Practical Business Forecasting

Practical Business Forecasting

Michael K. Evans

Language: English

Pages: 522

ISBN: 0631220666

Format: PDF / Kindle (mobi) / ePub

Stressing the concrete applications of economic forecasting, Practical Business Forecasting is accessible to a wide-range of readers, requiring only a familiarity with basic statistics. The text focuses on the use of models in forecasting, explaining how to build practical forecasting models that produce optimal results. In a clear and detailed format, the text covers estimating and forecasting with single and multi- equation models, univariate time-series modeling, and determining forecasting accuracy. Additionally, case studies throughout the book illustrate how the models are actually estimated and adjusted to generate accurate forecasts. After reading this text, students and readers should have a clearer idea of the reasoning and choices involved in building models, and a deeper foundation in estimating econometric models used in practical business forecasting.

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the government are revised. The issuing agencies named above make an attempt to provide monthly or quarterly data as quickly as possible after the period has ended. These releases are generally known as “advance” or “preliminary” data. In general, these data are then revised over the next several months. They are then revised again using annual benchmarks; these revisions usually incorporate changing seasonal factors. Finally, they are revised again using five-year censuses of the agricultural,

lagged dependent variables on an a-priori basis, but it does raise a red flag. One of the tenets of the classical linear model, as will be shown in the next chapter, is that the values of all the independent variables are known at the time of forecast. Obviously that is not the case when the lagged dependent variable is used in multi-period forecasting. Hence it is advisable to use a different approach when multi-period forecasts are required. 1.3.6 SHORT RUN OR LONG RANGE To a certain

Parameters 273 273 275 276 280 283 285 290 290 291 292 294 297 8.7.1 8.8 Combining Forecasts: Summary Case Study 18: Improving the Forecasting Record for Inflation Summary Problems and Questions 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 9.2.2 9.2.3 9.3 Macroeconomic Variables Variables Controlled by the Firm Variables Reflecting Competitive

for consumption, monthly data for industrial production or housing starts, daily data for the stock market, annual data for capital spending, quarterly data for individual company sales and profits, or monthly levels of production and inventories. Most econometric and forecasting books cover “regression models” and “time-series models.” The first category includes the construction of models based on underlying economic theory; which are generally known as structural models. The second category

errors of an estimate, is known as White’s correction. Another method, known as the Newey–West correction, gives consistent estimates even if both autocorrelation and heteroscedasticity are present (which is likely to be the case in standard time-series analysis). Generally the results do not vary much between the two methods. While these two tests are theoretically sound, they still are not entirely satisfactory in the sense that, if a distortion or bias is introduced into the parameter estimate

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