Atnaujinkite slapukų nuostatas

Principles of Business Forecasting New edition [Kietas viršelis]

3.90/5 (15 ratings by Goodreads)
,
  • Formatas: Hardback, 250 pages
  • Išleidimo metai: 13-Apr-2012
  • Leidėjas: South-Western
  • ISBN-10: 0324311273
  • ISBN-13: 9780324311273
Kitos knygos pagal šią temą:
Principles of Business Forecasting New editionDidesnis paveikslėlis
  • Formatas: Hardback, 250 pages
  • Išleidimo metai: 13-Apr-2012
  • Leidėjas: South-Western
  • ISBN-10: 0324311273
  • ISBN-13: 9780324311273
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780324311273.html
Raktažodžiai:
Ord/Fildes PRINCIPLES OF BUSINESS FORECASTING, 1E serves as both a textbook for students and as a reference book for experienced forecasters in a variety of fields. The authors' motivation for writing this book is to give users the tools and insight to make the most effective forecasts drawing on the latest research ideas. Ord and Fildes have designed PRINCIPLES OF BUSINESS FORECASTING for users who have taken a first course in applied statistics or who have an equivalent background. This book introduces both standard and advanced forecasting methods and their underlying models, and also includes general principles to guide and simplify forecasting practice. A key strength of the book is its emphasis on real data sets, taken from government and business sources and used in each chapter's examples. Forecasting techniques are demonstrated using a variety of software platforms and the companion website provides easy-to-use Excel® macros to support the basic methods. After the introductory chapters, the focus shifts to using extrapolative methods (exponential smoothing and ARIMA) and then to statistical model-building using multiple regression. The authors also cover more novel techniques including data mining and judgmental methods, which are gaining increasing attention in applications. Finally, they examine organizational issues of implementation and the development of a forecasting support system within an organization.
Preface xvii
Chapter 1 Forecasting, the Why and the How
1(18)
Introduction
2(1)
1.1 Why Forecast?
2(4)
Purpose
3(1)
Horizon
3(1)
Information
3(1)
Value
4(1)
Evaluation
4(2)
1.2 What and Why Do Organizations Forecast?
6(1)
1.3 Examples of Forecasting Problems
7(7)
Retail Sales
7(1)
Seasonal Patterns for Retail Sales
8(3)
UK Road Accidents
11(1)
Airline Travel
11(1)
Sports Forecasting: Soccer (AKA Football!)
11(2)
Sports Forecasting: A Cross-sectional Example---Baseball Salaries
13(1)
Stochastic Processes
14(1)
1.4 How to Forecast
14(1)
1.5 Forecasting Step by Step
15(1)
1.6 Computer Packages for Forecasting
16(1)
1.7 Data Sources
16(1)
1.8 The Rest of the Book
17(1)
Summary
17(1)
Minicase 1.1 Inventory Planning
17(1)
Minicase 1.2 Long-Term Growth
17(1)
Minicase 1.3 Sales Forecasting
18(1)
Minicase 1.4 Adjusting for Inflation
18(1)
References
18(1)
Chapter 2 Basic Tools for Forecasting
19(39)
Introduction
20(1)
2.1 Types of Data
20(3)
Use of Large Databases
21(2)
2.2 Time Series Plots
23(3)
Seasonal Plots
24(2)
2.3 Scatterplots
26(3)
2.4 Summarizing the Data
29(6)
Notational Conventions
29(1)
Measures of Average
30(1)
Measures of Variation
31(1)
Assessing Variability
32(1)
An Example: Hot Growth Companies
33(2)
2.5 Correlation
35(3)
2.6 Transformations
38(3)
Differences and Growth Rates
38(1)
The Log Transform
39(2)
2.7 How to Measure Forecasting Accuracy
41(7)
Measures of Forecasting Accuracy
42(4)
Measures of Absolute Error
46(2)
2.8 Prediction Intervals
48(3)
Using the Normal Distribution
49(1)
Empirical Prediction Intervals
49(1)
Prediction Intervals: Summary
50(1)
2.9 Basic Principles
51(1)
Summary
52(1)
Exercises
52(3)
Minicase 2.1 Baseball Salaries
55(1)
Minicase 2.2 Whither Walmart?
