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Macroeconomics for managers / / Michael K. Evans
| Macroeconomics for managers / / Michael K. Evans |
| Autore | Evans Michael K |
| Pubbl/distr/stampa | Oxford, England ; ; Malden, Mass., : Blackwell, 2003 |
| Descrizione fisica | 1 online resource (816 p. ) : ill |
| Disciplina | 339/.024/68 |
| Soggetto topico |
Managerial economics
Macroeconomics |
| ISBN |
9781280199479
1280199474 9780470752784 0470752785 9781405142243 1405142243 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | The importance of macroeconomics -- 1.1. What is macroeconomics? -- 1.2. Links between macroeconomics and microeconomics -- 1.3. Current core of macroeconomic theory -- 1.4. Macroeconomics - an empirical discipline -- 1.5. The importance of policy applications -- 1.6. Positive and normative economics: why macroeconomists disagree -- 1.7. Roadmap of this book -- Appendix: thumbnail sketch of the development of macroeconomics -- 2: National income and product accounts (nipa) -- 2.1. How the national income and product accounts are constructed -- A double-entry bookkeeping system -- 2.2. Components of GDP: final goods and services -- Government purchases and other expenditures -- Inventory investment: an exception to the rule -- Relative size of the major components of GDP -- Manager's briefcase: interpreting the GDP statistics -- Case study 2.1. Shifting shares of GDP in the post World War II period -- 2.3. Differences between final and intermediate goods and services -- Defining and determining intermediate services -- 2.4. Components of national income -- Major components of national income -- Case study -- 2.2. Shifting patterns of components of factor income -- Case study -- 2.3. Different measures of corporate profits -- The tradeoff between corporate profits and net interest income -- Differences between GDP and GNP -- 2.5. Balancing items linking GDP, NI, PI, and DI -- Links between GDP and disposable income -- Managers briefcase: understanding personal saving -- 2.6. Value added by stages of production: an example -- 2.7. Inclusions and exclusions in the NIPA data -- Transfers of assets -- Case study -- 2.4. Treatment of mortgage payments -- Foreign expenditures -- Different types of government expenditures -- The underground economy -- 2.8. Circular flow between aggregate demand and production -- Appendix: key macroeconomic identities -- Key data concepts: inflation, unemployment, and labor costs -- 3.1. Measuring inflation: three different types of indexes -- 3.2. Factors causing the inflation rate to be overstated -- Effect of a fixed-weight market basket -- Measuring quality changes -- New products and services -- Drawbacks to implicit deflators -- The chained index: the latest compromise -- The Boskin Committee report on inflation -- Recent improvements by the BLS -- Case study -- 3.1. Fixed weight, implicit, and chained price indexes -- 3.3. Could the inflation rate be understated? -- Manager's briefcase: how to interpret the inflation data -- 3.4. Different measures of unemployment -- The duration of unemployment -- 3.5. Collecting the employment and unemployment data -- The BLS "fudge factor" -- Initial unemployment claims -- Manager's briefcase: how to interpret the employment and unemployment data -- Case study -- 3.2. Differences in payroll and household measures of employment -- 3.6. The concept of full employment -- Full employment: not a fixed rate -- Determinants of the full-employment unemployment rate -- 3.7. Unit labor costs -- Case study -- 3.3. Rising labor compensation costs in 200 -- Managers briefcase: using the data for wages and unit labor costs -- 3.8. Summarizing the economic data: indexes of leading and coincident indicators -- 3.9. Methods and flaws of seasonally adjusted data -- 3.10. Preliminary and revised data -- Part II: Aggregate demand and joint determination of output and interest rates -- 4: The consumption function -- 4.1. Principal determinants of consumption. |
| Record Nr. | UNINA-9911019460803321 |
Evans Michael K
|
||
| Oxford, England ; ; Malden, Mass., : Blackwell, 2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Practical business forecasting [[electronic resource] /] / Michael K. Evans
| Practical business forecasting [[electronic resource] /] / Michael K. Evans |
| Autore | Evans Michael K |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Malden, MA, : Blackwell Publishers, 2002 |
| Descrizione fisica | 1 online resource (xx, 501 p. ) : ill |
| Disciplina | 658.40355 |
| Soggetto topico |
Business forecasting
Management Theory Management Business & Economics |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-19755-2
9786610197552 0-470-70269-9 0-470-75562-8 1-4051-3780-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Practical Business Forecasting -- Contents -- Acknowledgments -- Preface -- Part I -- Chapter 1: Choosing the Right Type of Forecasting Model -- Introduction -- 1.1 Statistics, Econometrics, and Forecasting -- 1.2 The Concept of Forecasting Accuracy: Compared to What? -- 1.2.1 Structural Shifts in Parameters -- 1.2.2 Model Misspecification -- 1.2.3 Missing, Smoothed, Preliminary, or Inaccurate Data -- 1.2.4 Changing Expectations by Economic Agents -- 1.2.5 Policy Shifts -- 1.2.6 Unexpected Changes in Exogenous Variables -- 1.2.7 Incorrect Assumptions About Exogeneity -- 1.2.8 Error Buildup in Multi-Period Forecasts -- 1.3 Alternative Types of Forecasts -- 1.3.1 Point or Interval -- 1.3.2 Absolute or Conditional -- 1.3.3 Alternative Scenarios Weighed by Probabilities -- 1.3.4 Asymmetric Gains and Losses -- 1.3.5 Single-Period or Multi-Period -- 1.3.6 Short Run or Long Range -- 1.3.7 Forecasting Single or Multiple Variables -- 1.4 Some Common Pitfalls in Building Forecasting Equations -- Problems and Questions -- Chapter 2: Useful Tools for Practical Business Forecasting -- Introduction -- 2.1 Types and Sources of Data -- 2.1.1 Time-Series, Cross-Section, and Panel Data -- 2.1.2 Basic Sources of US Government Data -- 2.1.3 Major Sources of International Government Data -- 2.1.4 Principal Sources of Key Private Sector Data -- 2.2 Collecting Data from the Internet -- 2.3 Forecasting Under Uncertainty -- 2.4 Mean and Variance -- 2.5 Goodness-of-Fit Statistics -- 2.5.1 Covariance and Correlation Coefficients -- 2.5.2 Standard Errors and t-ratios -- 2.5.3 F-ratios and Adjusted R-Squared -- 2.6 Using the Eviews Statistical Package -- 2.7 Utilizing Graphs and Charts -- 2.8 Checklist Before Analyzing Data -- 2.8.1 Adjusting for Seasonal Factors -- 2.8.2 Checking for Outlying Values -- 2.9 Using Logarithms and Elasticities -- Problems and Questions.
