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Year 2 Managerial Economics (AUT EXAM) > Business Forecasting > Flashcards

Business Forecasting Flashcards

Week 3 & 4

1
Q

What is the interaction between companies/managers and uncertainty?

A
  • All companies experience some levels of uncertainty
  • Reduction in uncertainty helps make better decisions
  • Managers wish to predict changes to allow for greater trend prediction so that adequate plans are in place
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2
Q

When is forecasting more accurate?

A
  • Forecasts tend to be better in periods of economic stability, as there is more data to predict this period accurately
  • Forecast horizons tend to be higher/more accurate when they are short-term, as little can occur in a smaller timeframe
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3
Q

What is the Hierarchy of Forecasts? Name the order of this

A
  • Hierarchy of Forecasts shows the denominations of forecasts that can be predicted
  • Move from national hierarchy to industry to individual firm to product
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4
Q

How best to select a forecasting technique?

A
  • Cost associated with developing the forecasting model compared with its gains
  • Complexity of the relationship that are forecasted
  • Time period of forecast (LT/ST)
  • Time between receiving information and using information
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5
Q

How can you assess the accuracy of a forecast (mathematically) ?

A
  • Forecasting error = (Y - Ŷ)
  • Root Mean Square Error (RMSE) = √[1/T Σ(Y - Ŷ)²
  • Mean Absolute Deviation (MAD) = 1/T Σ |Y - Ŷ|
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6
Q

What are the two broad types of forecasting techniques?

A
  • Qualitative (Looking for a direction of up/down)
  • Quantitative (Uses accurate/exact estimates)
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7
Q

What are some advantages or disadvantages of qualitative forecasting?

A
  • Flexible
  • Shows early signals
  • It can become complex to track all interactions
  • Can be heavily biased
  • There is a lack of test of accuracy
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8
Q

What are some examples of Expert Opinion forecasting?

A
  • Personal Insight: An informed individual uses personal/company experience as a basis for expectations
  • Panel Consensus: Based on several people’s opinions, the resulting forecast is an amalgamation of their opinions
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9
Q

What is the Delphi Method?

A
  • Developed by RAND Corporation, where forecasts are derived from expert analytics
  • Panel receive a series of questions individually and responses are analysed by an independent party
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10
Q

What are Surveys and Opinion Polls? What are some of the advantages and disadvantages?

A
  • Surveys are interview/mailed questions that ask businesses/firms/Govt/individuals about plans
  • Quick, cheap and give new ideas
  • Can help appeal to consumer tastes
  • Sample bias/biased questions
  • Could lie
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11
Q

What is a Market experiment? What can be a disadvantage of this?

A
  • Select a test market to design a strategy
  • Experiment is designed to test reactions, prices and products
  • Risk of losing consumers- so can’t control all factors
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12
Q

What are Barometric Indicators? Give an example of how these can help this

A
  • They are designed to alert businesses about general economic conditions
  • Lagging and Leading indicators are slightly out of sync- so leading indicators can be useful for forecasting (but these are still out of sync)
  • Confidence board have a list of indicators
  • Average working week can show manufacturing output
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13
Q

How is it best to choose accurate Leading indicators?

A
  • Must be accurate
  • Must provide adequate lead time
  • The lead time should be consistent
  • There should be logic in why it predicts the outcome
  • The cost/time necessary for data collection should be minimised
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14
Q

How can these indicators be interpreted?

A
  • Composite Indicators
  • Diffusion Indicators
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15
Q

What is the difference between Composite and Diffusion Indices?

A
  • Composite Indices: Weighted Average of individual indicators and interpret them in terms of % change
  • Diffusion Indices: Measure the individual time series that increases one month to the next
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16
Q

What are some disadvantages of using Barometric Indicators?

A
  • Non-perfect prediction, variability in Lead times
  • High variability of indicators month-to-month
  • Doesn’t imply the magnitude of changes
  • Inconsistencies in component indicators
17
Q

What are some advantages or disadvantages of quantitative forecasting?

A
  • This can illustrate the organised or behavioural relationship
  • Easy to test accuracy (reliability) of forecast
  • Relies on past data -> cannot provide us real time
  • Data mining of some information
18
Q

What are some quantitative forecasting techniques?

