AGBU 430

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Price Forecasting

• AGBU 430

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Economic Changes

• Growth in national and international markets
• Advances in transportation and communication
• Specialization
• Increase in the size of businesses
• Greater economic interdependence and uncertainty

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Forecasting

• Reduces uncertainty in business decisions
• Must understand what forecasters can do
• how they do it and
• how to recognize good forecasts

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The Basics

• Future levels of economic variables
• Explain past and future customer buying habits or
  impact of new technology
• Failure to recognize these things is the leading
  cause of business failure

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5 Factors affecting Forecasting

• Accuracy desired
• Time permitted to develop forecast
• Complexity of situation
• Time period to be projected
• Resources available

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Sources of Forecasts

• Forecasting info. from private public sources
• 1. Private Firms Private firms use sophisticated
  computer models to make their projections sell
  them for a fee. Some agribusiness firms operate
  their own in-house forecasting units
• 2. Trade Associations Small firms that cannot
  buy or forecast on their own can get forecasts
  from trade associations
• (Ex Cattleman's Association)
• 3. Government Some forecasting data (e.g. town,
  retail sales, pop, age, income, of households)
  is available, free of charge, from federal,
  state, local gov agencies
• (Ex Census of Retail Trade published by US
  Dept of Commerce)
• 4. Business Publications business publications
  like Fortune, Business Week, Wall Street Journal,
  The Economist, Supermarket News, etc

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Types of Data

• Cross-sectional Data Used to determine economic
  forces that influence other variables at a time
  or place
• Ex amount of chicken eaten in each state last
  year
• Time Series data Frequently used in forecasting
  involves many observations of same variable
  over many time periods at the same location
• Ex av. monthly farm-level chicken price from Jan
  1990 to now.

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Simple Time Series Analysis

• Many economic data exhibit repeating patterns of
  behavior or regularity when plotted over time.
• The simplest approach to examining such data
  assumes that each observation comprises 4
  components
• (1) trend,
• (2) seasonal effect,
• (3) cyclical effect, and
• (4) an irregular effect or error term.

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• Trend Season Cyclical Irregular
• Trend Is the broad long-term growth or decline
  of the industry or firm.
• Season represents annually recurring forces that
  affect sales or prices.
• Cyclical Is a measure of forces that act as
  broad irregular waves. Such cycles may be due to
  demographic changes, general business cycles,
  etc.
• Irregular Is simply an error term and represents
  variations that cannot be attributed to
  trend,season or cycle.

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Forecasting Procedures

• Extrapolation Simplest procedure assumes that
  whatever happened in the past will continue to
  happen in the future
• Ex if corn price rose 10 last year, one expects
  it to rise by 10 again this year.
• Disadvantage Method has no economic base, this
  limits its long-run use may cause a manager to
  miss quick changes in economic environment.
• AdvantageEffective forecasting method in short
  run (1 yr or less), ease of application makes
  it an attractive "first-cut" forecasting
  procedure as many economic variables are often
  slow to change.

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Forecasting Procedures

• Graphical Analysis time-series data are plotted
  on a graph to
• permit forecasters to "see" changes over time
• A trend line can be fitted to the graph its
  slope gives expected
• future or long-run direction of the change.
• Trend Line A rise in price from
• 2 in yr 9 to 6 in yr 13, Average
• price rise of 1 per yr. If
• things stay the same we can
• forecast an increase in price
• of 1 for next year
• Caution No economic foundation

6 5 4 3 2 1
trend
Price
1 2 3 4 5 6 7 8 9 10 11 12
13 time
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Adjusting for Trend

• Graphical analysis does not provide a realistic
  picture of the data or
• impact of inflation
• Does trend in graphical analysis show a real
  increase in price or
• show a rise in the general level of all prices
  due to inflation?
• Deflation deflate or remove from these prices
  the effects of inflation
• Dividing the price series by an appropriate index
  of price levels
• calculated for the same period, such as the
  Index of Prices Received
• by Farmers, the Consumer Price Index, or the
  Producer Price Index.
• Deflation Procedure
  Deflated
• Corn price Price Recd Real
  Price
• Yr1 Dec 2.23 99 2.25
• Yr 2 Jan 2.27 102 2.22
• Deflated prices can be plotted new trend line
  is fitted.

