Lecture_1_Introduction

Lecture_1_Introduction - 1 Lecture 1 Introduction to...

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Lecture 1 Introduction to Forecasting Read: (WK Ch 1) EC 413/513 Economic Forecast and Analysis (Professor Lee) 1
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Major Questions in this lecture 1. Why and what to forecast? 2. Based on What? 3. Is your forecast good or bad? 1. Why and what to forecast? Examples of Forecasting Questions for discussion: Q.1. Should I buy or sell stocks? Should we buy our house now or later? Did the price touch the bottom? U-shaped? L-shaped? Q.2 How many donuts to prepare each morning? Q.3 Should we build more plants for making ships/cars? Q.4 Forecasting the demand for boats next year? Q.5 Can we predict recession? 2
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More Examples of Forecasting (Ex 1) Sales of the Gap Store for each month for the next two years. (Ex 2) Sales of SUVs to be sold for the next 5 years in the US. (Ex 3) Demand for tickets; passengers-miles; Revenue forecasts for TWA; (industry passenger traffic industry share of TWA) (Ex 4) # of expected passengers for tomorrow for each flight of the Southwest Airlines (Ex 5) Who will pay only the minimum payment for credit cards? (Ex 6) Gas / electricity peak-load demand for each month in a certain region (Ex 7) How many spare parts to carry for a submarine? How many orders to make; Supply Chain Management (Ex 8) Demand for police patrol services (Ex 9) Tomorrow’s return (or price) of the Disney stock; Interest rates; Foreign exchange rates 3
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(Ex 10) Economic and financial forecasts for the state in the coming year?? Unemployment rates? (Ex 11) Department of Defense (DOE)’s budget plans (Ex 12) California Legislative Analysis Office (LAO) Forecasts of national economics variables (growth rates, interest rates, taxes. .) California economics submodels (population, employment, housing activities. .) State revenue submodels (personal income, sales, vehicle registrations,.) Why is forecasting important? ; anticipating the course of events. . Fundamental Questions Our forecasts are never correct , but do we still need forecasting? Why are some questions more difficult? Nature of the data? Structural Changes? More Randomness? 4
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2. Based on What? Past values Past errors Relationships with other variables How? Business Forecasting Collaborative Forecasting (Manufacturers and retailers share the information; Target, Goodyear,. .) Computers (software); must use statistical models Subjective forecasting methods o Salesperson o Surveys o Jury of executive opinion, Delphi method Marketing research, product life cycle models. . o E.g., Bass (1969, 2004) Regression Model S t = a + b Y t-1 + c Y t-1 2 + error where S t = Sales and Y t-1 = # of previous buyers We rely on statistical models 5
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Tools: Statistical Models (which we will learn) Time Series Models o Moving Average and Exponential Smoothing Models o Box-Jenkins ARIMA Models o Multivariate Time Series Models (VAR models) Regression Models
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This note was uploaded on 02/29/2012 for the course EC 513 taught by Professor Staff during the Fall '08 term at Alabama.

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Lecture_1_Introduction - 1 Lecture 1 Introduction to...

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