A
  • Deterministic trend analysis: Time Series Data are a sequence of the values of an economic variable at different points (assuming that future patterns in a time series might project past patterns)
  • Smoothing Techniques: Use average of the past to predict the future
  • Econometric Modelling: Using Time series/Cross-sectional/ both
  • Input-Output Analysis: Interrelationship between different sectors/industry
19
Q

What are the components of a time series?

A
  • Secular trends: Long-run trends in economic data
  • Cyclical components: Business Cycles -> major expansions
  • Seasonal effects: Seasonal variation -> fairly constant
  • Random fluctuations: Unpredictable random factors
20
Q

How do you model a time series?

A
  • Using past data to project future
  • Yt is the actual variable, Ŷt is the forecast
  • Ŷt = f(Yt-1, Yt-2, Yt-3…)
21
Q

What are the criteria for prediction accuracry?

A
  • MSE = 1/T Σ(Y - Ŷ)² ; RMSE =√MSE
  • The lower MSE/MAD/RMSE
22
Q

What are the elementary time-series models?

A
  • (1) Ŷt+1 = Yt
  • Simplest method, works where there is no trend
  • Need to collect the data quickly to make good forecast
  • (2) Ŷt+1 = Yt + (Yt - Yt-1)
  • Additional adjustment to include changes
  • Model predicts better if there are +ve/-ve trends
23
Q

What are Secular trends? What are some components of these lines?

A
  • By plotting data, we will see a trajectory from the line of best fits
  • If Linear, the equation is Y = α + βt
  • If constant growth, the equation is Y = Y0 (1+g)ᵗ
  • If declined growth, Y = e ^ (β1 - β2/t)
  • Parameters β, β1, β2, g and α can be found through OLS
24
Q

How can you adjust for seasonality (name the two methods)?

A
  • Ratio-to-trend method
  • Season dummy variable method
25
Q

What is the Ratio to Trend method?

A
    1. Find the trend forecast [linear trend => Ŷ = α + βt ]
    1. Divide the actual values by trend forecast values
    1. Using these ratios, compute an average ratio per month/quarter
    1. Multiply trend forecast values each month by seasonality to give a seasonally adjusted forecasting
26
Q

What is the Seasonal Dummy method?

A
    1. Incorporate seasonality into linear trends
      Ŷ = β0 + β1t + β2D1t + β3D2t + β4D3t … where D1t = 1 for Q1, 0 if not etc.
    1. Estimate parameters via OLS/past data
  • For N periods, you need N-1 dummies to avoid the ‘dummy trap’
27
Q

What are smoothing techniques? Name the two methods

A
  • Attempt to average out random variables
  • Works best when a data series changes slowly
  • Can use moving average or first-order exponential smoothing
28
Q

What is the formula for Moving Average smoothing technique?

A
  • Ŷt+1 = (ΣYt) / n
  • To choose the correct N, RMSE should be minimised
  • Each past observation has the same weight of 1/N
29
Q

What is the formula for First-Order exponential smoothing technique? What is the fundamental difference between this and the moving average method?

A
  • FOES puts a weight on observations- giving greater weight to more recent observations
  • Ŷt+1 = Ŷt + w (Yt - Ŷt)
  • OR Ŷt = wYt-1 + (1-w) Ŷt-1
  • Combining the two equations, we get:
    Ŷt+1 = wYt + w(1-w)Yt-1 + w(1-w)²
  • Choose a w that minimises RMSE
30
Q

What is the process of constructing an econometric model?

A
  • Specify the Model
  • Identify Variables
  • Collect data
  • Estimate the parameters of the model (α, β …)
  • Develop a forecast based on parameters
  • NOTE : ε~N (0, σ²)
31
Q

What are some issues with econometric models?

A
  • Simultaneity/identification problem- some independent variables can be endogenous
  • Multicollinearity- independent variables may be highly related
  • Autocorrelation- Error terms may have a pattern
  • Heteroscedasticity- Error terms may have a non-constant σ²

Year 2 Managerial Economics (AUT EXAM) (5 decks)

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