56(1)
Minicase 2.3 Economic Recessions
57(1)
References
57(1)
Chapter 3 Forecasting Trends: Exponential Smoothing
58(40)
Introduction
59(1)
Software
59(1)
3.1 Method or Model?
59(2)
A Forecasting Model
60(1)
3.2 Extrapolation Methods
61(5)
Extrapolation of the Mean Value
62(2)
Use of Moving Averages
64(2)
3.3 Simple Exponential Smoothing
66(7)
Forecasting with the EWMA, or Simple Exponential Smoothing
68(1)
An Excel Macro for SES
69(2)
The Use of Hold-Out Samples
71(1)
Some General Comments
72(1)
3.4 Linear Exponential Smoothing
73(6)
Basic Structure for LES
74(1)
Updating Relationships
75(1)
Starting Values
76(3)
3.5 Exponential Smoothing with a Damped Trend
79(2)
Choice of Method
81(1)
3.6 Other Approaches to Trend Forecasting
81(2)
Brown's Method of Double Exponential Smoothing (DES)
81(1)
SES with (Constant) Drift
82(1)
Tracking Signals
82(1)
Linear Moving Averages
82(1)
3.7 Prediction Intervals
83(1)
3.8 The Use of Transformations
84(5)
The Log Transform
85(1)
Use of Growth Rates
86(1)
The Box-Cox Transformations
87(2)
3.9 Model Selection
89(1)
3.10 Principles for Extrapolative Models
90(1)
Summary
91(1)
Exercises
91(3)
Minicase 3.1 The Growth of Netflix
94(1)
Minicase 3.2 The Evolution of Walmart
94(2)
Minicase 3.3 Volatility in the Dow Jones Index
96(1)
References
96(1)
Appendix 3A Excel Macros
97(1)
Chapter 4 Seasonal Series: Forecasting and Decomposition
98(29)
Introduction
99(1)
4.1 Components of a Time Series
100(1)
4.2 Forecasting Purely Seasonal Series
101(3)
Purely Seasonal Exponential Smoothing
103(1)
4.3 Forecasting Using a Seasonal Decomposition
104(4)
4.4 Pure Decomposition
108(2)
4.5* The Census X-12 Decomposition
110(1)
4.6 The Holt-Winters Seasonal Smoothing Methods
111(6)
The Additive Holt-Winters Method
111(1)
*Starting Values
112(5)
4.7 The Multiplicative Holt-Winters Method
117(1)
*Starting Values
117(1)
Purely Multiplicative Schemes
118(1)
4.8 Weekly Data
118(3)
Multiple Seasonalities
120(1)
4.9 Prediction Intervals
121(1)
4.10 Principles
121(1)
Summary
122(1)
Exercises
122(2)
Minicase 4.1 Walmart Sales
124(1)
Minicase 4.2 Automobile Production
124(1)
Minicase 4.3 U.S. Retail Sales
124(1)
Minicase 4.4 UK Retail Sales
125(1)
Minicase 4.5 Newspaper Sales
125(1)
References
126(1)
Appendix 4A Excel Macro for Holt-Winters Methods
126(1)
Chapter 5 State-Space Models for Time Series
127(24)
Introduction
128(1)
5.1 A State-Space Model for Simple Exponential Smoothing
