9 - ECMT 1020 Summer School 09 Lecture 9 Time Series and...

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Unformatted text preview: ECMT 1020 Summer School 09 Lecture 9 Time Series and Forecasting I Forecasting Decision making in business relates to current operations but also to future operations. Businesses are also involved in planning for the future. Decisions about future business directions and strategy must be undertaken in the face of uncertainty. There is uncertain information about markets, competitors, and environments . Forecasting is about prediction of key variables based on reasonable data and assessment. Why Do Agents Forecast? Apart from the uncertainty there are other issues There is a lag time between identification of problems, opportunities and actualisation This could be due to identification lags, contracts, physical processes, verification, testing etc. We need to identify problems and threats or opportunities, before they happen . We need to be proactive rather than reactive The Need for Planning The environment is ever changing. Static organisations do not survive. Reactive organisations may miss opportunities or respond too slowly. Lack of planning can lead to inefficiency. Inefficiency diminishes the chances of the firm surviving in the longer term- Efficient firms tend to survive. Accurate forecasting may lead to greater efficiency giving the firm a competitive edge Types of Forecasting Methods There are a number of classifications of forecast methods; Qualitative vs. Quantitative Time Series vs. Causal The above classifications are not mutually exclusive. The forecaster needs to be aware of the appropriate method to match the forecast situation. Quantitative vs Qualitative Prediction It should be noted that mostly forecasts will be of variables that are measured quantitatively eg sales, costs, exchange rates The distinction between quantitative and qualitative is how the prediction is derived. Quantitative- the prediction is derived using some algorithm or mathematical technique based on quantitative data Qualitative- The prediction is based primarily on judgment or opinion Time Series vs Causal Time Series- These are methods which rely on the past measurements of the variable of interest and no other variables. Eg. Moving average, exponential smoothing, decomposition, extrapolation Causal methods- where the prediction of the target series or variable is linked to other variables or time series. Eg. Regression, correlation and leading indicator methods. Types of Data A useful classification of data for forecasting is; Time Series : A sequence of measurements on a variable taken over specified successive intervals of time eg. monthly interest rates, sales/week, tourist arrivals per annum Cross-Sectional : Measurements on a variable that are at one point in time but spread across a population eg. tourism spend across age groups, production across sectors of the economy Time Series Example Consider annual Sales data for 1998 to 2007 Period Sales 1998 34 1999 44 2000 57 2001 67 2002 81 2003 89 2004 99 2005 112 2006 117 2007 125 Graphical Representation...
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This note was uploaded on 02/10/2012 for the course ECON 1002 taught by Professor Markmelatos during the Three '10 term at University of Sydney.

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9 - ECMT 1020 Summer School 09 Lecture 9 Time Series and...

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