Part II -- Chapter 3: The General Linear Regression Model -- Introduction -- 3.1 The General Linear Model -- 3.1.1 The Bivariate Case -- 3.1.2 Desirable Properties of Estimators -- 3.1.3 Expanding to the Multivariate Case -- 3.2 Uses and Misuses of R¯2 -- 3.2.1 Differences Between R2 and R¯2 -- 3.2.2 Pitfalls in Trying to Maximize R¯2 -- 3.2.3 An Example: The Simple Consumption Function -- 3.3 Measuring and Understanding Partial Correlation -- 3.3.1 Covariance and The Correlation Matrix -- 3.3.2 Partial Correlation Coefficients -- 3.3.3 Pitfalls of Stepwise Regression -- 3.4 Testing and Adjusting for Autocorrelation -- 3.4.1 Why Autocorrelation Occurs and What It Means -- 3.4.2 Durbin-watson Statistic to Measure Autocorrelation -- 3.4.3 Autocorrelation Adjustments: Cochrane...orcutt and Hildreth...lu -- 3.4.4 Higher-order Autocorrelation -- 3.4.5 Overstatement of t-ratios when Autocorrelation Is Present -- 3.4.6 Pitfalls of Using The Lagged Dependent Variable -- 3.5 Testing and Adjusting for Heteroscedasticity -- 3.5.1 Causes of Heteroscedasticity in Cross-Section and Time-Series Data -- 3.5.2 Measuring and Testing for Heteroscedasticity -- 3.6 Getting Started: An Example in Eviews -- Case Study 1: Predicting Retail Sales for Hardware Stores -- Case Study 2: German Short-term Interest Rates -- Case Study 3: Lumber Prices -- Problems and Questions -- Chapter 4: Additional Topics For Single-equation Regression Models -- Introduction -- 4.1 Problems Caused By Multicollinearity -- 4.2 Eliminating or Reducing Spurious Trends -- Case Study 4: Demand for Airline Travel -- 4.2.1 Log-linear Transformation -- 4.2.2 Percentage First Differences -- 4.2.3 Ratios -- 4.2.4 Deviations Around Trends -- 4.2.5 Weighted Least Squares -- 4.2.6 Summary and Comparison of Methods -- 4.3 Distributed Lags -- 4.3.1 General Discussion of Distributed Lags. 4.3.2 Polynomial Distributed Lags -- 4.3.3 General Guidelines for Using PDLs -- 4.4 Treatment of Outliers and Issues of Data Adequacy -- 4.4.1 Outliers -- 4.4.2 Missing Observations -- 4.4.3 General Comments on Data Adequacy -- 4.5 Uses and Misuses of Dummy Variables -- 4.5.1 Single-Event Dummy Variables -- 4.5.2 Changes in Dummy Variables for Institutional Structure -- 4.5.3 Changes in Slope Coefficients -- 4.6 Nonlinear Regressions -- 4.6.1 Log-linear Equations -- 4.6.2 Quadratic and Other Powers, Including Inverse -- 4.6.3 Ceiling, Floors, and Kronecker Deltas: Linearizing with Dummy Variables -- 4.7 General Steps for Formulating a Multivariate Regression Equation -- Case Study 5: The Consumption Function -- Case Study 6: Capital Spending -- Problems and Questions -- Chapter 5: Forecasting with a Single-Equation Regression Model -- Introduction -- 5.1 Checking for Normally Distributed Residuals -- 5.1.1 Higher-order Tests for Autocorrelation -- 5.1.2 Tests for Heteroscedasticity -- 5.2 Testing for Equation Stability and Robustness -- 5.2.1 Chow Test for Equation Stability -- 5.2.2 Ramsey Reset Test to Detect Misspecification -- 5.2.3 Recursive Least Squares - Testing Outside The Sample Period -- 5.2.4 Additional Comments on Multicollinearity -- Case Study 7: Demand for Motor Vehicles -- 5.3 Evaluating Forecast Accuracy -- 5.4 The Effect of Forecasting Errors in the Independent Variables -- Case Study 8: Housing Starts -- 5.5 Comparison With Naive Models -- 5.5.1 Same Level or Percentage Change -- 5.5.2 Naive Models using Lagged Values of the Dependent Variables -- 5.6 Buildup of Forecast Error Outside the Sample Period -- 5.6.1 Increased Distance from the Mean Value -- 5.6.2 Unknown Values of Independent Variables -- 5.6.3 Error Buildup in Multi-Period Forecasting -- Case Study 9: The Yen/Dollar Cross-Rate -- Problems and Questions -- Part III. Chapter 6: Elements of Univariate Time-Series Methods -- Introduction -- 6.1 The Basic Time-Series Decomposition Model -- Case Study 10: General Merchandise Sales -- 6.1.1 Identifying the Trend -- 6.1.2 Measuring the Seasonal Factor -- 6.1.3 Separating the Cyclical and Irregular Components -- 6.2 Linear and Nonlinear Trends -- 6.3 Methods of Smoothing Data -- 6.3.1 Arithmetic Moving Averages -- 6.3.2 Exponential Moving Averages -- 6.3.3 Holt-winters Method for Exponential Smoothing -- 6.3.4 Hodrick-prescott Filter -- 6.4 Methods of Seasonal Adjustment -- 6.4.1 Arithmetic and Multiplicative Fixed Weights -- 6.4.2 Variable Weights -- 6.4.3 Treatment of Outlying Observations -- 6.4.4 Seasonal Adjustment Factors With the Census Bureau X-11 Program -- Case Study 11: Manufacturing Inventory Stocks for Textile Mill Products -- Case Study 12: Seasonally Adjusted Gasoline Prices -- Problems and Questions -- Chapter 7: Univariate Time-Series Modeling and Forecasting -- Introduction -- 7.1 The Box-jenkins Approach to Non-Structural Models -- 7.2 Estimating Arma Models -- 7.2.1 First-Order Autoregressive Models - AR(1) -- 7.2.2 Ar(2) Models -- 7.2.3 Ar(N) Models -- 7.2.4 Moving-Average (Ma) Models -- 7.2.5 ARMA Procedures -- 7.3 Stationary and Integrated Series -- 7.4 Identification -- 7.5 Seasonal Factors in ARMA Modeling -- 7.6 Estimation of ARMA Models -- 7.7 Diagnostic Checking and Forecasting -- Case Study 13: New Orders for Machine Tools -- Case Study 14: Inventory/Sales (I/S) Ratio for Sic 37 (Transportation Equipment) -- Case Study 15: Non-Farm Payroll Employment -- Summary -- Problems and Questions -- Part IV -- Chapter 8: Combining Forecasts -- Introduction -- 8.1 Outline of the Theory of Forecast Combination -- 8.2 Major Sources of Forecast Error -- 8.3 Combining Methods of Non-Structural Estimation. 