128(4)
The Random Walk
130(1)
The Random Error Term
131(1)
5.2 Prediction Intervals for the Local-Level Model
132(2)
5.3 Model Selection
134(5)
Use of a Hold-out Sample
134(2)
Information Criteria
136(3)
Automatic Selection
139(1)
5.4 Outliers
139(3)
5.5 State-Space Modeling Principles
142(1)
Summary
143(1)
Exercises
143(1)
Minicase 5.1 Analysis of UK Retail Sales
144(1)
Minicase 5.2 Prediction Intervals for WFJ Sales
145(1)
References
145(1)
Appendix 5A* Derivation of Forecast Means and Variances
145(1)
Appendix 5B Pegels' Classification
146(1)
Appendix 5C* State-Space Models for Other Exponential Smoothing Methods
147(4)
Chapter 6 Autoregressive Integrated Moving Average (ARIMA) Models
151(53)
Introduction
152(1)
6.1 The Sample Autocorrelation Function
152(3)
Model Assumptions
154(1)
6.2 Autoregressive Moving Average (ARMA) Models
155(5)
The First-Order Autoregressive Model
155(2)
Higher Order Autoregressive Models
157(1)
Pure Moving Average (MA) Models
158(2)
Mixed Autoregressive Moving Average (ARMA) Models
160(1)
6.3 Partial Autocorrelations and Model Selection
160(11)
The Partial Autocorrelation Function (PACF)
160(4)
Model Choice
164(1)
Nonstationary Series
165(2)
The Random Walk
167(4)
6.4 Model Estimation and Selection
171(7)
Should We Assume Stationarity?
173(3)
Use of Information Criteria
176(1)
How Much Differencing?
177(1)
Formal Tests for Differencing
178(1)
6.5 Model Diagnostics
178(4)
The Ljung-Box Test
178(4)
6.6 Outliers Again
182(2)
6.7 Forecasting with ARIMA Models
184(4)
Prediction Intervals
185(1)
Forecasting Using Transformations
186(2)
6.8 Seasonal ARIMA Models
188(5)
Forecasts for Seasonal Models
192(1)
6.9* State-Space and ARIMA Models
193(2)
From ARIMA to a State-Space Form
194(1)
6.10* GARCH Models
195(4)
The GARCH (1,1) Model
196(3)
6.11 Principles of ARIMA Modeling
199(1)
ARIMA Models
199(1)
GARCH Models
199(1)
Summary
200(1)
Exercises
200(1)
Minicase 6.1
201(1)
Minicase 6.2
201(1)
References
202(1)
Appendix 6A* Mean and Variance for AR(1) Scheme
202(2)
Chapter 7 Simple Linear Regression for Forecasting
204(37)
Introduction
205(1)
7.1 Relationships Between Variables: Correlation and Causation
206(2)
What Is Regression Analysis?
207(1)
7.2 Fitting a Regression Line by Ordinary Least Squares (OLS)
208(5)
The Method of Ordinary Least Squares (OLS)
210(3)
7.3 A Case Study on the Price of Gasoline
213(4)
Preliminary Data Analysis
214(2)
The Regression Model
216(1)
