Slides for Part IV-A. Introduction to Forecasting. Outline: What is forecasting? Why use forecasting techniques? Applications of forecasting methods Types of forecasting models Time series vs. cross-sectional data. . Introduction to Forecasting. Initial points:
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Introduction to Forecasting
If I could see the future, I would be on the plane to Las Vegas
S & P 500, Index of Total Return
S&K note that comparatively small forecast errors (that is , from point estimates) can have significant negative consequences for decision-makers.
Time -series data: historical data--i.e., the data sample consists of a series of daily, monthly, quarterly, or annual data for variables such as prices, income , employment , output , car sales, stock market indices, exchange rates, and so on.
Cross-sectional data: All observations in the sample are taken from the same point in time and represent different individual entities (such as households, houses, etc.)
Time series data:Daily observations, Korean Won per dollar
With time series models,we seek to uncover trendsin past data and then project them into the future
Time series models include:
This class of models entails the estimation of causal relationships (as suggested by theory, usually) between a dependent variable and one or more independentor explanatory variables.The principaltool of causal forecasting is regressionanalysis.
To forecast the demand for coal, we insert forecastedvalues of FIS, FEU, etc. into this equation
Example: The Demand for Coal
COAL = 12,262 + 92.43FIS + 118.57FEU -48.90PCOAL + 118.91PGAS
Source: Pyndyck and Rubinfeld (1998), p. 218.