8.4 Combining Structural and Non-Structural Methods -- Case Study 16: Purchases of Consumer Durables -- 8.5 The Role of Judgment in Forecasting -- 8.5.1 Surveys of Sentiment and Buying Plans -- 8.5.2 Sentiment Index for Prospective Home Buyers -- 8.6 The Role of Consensus Forecasts -- Case Study 17: Predicting Interest Rates by Combining Structural and Consensus Forecasts -- 8.7 Adjusting Constant Terms and Slope Coefficients -- 8.7.1 Advantages and Pitfalls of Adjusting the Constant Term -- 8.7.2 Estimating Shifting Parameters -- 8.8 Combining Forecasts: Summary -- Case Study 18: Improving the Forecasting Record for Inflation -- Summary -- Problems and Questions -- Chapter 9: Building and Presenting Short-Term Sales Forecasting Models -- Introduction -- 9.1 Organizing the Sales Forecasting Procedure -- 9.2 Endogenous and Exogenous Variables in Sales Forecasting -- 9.2.1 Macroeconomic Variables -- 9.2.2 Variables Controlled by the Firm -- 9.2.3 Variable Reflecting Competitive Response -- 9.3 The Role of Judgment -- 9.3.1 Deflecting Excess Optimism -- 9.3.2 The Importance of Accurate Macroeconomic Forecasts -- 9.3.3 Assessing Judgmental Inputs -- 9.4 Presenting Sales Forecasts -- 9.4.1 Purchases of Construction Equipment -- 9.4.2 Retail Furniture Sales -- Case Study 19: The Demand for Bicycles -- Case Study 20: New Orders for Machine Tools -- Case Study 21: Purchases of Farm Equipment -- Problems and Questions -- Chapter 10: Methods of Long-term Forecasting -- Introduction -- 10.1 Non-Parametric Methods of Long-term Forecasting -- 10.1.1 Survey Methods -- 10.1.2 Analogy and Precursor Methods -- 10.1.3 Scenario Analysis -- 10.1.4 Delphi Analysis -- 10.2 Statistical Methods of Determining Nonlinear Trends: Nonlinear Growth and Decline, Logistics, and Saturation Curves -- 10.2.1 Nonlinear Growth and Decline Curves -- 10.2.2 Logistics Curves (s-Curves). 10.2.3 Saturation Curves. |
| Record Nr. | UNINA-9910143255603321 |
|
Evans Michael K
|
||
| Malden, MA, : Blackwell Publishers, 2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Practical business forecasting [[electronic resource] /] / Michael K. Evans
| Practical business forecasting [[electronic resource] /] / Michael K. Evans |
| Autore | Evans Michael K |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Malden, MA, : Blackwell Publishers, 2002 |
| Descrizione fisica | 1 online resource (xx, 501 p. ) : ill |
| Disciplina | 658.40355 |
| Soggetto topico |
Business forecasting
Management Theory Management Business & Economics |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-19755-2
9786610197552 0-470-70269-9 0-470-75562-8 1-4051-3780-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Practical Business Forecasting -- Contents -- Acknowledgments -- Preface -- Part I -- Chapter 1: Choosing the Right Type of Forecasting Model -- Introduction -- 1.1 Statistics, Econometrics, and Forecasting -- 1.2 The Concept of Forecasting Accuracy: Compared to What? -- 1.2.1 Structural Shifts in Parameters -- 1.2.2 Model Misspecification -- 1.2.3 Missing, Smoothed, Preliminary, or Inaccurate Data -- 1.2.4 Changing Expectations by Economic Agents -- 1.2.5 Policy Shifts -- 1.2.6 Unexpected Changes in Exogenous Variables -- 1.2.7 Incorrect Assumptions About Exogeneity -- 1.2.8 Error Buildup in Multi-Period Forecasts -- 1.3 Alternative Types of Forecasts -- 1.3.1 Point or Interval -- 1.3.2 Absolute or Conditional -- 1.3.3 Alternative Scenarios Weighed by Probabilities -- 1.3.4 Asymmetric Gains and Losses -- 1.3.5 Single-Period or Multi-Period -- 1.3.6 Short Run or Long Range -- 1.3.7 Forecasting Single or Multiple Variables -- 1.4 Some Common Pitfalls in Building Forecasting Equations -- Problems and Questions -- Chapter 2: Useful Tools for Practical Business Forecasting -- Introduction -- 2.1 Types and Sources of Data -- 2.1.1 Time-Series, Cross-Section, and Panel Data -- 2.1.2 Basic Sources of US Government Data -- 2.1.3 Major Sources of International Government Data -- 2.1.4 Principal Sources of Key Private Sector Data -- 2.2 Collecting Data from the Internet -- 2.3 Forecasting Under Uncertainty -- 2.4 Mean and Variance -- 2.5 Goodness-of-Fit Statistics -- 2.5.1 Covariance and Correlation Coefficients -- 2.5.2 Standard Errors and t-ratios -- 2.5.3 F-ratios and Adjusted R-Squared -- 2.6 Using the Eviews Statistical Package -- 2.7 Utilizing Graphs and Charts -- 2.8 Checklist Before Analyzing Data -- 2.8.1 Adjusting for Seasonal Factors -- 2.8.2 Checking for Outlying Values -- 2.9 Using Logarithms and Elasticities -- Problems and Questions.