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Adjusting for Population Growth

• Population can also distort time series data
• If consumption rising yearly, it is important to
  know if its caused by changes in consumer tastes,
  lower prices, or a rising population
• Population effects on consumption is removed by
  dividing consumption by pop to get a per capita
  consumption rate.
• If per capita consumption is rising or falling
  over time, it could indicate a change in consumer
  tastes, the effects of price shifts, or other
  variables, but not population.

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Moving Averages

• Moving Averages way to get a clearer picture of
• change or trend in the data
• Helps reduce the impact of short-run fluctuations
  in
• the data thro the process of "smoothing out
• 12-month moving average is calculated by adding
  the
• 12 most recent months of prices dividing by
  12. This
• is repeated each subsequent months, with the
  oldest
• month's value being dropped replaced by the
  next
• month's price.
• Plotting the moving average for these prices
  against
• the monthly prices shows the smoothing effect
  of the procedure

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• Farm-level Average Corn Prices, the Index of
  Prices Received by Farms, Real Prices
  
  Deflated
• (1) (2) (3)
  (4) (5)
• PRICE PER INDEX OF
  REAL PRICE MOVING-AV
• DATE BUSHEL PRICES RECVD
  PER BUSHEL PRICE/ BUSHEL
• Yr 1 Dec. 2.23 99 2.25
• Yr 2 Jan. 2.27 102 2.22
• Feb. 2.29 105 2.18
• Mar. 2.43 109 2.23
• Apr. 2.51 114 2.20
• May 2.72 118 2.30
  2.11
• June 2.68 118 2.27 2.09
• July 2.49 117 2.13 2.07
• Aug. 2.35 116 2.03 2.04
• Sept. 2.27 118 1.92 2.01
• Oct. 2.20 125 1.76 2.00
• Nov. 2.20 119 1.85 1.98
• Dec. 2.45 122 2.01 1.99
• Yr 3 Jan. 2.48 127 1.95 2.03
• Feb. 2.46 132 1.86 2.08

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Seasonality

• Seasonality recurring patterns in prices and
  quantities heavily influenced throughout the year
  by weather and biology
• Annual production cycle reflected in price levels
  throughout the year
• Lowest prices at time of harvest
• Slow rise in prices as time goes by

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Seasonality

Year 1 Year 2 Year 3 Monthly Average
Monthly Price Index Jan 2.24 2.02 1.90 2.053 93.
4 Feb 2.22 1.97 2.02 2.070 94.2 Mar 2.32 2.02 1.
94 2.093 95.3 Apr 2.37 2.18 1.85 2.133 97.1 Ma
y 2.54 2.24 1.84 2.207 100.5 June 2.47 2.51 1.99
2.323 105.7 July 2.24 2.74 2.22 2.400 109.2 A
ug 2.07 2.79 2.44 2.433 110.7 Sept 1.96 2.31 2.6
0 2.290 104.3 Oct 1.85 2.11 2.32 2.093 95.3 No
v 1.82 2.02 2.30 2.047 93.2 Dec 2.03 2.06 2.58
2.223 101.2
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Additional Approaches

• Opinion Polls opinions and intuition of people
  which represent changes in the business climate
• Leading Indicators variables that change
  direction 3-6 months before economy does
• Roughly Coincident Indicators variables that
  change direction normally when the economy does
• Lagging Indicators variables that change
  direction 3-6 months after the economy does

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Additional Approaches

• Leading Indicators
• Change in consumer debt
• Average workweek
• New building permits
• Roughly Coincident Indicators
• GDP
• Personal income
• Retail store sales
• Lagging Indicators
• Unemployment rate
• Book values
• Commercial loans outstanding

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Using Forecasts

• Use best case scenario, most likely scenario, and
  worst case scenario
• If forecast follows what happens in market it is
  estimating correctly, if not, make adjustments
• Update constantly
• Ultimately, forecasting should assist the firm in
  planning purchases of raw materials, production
  levels, inventory levels, introductions of new
  products
• Done correctly, forecasts should lower costs and
  increase sales and profit the final test of
  every forecast