7.4 How Good Is the Fitted Line?
217(3)
The Standard Error of Estimate
218(1)
The Coefficient of Determination
219(1)
7.5 The Statistical Framework for Regression
220(3)
The Linear Model
220(2)
Parameter Estimates
222(1)
7.6 Testing the Slope
223(6)
P-Values
224(3)
Interpreting the Slope Coefficient
227(1)
Transformations
228(1)
7.7 Forecasting by Using Simple Linear Regression
229(5)
The Point Forecast
229(1)
Prediction Intervals
230(2)
An Approximate Prediction Interval
232(1)
Forecasting More than One Period Ahead
232(2)
7.8 Forecasting by Using Leading Indicators
234(1)
Summary
234(1)
Exercises
234(2)
Minicase 7.1 Gasoline Prices Revisited
236(1)
Minicase 7.2 Consumer Confidence and Unemployment
236(1)
Minicase 7.3 Baseball Salaries Revisited
236(1)
References
237(1)
Appendix 7A Derivation of Ordinary Least Squares Estimators
237(2)
Appendix 7B Computing P-Values in Excel
239(1)
Appendix 7C Computing Prediction Intervals
240(1)
Chapter 8 Multiple Regression for Time Series
241(28)
Introduction
242(1)
8.1 Graphical Analysis and Preliminary Model Development
242(1)
8.2 The Multiple Regression Model
243(2)
The Method of Ordinary Least Squares (OLS)
244(1)
8.3 Testing the Overall Model
245(4)
The F-test for Multiple Variables
246(2)
ANOVA in Simple Regression
248(1)
S and Adjusted R2
248(1)
8.4 Testing Individual Coefficients
249(4)
Case Study: Baseball Salaries
251(1)
Testing a Group of Coefficients
251(2)
8.5 Checking the Assumptions
253(5)
Analysis of Residuals for Gas Price Data
256(2)
8.6 Forecasting with Multiple Regression
258(3)
The Point Forecast
259(1)
Prediction Intervals
260(1)
Forecasting More than One Period Ahead
261(1)
8.7 Principles
261(1)
Summary
262(1)
Exercises
262(2)
Minicase 8.1 The Volatility of Google Stock
264(1)
Minicase 8.2 Economic Factors in Homicide Rates
265(1)
Minicase 8.3 Forecasting Natural Gas Consumption for the DC Metropolitan Area
265(1)
Minicase 8.4 Economic Factors in Property Crime
266(1)
Minicase 8.5 U.S. Retail & Food Service Sales
266(1)
Minicase 8.6 U.S. Unemployment Rates
267(1)
References
268(1)
Appendix 8A The Durbin-Watson Statistic
268(1)
Chapter 9 Model Building
269(41)
Introduction
270(1)
9.1 Indicator (Dummy) Variables
271(7)
Seasonal Indicators
274(4)
9.2 Autoregressive Models
278(1)
9.3 Models with Both Autoregressive and Regression Components
279(2)
9.4 Selection of Variables
281(5)
Forward, Backward, and Stepwise Selection
282(2)
Searching All Possible Models: Best Subset Regression
284(1)
Using a Hold-out Sample to Compare Models
285(1)
A Regression Model with Autoregressive Errors
286(1)
9.5 Multicollinearity and Structural Change
286(6)
Use of Differences
289(1)
Structural Change
290(2)
9.6 Nonlinear Models
292(7)
Polynomial Schemes
293(2)
Nonlinear Transformations
295(1)
Intrinsically Nonlinear Models
296(1)
Changing Variances and the Use of Logarithmic Models
297(2)
9.7 Outliers and Leverage
299(5)
Leverage Points and What to Do About Them
299(2)
The Effects of Outliers
301(2)
The Role of Outliers and Leverage Points: A Summary
303(1)
9.8 Intervention Analysis
304(1)
9.9 An Update on Forecasting
305(1)
9.10 Principles
306(1)
Summary
306(1)
Exercises
307(1)
Minicase 9.1 An Econometric Analysis of Unleaded Gasoline Prices
308(1)
Minicase 9.2 The Effectiveness of Seat-Belt Legislation
308(1)
References
309(1)
Chapter 10* Advanced Methods of Forecasting
310
Introduction
311(1)
10.1 Predictive Classification
311(7)
Evaluating the Accuracy of the Predictions
314(3)
A Comment
317(1)
10.2 Classification and Regression Trees
318(4)
Performance Measures: An Example
319(1)
Computer Ownership Example Revisited
320(2)
10.3 Logistic Regression
322(4)
Issues in Logistic Regression Modeling
325(1)
10.4 Neural Network Methods
326(10)
A Cross-Sectional Neural Network Analysis
329(2)
A Time Series Neural Network Analysis
331(4)
Neural Networks: A Summary
335(1)
10.5 Vector Autoregressive (VAR) Models
336(5)
10.6 Principles
341(1)
Summary
341(1)
Exercises
342