Part II -- Chapter 3: The General Linear Regression Model -- Introduction -- 3.1 The General Linear Model -- 3.1.1 The Bivariate Case -- 3.1.2 Desirable Properties of Estimators -- 3.1.3 Expanding to the Multivariate Case -- 3.2 Uses and Misuses of R¯2 -- 3.2.1 Differences Between R2 and R¯2 -- 3.2.2 Pitfalls in Trying to Maximize R¯2 -- 3.2.3 An Example: The Simple Consumption Function -- 3.3 Measuring and Understanding Partial Correlation -- 3.3.1 Covariance and The Correlation Matrix -- 3.3.2 Partial Correlation Coefficients -- 3.3.3 Pitfalls of Stepwise Regression -- 3.4 Testing and Adjusting for Autocorrelation -- 3.4.1 Why Autocorrelation Occurs and What It Means -- 3.4.2 Durbin-watson Statistic to Measure Autocorrelation -- 3.4.3 Autocorrelation Adjustments: Cochrane...orcutt and Hildreth...lu -- 3.4.4 Higher-order Autocorrelation -- 3.4.5 Overstatement of t-ratios when Autocorrelation Is Present -- 3.4.6 Pitfalls of Using The Lagged Dependent Variable -- 3.5 Testing and Adjusting for Heteroscedasticity -- 3.5.1 Causes of Heteroscedasticity in Cross-Section and Time-Series Data -- 3.5.2 Measuring and Testing for Heteroscedasticity -- 3.6 Getting Started: An Example in Eviews -- Case Study 1: Predicting Retail Sales for Hardware Stores -- Case Study 2: German Short-term Interest Rates -- Case Study 3: Lumber Prices -- Problems and Questions -- Chapter 4: Additional Topics For Single-equation Regression Models -- Introduction -- 4.1 Problems Caused By Multicollinearity -- 4.2 Eliminating or Reducing Spurious Trends -- Case Study 4: Demand for Airline Travel -- 4.2.1 Log-linear Transformation -- 4.2.2 Percentage First Differences -- 4.2.3 Ratios -- 4.2.4 Deviations Around Trends -- 4.2.5 Weighted Least Squares -- 4.2.6 Summary and Comparison of Methods -- 4.3 Distributed Lags -- 4.3.1 General Discussion of Distributed Lags. 4.3.2 Polynomial Distributed Lags -- 4.3.3 General Guidelines for Using PDLs -- 4.4 Treatment of Outliers and Issues of Data Adequacy -- 4.4.1 Outliers -- 4.4.2 Missing Observations -- 4.4.3 General Comments on Data Adequacy -- 4.5 Uses and Misuses of Dummy Variables -- 4.5.1 Single-Event Dummy Variables -- 4.5.2 Changes in Dummy Variables for Institutional Structure -- 4.5.3 Changes in Slope Coefficients -- 4.6 Nonlinear Regressions -- 4.6.1 Log-linear Equations -- 4.6.2 Quadratic and Other Powers, Including Inverse -- 4.6.3 Ceiling, Floors, and Kronecker Deltas: Linearizing with Dummy Variables -- 4.7 General Steps for Formulating a Multivariate Regression Equation -- Case Study 5: The Consumption Function -- Case Study 6: Capital Spending -- Problems and Questions -- Chapter 5: Forecasting with a Single-Equation Regression Model -- Introduction -- 5.1 Checking for Normally Distributed Residuals -- 5.1.1 Higher-order Tests for Autocorrelation -- 5.1.2 Tests for Heteroscedasticity -- 5.2 Testing for Equation Stability and Robustness -- 5.2.1 Chow Test for Equation Stability -- 5.2.2 Ramsey Reset Test to Detect Misspecification -- 5.2.3 Recursive Least Squares - Testing Outside The Sample Period -- 5.2.4 Additional Comments on Multicollinearity -- Case Study 7: Demand for Motor Vehicles -- 5.3 Evaluating Forecast Accuracy -- 5.4 The Effect of Forecasting Errors in the Independent Variables -- Case Study 8: Housing Starts -- 5.5 Comparison With Naive Models -- 5.5.1 Same Level or Percentage Change -- 5.5.2 Naive Models using Lagged Values of the Dependent Variables -- 5.6 Buildup of Forecast Error Outside the Sample Period -- 5.6.1 Increased Distance from the Mean Value -- 5.6.2 Unknown Values of Independent Variables -- 5.6.3 Error Buildup in Multi-Period Forecasting -- Case Study 9: The Yen/Dollar Cross-Rate -- Problems and Questions -- Part III. Chapter 6: Elements of Univariate Time-Series Methods -- Introduction -- 6.1 The Basic Time-Series Decomposition Model -- Case Study 10: General Merchandise Sales -- 6.1.1 Identifying the Trend -- 6.1.2 Measuring the Seasonal Factor -- 6.1.3 Separating the Cyclical and Irregular Components -- 6.2 Linear and Nonlinear Trends -- 6.3 Methods of Smoothing Data -- 6.3.1 Arithmetic Moving Averages -- 6.3.2 Exponential Moving Averages -- 6.3.3 Holt-winters Method for Exponential Smoothing -- 6.3.4 Hodrick-prescott Filter -- 6.4 Methods of Seasonal Adjustment -- 6.4.1 Arithmetic and Multiplicative Fixed Weights -- 6.4.2 Variable Weights -- 6.4.3 Treatment of Outlying Observations -- 6.4.4 Seasonal Adjustment Factors With the Census Bureau X-11 Program -- Case Study 11: Manufacturing Inventory Stocks for Textile Mill Products -- Case Study 12: Seasonally Adjusted Gasoline Prices -- Problems and Questions -- Chapter 7: Univariate Time-Series Modeling and Forecasting -- Introduction -- 7.1 The Box-jenkins Approach to Non-Structural Models -- 7.2 Estimating Arma Models -- 7.2.1 First-Order Autoregressive Models - AR(1) -- 7.2.2 Ar(2) Models -- 7.2.3 Ar(N) Models -- 7.2.4 Moving-Average (Ma) Models -- 7.2.5 ARMA Procedures -- 7.3 Stationary and Integrated Series -- 7.4 Identification -- 7.5 Seasonal Factors in ARMA Modeling -- 7.6 Estimation of ARMA Models -- 7.7 Diagnostic Checking and Forecasting -- Case Study 13: New Orders for Machine Tools -- Case Study 14: Inventory/Sales (I/S) Ratio for Sic 37 (Transportation Equipment) -- Case Study 15: Non-Farm Payroll Employment -- Summary -- Problems and Questions -- Part IV -- Chapter 8: Combining Forecasts -- Introduction -- 8.1 Outline of the Theory of Forecast Combination -- 8.2 Major Sources of Forecast Error -- 8.3 Combining Methods of Non-Structural Estimation. 