2. Pro Forma Analysis

3. Topic Coverage 1. Definition of Pro forma analysis. 2. Alternative approaches to projecting net benefits from production and investment opportunities. 3. Input to capital budgeting and valuation decisions.

4. PASTFUTURE PRESENT  Historical analysis  Comparative analysis  Historical price and yield trends  Pro forma analysis  Forming expectations about future prices, costs and productivity  Ad hoc extrapolations  Projections based upon available outlook data  Projections based upon econometric analysis

5. PEPE QEQE Assumes perfect knowledge of outcomes in all 5 areas!!!!

6. Supply-side risk for a given price… QLQEQHQLQEQH PEPE

7. Demand and supply- side risk and potential price variability… QLQEQHQLQEQH PHPEPLPHPEPL

8. Ad Hoc Modeling Approaches Naïve model – using last year’s prices, costs and yields Simple linear trend extrapolation of historical prices, costs and yields Using assumptions made by others

9. Econometric Model Approach Capturing future supply/demand impacts on prices and unit costs Linkages to commodity policy Linkages to domestic economy Linkages to the global economy

10. Historical Data on Fixed Input Sales to Producers

11. 2000 2001 2002 2003 2004 2005 2006 Timeline Required for Capital Budgeting… Assume it is the year 2000 and John Deere wants to project farm machinery and equipment sales over the next six years to determine if plant expansion is necessary.

12. 2000 2001 2002 2003 2004 2005 2006 Timeline Required for Capital Budgeting… Assume it is the year 2000 and John Deere wants to project farm machinery and equipment sales over the next six years to determine if plant expansion is necessary. farm Capital budgeting models of investment decisions require projections of the annual farm revenue and cost values over the entire 2001 to 2006 time period.

13. Econometric Analysis Based on Time Trend Extrapolation I T = f(Year T )

14. poor job A linear time trend projection of future farm machinery and equipment sales therefore does a poor job of predicting future sales activity.

15. Econometric Analysis Based on Investment Theory I T = f{[E(P T )×E(Q T )]/E(c T )}

16. muchbetter job An econometric model based on investment theory does a much better job of predicting future sales activity.

17. Crop Market Model Demand equation: Q d = a 0 - a 1 (Price) +  a i (demand shifters) Supply equation: Q s = b 0 +b 1 (price) +  b i (supply shifters) Market equilibrium: Q d = Q s

18. Crop Market Equilibrium D S D S Quantity Price PePe QeQe D S Demand consists of: -Food use -Feed use -Exports -Ending stocks Demand consists of: -Food use -Feed use -Exports -Ending stocks Supply consists of: -Beginning stocks -Production -Imports Supply consists of: -Beginning stocks -Production -Imports

19. Histogram for Wheat Prices

20. 3.345 3.145 3.945 -.80 +.80

21. Wheat Projections Made in 1997 ActualForecast

22. Estimating the Annual Supply and Use of Wheat

23. Income elasticity Cross price elasticity Econometric Analysis – Food Use Own price elasticity

24. Observed and Predicted Values For Wheat Food Use

25. Remaining Steps to Forecasting the Price of Wheat Develop similar econometric equations for feed use, exports and ending stock demand. Develop econometric equations for production and import supply. (Q D =Q S ) excess demand equals zero Substitute the estimated equations into the market equilibrium definition (Q D =Q S ) and solve for the price where excess demand equals zero.

26. Crop Market Model Demand equation: Q d = a 0 - a 1 (Price) +  a i (demand shifters) Supply equation: Q s = b 0 +b 1 (price) +  b i (supply shifters) Market equilibrium: Q d = Q s

27. Conclusions Econometric models are preferred over naïve models and linear time trend models. Much more accurate. elasticities Provide much more information (e.g., elasticities). sensitivity analysis potential variability Allow for sensitivity analysis with independent (exogenous) variables when evaluating potential variability about expected trends.