions

Price Controls

Criticism Prices kept artificially low Demand is increased to the point where supply can not keep up Shortages in the price-controlled product. Shortages lead to black markets where prices for the same good

exceed those of an uncontrolled market. Once controls are removed, prices will immediately be subject to

rampant inflation, which can temporarily shock the economic system.

Example of how price controls cause shortages Arab oil embargo between October 19, 1973 and March 17, 1974.

Long lines of cars and trucks quickly appeared at retail gas stations in the U.S. and some stations closed because of a shortage of fuel.

The fixed price was below what the market would otherwise bear and, as a result, the inventory disappeared.

4

Forecasting Techniques

Forecasting is the establishment of future expectations by the analysis of past data, or the formation of opinions.

Forecasting is an essential element of capital budgeting.

Capital budgeting requires the commitment of significant funds today in the hope of long term benefits. The role of forecasting is the estimation of these benefits.

5

Forecasting Techniques and Routes

Techniques

Quantitative

RegressionTime trends

Moving averages

Qualitative

Delphi methodNominal group

Jury of exec opinion

Scenario projection

Routes

Top-downBottom-up

6

Quantitative Forecasting

Quantitative: Regression with related variable

Data set of ‘Sales’ as related to both time and the number of households.

YEAR HOUSEHOLDS SALES1991 815 21091992 927 25301993 1020 22871994 987 31941995 1213 37851996 1149 33721997 1027 36981998 1324 39081999 1400 37252000 1295 41292001 1348 45322002 1422 4487

HISTORICAL DATA

7

Quantitative Forecasting

Quantitative: Sales plotted related to households.

SalesUnits Related to Number of Households

0

1000

2000

3000

4000

5000

0 500 1000 1500

Number of Households

Sa

les

Un

its

Sales

8

Regression

Straight line Y = mx + C is the trend line that represents the regressed data

Co-efficient of the Y axis intercept and slope can be derived using regression analysis.

9

Regression Analysis

Predicting with the regression output. Regression equation is:

Sales(for year) = -348.218 + ( 3.316 x households).

Assuming that a separate data set forecasts the number of households at 1795 for the year 2006, then: Sales(2006) = -348.218 + ( 3.316 x

1795) = 5,604 units.

10

Time series

Sales plotted as a function of time. Sales Regression: Line Fit Plot

0

1000

2000

3000

4000

5000

1990 1995 2000 2005

Year

Sale

s Actual

Predicted

11

Regression: Auto Forecast by Excel.

Sales by Year, With Automatic Three Year Prediction

0

1000

2000

3000

4000

5000

6000

1990 1995 2000 2005 2010

Year

Sale

s

SALES

Simple LinearRegression,Forecast Out toYear 2005

12

Moving Average

Sales Units Per Year With Fitted Two Year Moving Average

0

1000

2000

3000

4000

5000

1990 1995 2000 2005

Years

Sa

les

Un

its

SALES

2 per. Mov.Avg.(SALES)

13

Forecasting Routes

Top-Down where international and national events affect the future behaviour of local variables.

14

Forecasting Routes

Bottom-Up

Where local events affect the future behaviour of local

variables.

15

Forecasting: Summary

Sophisticated forecasting is essential for capital budgeting decisions

Quantitative forecasting uses historical data to establish relationships and trends which can be projected into the future

Qualitative forecasting uses experience and judgment to establish future behaviours

Forecasts can be made by either the ‘top down’ or ‘bottom up’ routes.