8.4 Combining Structural and Non-Structural Methods -- Case Study 16: Purchases of Consumer Durables -- 8.5 The Role of Judgment in Forecasting -- 8.5.1 Surveys of Sentiment and Buying Plans -- 8.5.2 Sentiment Index for Prospective Home Buyers -- 8.6 The Role of Consensus Forecasts -- Case Study 17: Predicting Interest Rates by Combining Structural and Consensus Forecasts -- 8.7 Adjusting Constant Terms and Slope Coefficients -- 8.7.1 Advantages and Pitfalls of Adjusting the Constant Term -- 8.7.2 Estimating Shifting Parameters -- 8.8 Combining Forecasts: Summary -- Case Study 18: Improving the Forecasting Record for Inflation -- Summary -- Problems and Questions -- Chapter 9: Building and Presenting Short-Term Sales Forecasting Models -- Introduction -- 9.1 Organizing the Sales Forecasting Procedure -- 9.2 Endogenous and Exogenous Variables in Sales Forecasting -- 9.2.1 Macroeconomic Variables -- 9.2.2 Variables Controlled by the Firm -- 9.2.3 Variable Reflecting Competitive Response -- 9.3 The Role of Judgment -- 9.3.1 Deflecting Excess Optimism -- 9.3.2 The Importance of Accurate Macroeconomic Forecasts -- 9.3.3 Assessing Judgmental Inputs -- 9.4 Presenting Sales Forecasts -- 9.4.1 Purchases of Construction Equipment -- 9.4.2 Retail Furniture Sales -- Case Study 19: The Demand for Bicycles -- Case Study 20: New Orders for Machine Tools -- Case Study 21: Purchases of Farm Equipment -- Problems and Questions -- Chapter 10: Methods of Long-term Forecasting -- Introduction -- 10.1 Non-Parametric Methods of Long-term Forecasting -- 10.1.1 Survey Methods -- 10.1.2 Analogy and Precursor Methods -- 10.1.3 Scenario Analysis -- 10.1.4 Delphi Analysis -- 10.2 Statistical Methods of Determining Nonlinear Trends: Nonlinear Growth and Decline, Logistics, and Saturation Curves -- 10.2.1 Nonlinear Growth and Decline Curves -- 10.2.2 Logistics Curves (s-Curves). 10.2.3 Saturation Curves. |
| Record Nr. | UNISA-996210535703316 |
|
Evans Michael K
|
||
| Malden, MA, : Blackwell Publishers, 2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Practical business forecasting [[electronic resource] /] / Michael K. Evans
| Practical business forecasting [[electronic resource] /] / Michael K. Evans |
| Autore | Evans Michael K |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Malden, MA, : Blackwell Publishers, 2002 |
| Descrizione fisica | 1 online resource (xx, 501 p. ) : ill |
| Disciplina | 658.40355 |
| Soggetto topico |
Business forecasting
Management Theory Management Business & Economics |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-19755-2
9786610197552 0-470-70269-9 0-470-75562-8 1-4051-3780-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Practical Business Forecasting -- Contents -- Acknowledgments -- Preface -- Part I -- Chapter 1: Choosing the Right Type of Forecasting Model -- Introduction -- 1.1 Statistics, Econometrics, and Forecasting -- 1.2 The Concept of Forecasting Accuracy: Compared to What? -- 1.2.1 Structural Shifts in Parameters -- 1.2.2 Model Misspecification -- 1.2.3 Missing, Smoothed, Preliminary, or Inaccurate Data -- 1.2.4 Changing Expectations by Economic Agents -- 1.2.5 Policy Shifts -- 1.2.6 Unexpected Changes in Exogenous Variables -- 1.2.7 Incorrect Assumptions About Exogeneity -- 1.2.8 Error Buildup in Multi-Period Forecasts -- 1.3 Alternative Types of Forecasts -- 1.3.1 Point or Interval -- 1.3.2 Absolute or Conditional -- 1.3.3 Alternative Scenarios Weighed by Probabilities -- 1.3.4 Asymmetric Gains and Losses -- 1.3.5 Single-Period or Multi-Period -- 1.3.6 Short Run or Long Range -- 1.3.7 Forecasting Single or Multiple Variables -- 1.4 Some Common Pitfalls in Building Forecasting Equations -- Problems and Questions -- Chapter 2: Useful Tools for Practical Business Forecasting -- Introduction -- 2.1 Types and Sources of Data -- 2.1.1 Time-Series, Cross-Section, and Panel Data -- 2.1.2 Basic Sources of US Government Data -- 2.1.3 Major Sources of International Government Data -- 2.1.4 Principal Sources of Key Private Sector Data -- 2.2 Collecting Data from the Internet -- 2.3 Forecasting Under Uncertainty -- 2.4 Mean and Variance -- 2.5 Goodness-of-Fit Statistics -- 2.5.1 Covariance and Correlation Coefficients -- 2.5.2 Standard Errors and t-ratios -- 2.5.3 F-ratios and Adjusted R-Squared -- 2.6 Using the Eviews Statistical Package -- 2.7 Utilizing Graphs and Charts -- 2.8 Checklist Before Analyzing Data -- 2.8.1 Adjusting for Seasonal Factors -- 2.8.2 Checking for Outlying Values -- 2.9 Using Logarithms and Elasticities -- Problems and Questions.
