Lecture

1
Lecture 1Introduction to Forecasting
EC473 Economic Forecasting Senior Seminar Colby
College Fall 2007
2
Introduction to Forecasting

• Predicting the future
• Not an exact science but instead consists of a
  set of statistical tools and techniques that are
  supported by human judgment and intuition.

3
Introduction to Forecasting

• Business forecasting generally attempts to
  predict future customer demand for a firms goods
  or services
• Macroeconomic forecasting attempts to predict
  future behavior of the economy and identify
  business cycle turning points.

4
Inputs to the Forecasting Process(data
collection)

• Finding sources of data about the item to be
  forecast.
• Obtaining information about external conditions
  --- those factors in the environment that will
  influence a forecast.
• Determining the needs of the user of the forecast.

5
Inputs to the Forecasting Process(data
collection)

• Putting together the human financial resources
  required to produce a forecast.
• Listing the available alternatives for
  forecasting techniques.

6
Outputs From the Forecast Process

• Formatting the output.
• Presenting the forecast.
• Evaluating the forecast (an ongoing activity).

7
Steps for Developing a Forecast.

• 1. Write down the basic facts about past trends
  and forecasts.
• 2. Identify the causes of changes in past trends.
• 3. Identify the causes of past forecast errors
  (forecast - actual).
• 4. Identify factors likely to influence future
  behavior.

8
Steps for Developing a Forecast.

• 5. Produce a forecast and give the user some
  measure of its accuracy and reliability.
• 6. Monitor forecast accuracy regularly and
  identify reasons for significant errors.
• 7. Revise the forecasts and forecasting methods
  when necessary.
• Forecast early often

9
Alternative Forecasting Techniques

• Qualitative Techniques
• Delphi method (technological forecasting)
• Market research
• Panel of consensus (CEFC in Maine)
• Visionary forecasts
• Historical analogies
• Useful for long forecast horizons and/or when the
  amount of historical data is limited.

10
Alternative Forecasting Techniques

• Quantitative Statistical Techniques
• Stochastic methods including
• summary statistics moving averages exponential
  smoothing time series decomposition regression
  models trend projections Box-Jenkins
  methodology.
• Poor predictors of turning points.

11
Alternative Forecasting Techniques

• Deterministic Causal Techniques
• Surveys
• Input-Output Models
• Econometric Models
• Leading Indicators

12
Definitions

• Time Series
• A set of chronologically ordered points of data.
• In forecasting a time series it is generally
  assumed that factors which caused demand in the
  past will persist into the future.

13
Definitions

• Decomposition Techniques
• Separating a time series into several
  unobservable components, generally in an additive
  or multiplicative fashion.
• Such components usually include a trend,
  seasonal, cycle, and residual or irregular.

14
Definitions

• Seasonal Component
• Regularly occurring, systematic variation in a
  time series according to the time of year.
• Not found in annual data, or data of lower
  frequencies.

15
(No Transcript)
16
(No Transcript)
17
Residential Electricity Demand in MaineSeasonal
Means
18
Definitions

• Trend Component
• The tendency of a variable to grow over time,
  either positively or negatively.

19
Residential Electricity Demand in Maine(millions
of kWh)
20
Definitions

• Cycle
• Cyclical patterns in a time series which are
  generally irregular in depth and duration.
• Such cycles often correspond to periods of
  economic expansion or contraction.
• Also know as the business cycle.

21
The Solar Cycle 1749 - 1999Sunspot activity
peaks about every 11 years.
22
(No Transcript)
23
The Business CycleReal GDP 1947 -
1999(billions of 1992 dollars)
24
Nominal GDP 1990 - 1997There are seasonal
variations in GDP.
25
Definitions

• Irregular Component
• The unexplained variation in a time series.

26
Residential Electricity Demand in MaineIrregular
Component
27
Selecting an Appropriate Forecasting Technique

• Requirements
• Must understand the nature of the forecasting
  problem.
• Must understand the nature of the data --
  source errors outliers smooth or irregular
  trends cycles seasonality, etc.

28
Selecting an Appropriate Forecasting Technique

• Requirements
• List all potentially useful forecasting
  techniques and information regarding their
  limitations and capabilities.
• Predetermine your selection criteria ability to
  handle the historical forecast horizons
  accuracy applicability cost etc.

29
Selecting an Appropriate Forecasting Technique
Use a multi-method approach
30
Diagnostic Checking

• Consider new explanatory variables in your model
  different forecasting methods and alternative
  data transformations.
• Reject a method if it is unable to provide
  statistically significant results and/or if it is
  unable to achieve the desired degree of accuracy.

31
Diagnostic Checking

• Critical Document each step in the forecasting
  process and any corresponding assumptions.
• Provide estimates of the accuracy of your
  forecast.

32
The Role of Judgment

• The users of econometric models have come to
  realize that their models can only be relied upon
  to provide a first approximation --- a set of
  consistent forecasts which then must be
  massaged with intuition and good judgment to
  take into account those influences on economic
  activity for which history is a poor guide.
• ---Butler, et. Al., Methods and Techniques of
  Business Forecasting, 1974, p.7

33
Presenting the Results

• Contents
• The forecast.
• Analytical evaluation of the forecast relative to
  history.
• Documentation of the rationale and assumptions
  underlying the forecast.

34
Presenting the Results

• Contents
• Documentation of the methods used to produce the
  forecast.
• Potential decision points for the user regarding
  risks and the significance of particular
  assumptions.

35
Presenting the Results

• Supporting Documentation
• Details of the forecasting methods.
• Inputs used during the process.
• The role of judgment.
• Abandoned alternatives.

36
Data Analysis

• Identify the components of a time series.
• Trend Does the series exhibit some slope when
  graphed?
• Seasonal Does the series exhibit regular peaks
  and troughs during the year?
• Cycle Are there identifiable cycles which last
  longer than 1 year?

37
Data Analysis

• Identify the components of a time series.
• Irregular Are there observations which cannot be
  associated with either the trend or seasonal
  components?
• (The Ice Storm of 1998)
• Looking for irregularities is the primary focus
  of data analysis.

38
Data Quality

• Check for accuracy.
• Check for conformity the data must adequately
  represent the phenomenon for which it is being
  used.
• Macroeconomic indicators should reflect business
  cycles.

39
Data Quality

• Timeliness When are the data available?
• Preliminary versus revised data?
• Are the data consistent across time?
• BLS is experimenting with new measures of
  employment and the CPI that are not completely
  consistent with historical data.

40
Exploratory Data Analysis

• Begin with a plot for your series and look for
  predominant features.
• Calculate summary statistics for the series.
• Fit a time trend to the series and look for
  outliers. Calculate (fitted - actual) values.
• Can you decompose the series?

41
What can we say about this series?
42
(No Transcript)