Practical Business Forecasting

ISBN-10: 0631220658

ISBN-13: 9780631220657

Edition: 2002

Authors: Michael K. Evans

List price: $140.95
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Focussing on how to build practical business-forecasting models that produce optimal results, this text explores: methods of forecasting and alternative goals, econometric and regression analysis, and time-series forecasting.
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Book details

List price: $140.95
Copyright year: 2002
Publisher: John Wiley & Sons, Incorporated
Publication date: 5/6/2002
Binding: Hardcover
Pages: 528
Size: 7.00" wide x 10.00" long x 1.50" tall
Weight: 2.288
Language: English

Choosing the Right Type of Forecasting Model
Statistics, Econometrics, and Forecasting
Concept of Forecast Accuracy: Compared to What?
Structural Shifts in Parameters
Model Misspecification
Missing, Smoothed, Preliminary, or Inaccurate Data
Changing Expectations by Economic Agents
Policy Shifts.Unexpected Changes in Exogenous Variables
Incorrect Assumptions about Exogenity
Error Buildup in Multi-Period Forecasts
Alternative Types of Forecasts
Point or Interval
Absolute or Conditional
Alternative Scenarios Weighed by Probabilities
Single or Multi Period
Short Run or Long Range
Forecasting Single or Multiple Variables
Some Common Pitfalls in Building Forecasting Equations
Useful Tools for Practical Business Forecasting
Types and Sources of Data
Time Series, Cross Section, and Panel Data
Basic Sources of U.S. Government Data
Major Sources of International Government Data
Principal Sources of Key Private Sector Data
Collecting Data from the Internet
Forecasting Under Uncertainty
Utilizing Graphs and Charts
Mean and Variance
Goodness of Fit Statistics
Covariance and Correlation Coefficients
Standard Errors and t-ratios.F-ratios and Adjusted R-squared
Using the EViews Statistical Package
Utilizing Graphs and Charts
Checklist Before Analyzing Data
Adjusting for Seasonal Factors
Checking for Outlying Values
Using Logarithms and Elasticities
The General Linear Regression Model
The General Linear Model
The Bivariate Case.Desirable Properties of Estimators
Expanding to the Multivariate Case
Uses and Misuses of R-Bar Squared
Differences Between R-Square and R-Bar Square
Pitfalls in Trying to Maximize R-Bar Square
An Example: the Simple Consumption Function
Measuring And Understanding Partial Correlation
Covariance and the Correlation Matrix
Partial Correlation Coefficients
Pitfalls Of Stepwise Regression
Testing and Adjusting for Autocorrelation
Why Autocorrelation Occurs and What it Means
Durbin-Watson Statistic to Measure Autocorrelation
Autocorrelation Adjustments: Cochrane-Orcutt and Hildreth-Lu
Higher Order Autocorrelation
Overstatement of t-ratios When Autocorrelation is Present
Pitfalls of Using the Lagged Dependent Variable
Testing and Adjusting for Heteroscedasticity
Causes of Heteroscedasticity in Cross-Section and Time-Series Data
Measuring and Testing for Heteroscedasticity
Getting Started: An Example in Eviews
Predicting Retail Sales for Hardware Stores
German Short-Term Interest Rates
Lumber Prices
Additional Topics for Single-Equation Regression Models
Problems Caused by Multicollinearity
Eliminating or Reducing Spurious Trends
Demand for Airline Travel
Log-Linear Transformation
Percentage First Differences
Deviations Around Trends
Weighted Least Squares
Summary and Comparison of Methods
Distributed Lags
General Discussion of Distributed Lags
Polynomial Distributed Lags
General Guidelines for Using PDLs
Treatment of Outliers and Issues of Data Adequacy
Missing Observations
General Comments of Data Adequacy
Uses and Misuses of Dummy Variables
Single-Event Dummy Variables
Changes in Dummy Variables for Institutional Structure
Changes in Slope Coefficients
Nonlinear Regressions
Log-Linear Regressions
Quadratic and Other Powers, Including Inverse
Ceilings, Floors, and Kronecker Deltas: Linearizing with Dummy Variables
General Steps For Formulating A Multiple Regression Equation
The Consumption Function
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