Part II -- Chapter 3: The General Linear Regression Model -- Introduction -- 3.1 The General Linear Model -- 3.1.1 The Bivariate Case -- 3.1.2 Desirable Properties of Estimators -- 3.1.3 Expanding to the Multivariate Case -- 3.2 Uses and Misuses of R¯2 -- 3.2.1 Differences Between R2 and R¯2 -- 3.2.2 Pitfalls in Trying to Maximize R¯2 -- 3.2.3 An Example: The Simple Consumption Function -- 3.3 Measuring and Understanding Partial Correlation -- 3.3.1 Covariance and The Correlation Matrix -- 3.3.2 Partial Correlation Coefficients -- 3.3.3 Pitfalls of Stepwise Regression -- 3.4 Testing and Adjusting for Autocorrelation -- 3.4.1 Why Autocorrelation Occurs and What It Means -- 3.4.2 Durbin-watson Statistic to Measure Autocorrelation -- 3.4.3 Autocorrelation Adjustments: Cochrane...orcutt and Hildreth...lu -- 3.4.4 Higher-order Autocorrelation -- 3.4.5 Overstatement of t-ratios when Autocorrelation Is Present -- 3.4.6 Pitfalls of Using The Lagged Dependent Variable -- 3.5 Testing and Adjusting for Heteroscedasticity -- 3.5.1 Causes of Heteroscedasticity in Cross-Section and Time-Series Data -- 3.5.2 Measuring and Testing for Heteroscedasticity -- 3.6 Getting Started: An Example in Eviews -- Case Study 1: Predicting Retail Sales for Hardware Stores -- Case Study 2: German Short-term Interest Rates -- Case Study 3: Lumber Prices -- Problems and Questions -- Chapter 4: Additional Topics For Single-equation Regression Models -- Introduction -- 4.1 Problems Caused By Multicollinearity -- 4.2 Eliminating or Reducing Spurious Trends -- Case Study 4: Demand for Airline Travel -- 4.2.1 Log-linear Transformation -- 4.2.2 Percentage First Differences -- 4.2.3 Ratios -- 4.2.4 Deviations Around Trends -- 4.2.5 Weighted Least Squares -- 4.2.6 Summary and Comparison of Methods -- 4.3 Distributed Lags -- 4.3.1 General Discussion of Distributed Lags. 4.3.2 Polynomial Distributed Lags -- 4.3.3 General Guidelines for Using PDLs -- 4.4 Treatment of Outliers and Issues of Data Adequacy -- 4.4.1 Outliers -- 4.4.2 Missing Observations -- 4.4.3 General Comments on Data Adequacy -- 4.5 Uses and Misuses of Dummy Variables -- 4.5.1 Single-Event Dummy Variables -- 4.5.2 Changes in Dummy Variables for Institutional Structure -- 4.5.3 Changes in Slope Coefficients -- 4.6 Nonlinear Regressions -- 4.6.1 Log-linear Equations -- 4.6.2 Quadratic and Other Powers, Including Inverse -- 4.6.3 Ceiling, Floors, and Kronecker Deltas: Linearizing with Dummy Variables -- 4.7 General Steps for Formulating a Multivariate Regression Equation -- Case Study 5: The Consumption Function -- Case Study 6: Capital Spending -- Problems and Questions -- Chapter 5: Forecasting with a Single-Equation Regression Model -- Introduction -- 5.1 Checking for Normally Distributed Residuals -- 5.1.1 Higher-order Tests for Autocorrelation -- 5.1.2 Tests for Heteroscedasticity -- 5.2 Testing for Equation Stability and Robustness -- 5.2.1 Chow Test for Equation Stability -- 5.2.2 Ramsey Reset Test to Detect Misspecification -- 5.2.3 Recursive Least Squares - Testing Outside The Sample Period -- 5.2.4 Additional Comments on Multicollinearity -- Case Study 7: Demand for Motor Vehicles -- 5.3 Evaluating Forecast Accuracy -- 5.4 The Effect of Forecasting Errors in the Independent Variables -- Case Study 8: Housing Starts -- 5.5 Comparison With Naive Models -- 5.5.1 Same Level or Percentage Change -- 5.5.2 Naive Models using Lagged Values of the Dependent Variables -- 5.6 Buildup of Forecast Error Outside the Sample Period -- 5.6.1 Increased Distance from the Mean Value -- 5.6.2 Unknown Values of Independent Variables -- 5.6.3 Error Buildup in Multi-Period Forecasting -- Case Study 9: The Yen/Dollar Cross-Rate -- Problems and Questions -- Part III. Chapter 6: Elements of Univariate Time-Series Methods -- Introduction -- 6.1 The Basic Time-Series Decomposition Model -- Case Study 10: General Merchandise Sales -- 6.1.1 Identifying the Trend -- 6.1.2 Measuring the Seasonal Factor -- 6.1.3 Separating the Cyclical and Irregular Components -- 6.2 Linear and Nonlinear Trends -- 6.3 Methods of Smoothing Data -- 6.3.1 Arithmetic Moving Averages -- 6.3.2 Exponential Moving Averages -- 6.3.3 Holt-winters Method for Exponential Smoothing -- 6.3.4 Hodrick-prescott Filter -- 6.4 Methods of Seasonal Adjustment -- 6.4.1 Arithmetic and Multiplicative Fixed Weights -- 6.4.2 Variable Weights -- 6.4.3 Treatment of Outlying Observations -- 6.4.4 Seasonal Adjustment Factors With the Census Bureau X-11 Program -- Case Study 11: Manufacturing Inventory Stocks for Textile Mill Products -- Case Study 12: Seasonally Adjusted Gasoline Prices -- Problems and Questions -- Chapter 7: Univariate Time-Series Modeling and Forecasting -- Introduction -- 7.1 The Box-jenkins Approach to Non-Structural Models -- 7.2 Estimating Arma Models -- 7.2.1 First-Order Autoregressive Models - AR(1) -- 7.2.2 Ar(2) Models -- 7.2.3 Ar(N) Models -- 7.2.4 Moving-Average (Ma) Models -- 7.2.5 ARMA Procedures -- 7.3 Stationary and Integrated Series -- 7.4 Identification -- 7.5 Seasonal Factors in ARMA Modeling -- 7.6 Estimation of ARMA Models -- 7.7 Diagnostic Checking and Forecasting -- Case Study 13: New Orders for Machine Tools -- Case Study 14: Inventory/Sales (I/S) Ratio for Sic 37 (Transportation Equipment) -- Case Study 15: Non-Farm Payroll Employment -- Summary -- Problems and Questions -- Part IV -- Chapter 8: Combining Forecasts -- Introduction -- 8.1 Outline of the Theory of Forecast Combination -- 8.2 Major Sources of Forecast Error -- 8.3 Combining Methods of Non-Structural Estimation. 8.4 Combining Structural and Non-Structural Methods -- Case Study 16: Purchases of Consumer Durables -- 8.5 The Role of Judgment in Forecasting -- 8.5.1 Surveys of Sentiment and Buying Plans -- 8.5.2 Sentiment Index for Prospective Home Buyers -- 8.6 The Role of Consensus Forecasts -- Case Study 17: Predicting Interest Rates by Combining Structural and Consensus Forecasts -- 8.7 Adjusting Constant Terms and Slope Coefficients -- 8.7.1 Advantages and Pitfalls of Adjusting the Constant Term -- 8.7.2 Estimating Shifting Parameters -- 8.8 Combining Forecasts: Summary -- Case Study 18: Improving the Forecasting Record for Inflation -- Summary -- Problems and Questions -- Chapter 9: Building and Presenting Short-Term Sales Forecasting Models -- Introduction -- 9.1 Organizing the Sales Forecasting Procedure -- 9.2 Endogenous and Exogenous Variables in Sales Forecasting -- 9.2.1 Macroeconomic Variables -- 9.2.2 Variables Controlled by the Firm -- 9.2.3 Variable Reflecting Competitive Response -- 9.3 The Role of Judgment -- 9.3.1 Deflecting Excess Optimism -- 9.3.2 The Importance of Accurate Macroeconomic Forecasts -- 9.3.3 Assessing Judgmental Inputs -- 9.4 Presenting Sales Forecasts -- 9.4.1 Purchases of Construction Equipment -- 9.4.2 Retail Furniture Sales -- Case Study 19: The Demand for Bicycles -- Case Study 20: New Orders for Machine Tools -- Case Study 21: Purchases of Farm Equipment -- Problems and Questions -- Chapter 10: Methods of Long-term Forecasting -- Introduction -- 10.1 Non-Parametric Methods of Long-term Forecasting -- 10.1.1 Survey Methods -- 10.1.2 Analogy and Precursor Methods -- 10.1.3 Scenario Analysis -- 10.1.4 Delphi Analysis -- 10.2 Statistical Methods of Determining Nonlinear Trends: Nonlinear Growth and Decline, Logistics, and Saturation Curves -- 10.2.1 Nonlinear Growth and Decline Curves -- 10.2.2 Logistics Curves (s-Curves). 10.2.3 Saturation Curves. |
| Record Nr. | UNINA-9910830883503321 |
|
Evans Michael K
|
||
| Malden, MA, : Blackwell Publishers, 2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Practical business forecasting / / Michael K. Evans
| Practical business forecasting / / Michael K. Evans |
| Autore | Evans Michael K |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Malden, MA, : Blackwell Publishers, 2002 |
| Descrizione fisica | 1 online resource (xx, 501 p. ) : ill |
| Disciplina | 658.4/0355 |
| Soggetto topico | Business forecasting |
| ISBN |
9786610197552
9781280197550 1280197552 9780470702697 0470702699 9780470755624 0470755628 9781405137805 1405137800 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Practical Business Forecasting -- Contents -- Acknowledgments -- Preface -- Part I -- Chapter 1: Choosing the Right Type of Forecasting Model -- Introduction -- 1.1 Statistics, Econometrics, and Forecasting -- 1.2 The Concept of Forecasting Accuracy: Compared to What? -- 1.2.1 Structural Shifts in Parameters -- 1.2.2 Model Misspecification -- 1.2.3 Missing, Smoothed, Preliminary, or Inaccurate Data -- 1.2.4 Changing Expectations by Economic Agents -- 1.2.5 Policy Shifts -- 1.2.6 Unexpected Changes in Exogenous Variables -- 1.2.7 Incorrect Assumptions About Exogeneity -- 1.2.8 Error Buildup in Multi-Period Forecasts -- 1.3 Alternative Types of Forecasts -- 1.3.1 Point or Interval -- 1.3.2 Absolute or Conditional -- 1.3.3 Alternative Scenarios Weighed by Probabilities -- 1.3.4 Asymmetric Gains and Losses -- 1.3.5 Single-Period or Multi-Period -- 1.3.6 Short Run or Long Range -- 1.3.7 Forecasting Single or Multiple Variables -- 1.4 Some Common Pitfalls in Building Forecasting Equations -- Problems and Questions -- Chapter 2: Useful Tools for Practical Business Forecasting -- Introduction -- 2.1 Types and Sources of Data -- 2.1.1 Time-Series, Cross-Section, and Panel Data -- 2.1.2 Basic Sources of US Government Data -- 2.1.3 Major Sources of International Government Data -- 2.1.4 Principal Sources of Key Private Sector Data -- 2.2 Collecting Data from the Internet -- 2.3 Forecasting Under Uncertainty -- 2.4 Mean and Variance -- 2.5 Goodness-of-Fit Statistics -- 2.5.1 Covariance and Correlation Coefficients -- 2.5.2 Standard Errors and t-ratios -- 2.5.3 F-ratios and Adjusted R-Squared -- 2.6 Using the Eviews Statistical Package -- 2.7 Utilizing Graphs and Charts -- 2.8 Checklist Before Analyzing Data -- 2.8.1 Adjusting for Seasonal Factors -- 2.8.2 Checking for Outlying Values -- 2.9 Using Logarithms and Elasticities -- Problems and Questions.
Part II -- Chapter 3: The General Linear Regression Model -- Introduction -- 3.1 The General Linear Model -- 3.1.1 The Bivariate Case -- 3.1.2 Desirable Properties of Estimators -- 3.1.3 Expanding to the Multivariate Case -- 3.2 Uses and Misuses of R¯2 -- 3.2.1 Differences Between R2 and R¯2 -- 3.2.2 Pitfalls in Trying to Maximize R¯2 -- 3.2.3 An Example: The Simple Consumption Function -- 3.3 Measuring and Understanding Partial Correlation -- 3.3.1 Covariance and The Correlation Matrix -- 3.3.2 Partial Correlation Coefficients -- 3.3.3 Pitfalls of Stepwise Regression -- 3.4 Testing and Adjusting for Autocorrelation -- 3.4.1 Why Autocorrelation Occurs and What It Means -- 3.4.2 Durbin-watson Statistic to Measure Autocorrelation -- 3.4.3 Autocorrelation Adjustments: Cochrane...orcutt and Hildreth...lu -- 3.4.4 Higher-order Autocorrelation -- 3.4.5 Overstatement of t-ratios when Autocorrelation Is Present -- 3.4.6 Pitfalls of Using The Lagged Dependent Variable -- 3.5 Testing and Adjusting for Heteroscedasticity -- 3.5.1 Causes of Heteroscedasticity in Cross-Section and Time-Series Data -- 3.5.2 Measuring and Testing for Heteroscedasticity -- 3.6 Getting Started: An Example in Eviews -- Case Study 1: Predicting Retail Sales for Hardware Stores -- Case Study 2: German Short-term Interest Rates -- Case Study 3: Lumber Prices -- Problems and Questions -- Chapter 4: Additional Topics For Single-equation Regression Models -- Introduction -- 4.1 Problems Caused By Multicollinearity -- 4.2 Eliminating or Reducing Spurious Trends -- Case Study 4: Demand for Airline Travel -- 4.2.1 Log-linear Transformation -- 4.2.2 Percentage First Differences -- 4.2.3 Ratios -- 4.2.4 Deviations Around Trends -- 4.2.5 Weighted Least Squares -- 4.2.6 Summary and Comparison of Methods -- 4.3 Distributed Lags -- 4.3.1 General Discussion of Distributed Lags. 4.3.2 Polynomial Distributed Lags -- 4.3.3 General Guidelines for Using PDLs -- 4.4 Treatment of Outliers and Issues of Data Adequacy -- 4.4.1 Outliers -- 4.4.2 Missing Observations -- 4.4.3 General Comments on Data Adequacy -- 4.5 Uses and Misuses of Dummy Variables -- 4.5.1 Single-Event Dummy Variables -- 4.5.2 Changes in Dummy Variables for Institutional Structure -- 4.5.3 Changes in Slope Coefficients -- 4.6 Nonlinear Regressions -- 4.6.1 Log-linear Equations -- 4.6.2 Quadratic and Other Powers, Including Inverse -- 4.6.3 Ceiling, Floors, and Kronecker Deltas: Linearizing with Dummy Variables -- 4.7 General Steps for Formulating a Multivariate Regression Equation -- Case Study 5: The Consumption Function -- Case Study 6: Capital Spending -- Problems and Questions -- Chapter 5: Forecasting with a Single-Equation Regression Model -- Introduction -- 5.1 Checking for Normally Distributed Residuals -- 5.1.1 Higher-order Tests for Autocorrelation -- 5.1.2 Tests for Heteroscedasticity -- 5.2 Testing for Equation Stability and Robustness -- 5.2.1 Chow Test for Equation Stability -- 5.2.2 Ramsey Reset Test to Detect Misspecification -- 5.2.3 Recursive Least Squares - Testing Outside The Sample Period -- 5.2.4 Additional Comments on Multicollinearity -- Case Study 7: Demand for Motor Vehicles -- 5.3 Evaluating Forecast Accuracy -- 5.4 The Effect of Forecasting Errors in the Independent Variables -- Case Study 8: Housing Starts -- 5.5 Comparison With Naive Models -- 5.5.1 Same Level or Percentage Change -- 5.5.2 Naive Models using Lagged Values of the Dependent Variables -- 5.6 Buildup of Forecast Error Outside the Sample Period -- 5.6.1 Increased Distance from the Mean Value -- 5.6.2 Unknown Values of Independent Variables -- 5.6.3 Error Buildup in Multi-Period Forecasting -- Case Study 9: The Yen/Dollar Cross-Rate -- Problems and Questions -- Part III. Chapter 6: Elements of Univariate Time-Series Methods -- Introduction -- 6.1 The Basic Time-Series Decomposition Model -- Case Study 10: General Merchandise Sales -- 6.1.1 Identifying the Trend -- 6.1.2 Measuring the Seasonal Factor -- 6.1.3 Separating the Cyclical and Irregular Components -- 6.2 Linear and Nonlinear Trends -- 6.3 Methods of Smoothing Data -- 6.3.1 Arithmetic Moving Averages -- 6.3.2 Exponential Moving Averages -- 6.3.3 Holt-winters Method for Exponential Smoothing -- 6.3.4 Hodrick-prescott Filter -- 6.4 Methods of Seasonal Adjustment -- 6.4.1 Arithmetic and Multiplicative Fixed Weights -- 6.4.2 Variable Weights -- 6.4.3 Treatment of Outlying Observations -- 6.4.4 Seasonal Adjustment Factors With the Census Bureau X-11 Program -- Case Study 11: Manufacturing Inventory Stocks for Textile Mill Products -- Case Study 12: Seasonally Adjusted Gasoline Prices -- Problems and Questions -- Chapter 7: Univariate Time-Series Modeling and Forecasting -- Introduction -- 7.1 The Box-jenkins Approach to Non-Structural Models -- 7.2 Estimating Arma Models -- 7.2.1 First-Order Autoregressive Models - AR(1) -- 7.2.2 Ar(2) Models -- 7.2.3 Ar(N) Models -- 7.2.4 Moving-Average (Ma) Models -- 7.2.5 ARMA Procedures -- 7.3 Stationary and Integrated Series -- 7.4 Identification -- 7.5 Seasonal Factors in ARMA Modeling -- 7.6 Estimation of ARMA Models -- 7.7 Diagnostic Checking and Forecasting -- Case Study 13: New Orders for Machine Tools -- Case Study 14: Inventory/Sales (I/S) Ratio for Sic 37 (Transportation Equipment) -- Case Study 15: Non-Farm Payroll Employment -- Summary -- Problems and Questions -- Part IV -- Chapter 8: Combining Forecasts -- Introduction -- 8.1 Outline of the Theory of Forecast Combination -- 8.2 Major Sources of Forecast Error -- 8.3 Combining Methods of Non-Structural Estimation. 8.4 Combining Structural and Non-Structural Methods -- Case Study 16: Purchases of Consumer Durables -- 8.5 The Role of Judgment in Forecasting -- 8.5.1 Surveys of Sentiment and Buying Plans -- 8.5.2 Sentiment Index for Prospective Home Buyers -- 8.6 The Role of Consensus Forecasts -- Case Study 17: Predicting Interest Rates by Combining Structural and Consensus Forecasts -- 8.7 Adjusting Constant Terms and Slope Coefficients -- 8.7.1 Advantages and Pitfalls of Adjusting the Constant Term -- 8.7.2 Estimating Shifting Parameters -- 8.8 Combining Forecasts: Summary -- Case Study 18: Improving the Forecasting Record for Inflation -- Summary -- Problems and Questions -- Chapter 9: Building and Presenting Short-Term Sales Forecasting Models -- Introduction -- 9.1 Organizing the Sales Forecasting Procedure -- 9.2 Endogenous and Exogenous Variables in Sales Forecasting -- 9.2.1 Macroeconomic Variables -- 9.2.2 Variables Controlled by the Firm -- 9.2.3 Variable Reflecting Competitive Response -- 9.3 The Role of Judgment -- 9.3.1 Deflecting Excess Optimism -- 9.3.2 The Importance of Accurate Macroeconomic Forecasts -- 9.3.3 Assessing Judgmental Inputs -- 9.4 Presenting Sales Forecasts -- 9.4.1 Purchases of Construction Equipment -- 9.4.2 Retail Furniture Sales -- Case Study 19: The Demand for Bicycles -- Case Study 20: New Orders for Machine Tools -- Case Study 21: Purchases of Farm Equipment -- Problems and Questions -- Chapter 10: Methods of Long-term Forecasting -- Introduction -- 10.1 Non-Parametric Methods of Long-term Forecasting -- 10.1.1 Survey Methods -- 10.1.2 Analogy and Precursor Methods -- 10.1.3 Scenario Analysis -- 10.1.4 Delphi Analysis -- 10.2 Statistical Methods of Determining Nonlinear Trends: Nonlinear Growth and Decline, Logistics, and Saturation Curves -- 10.2.1 Nonlinear Growth and Decline Curves -- 10.2.2 Logistics Curves (s-Curves). 10.2.3 Saturation Curves. |
| Record Nr. | UNINA-9911020168703321 |
|
Evans Michael K
|
||
| Malden, MA, : Blackwell Publishers, 2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||