Samuelson - Managerial Economics 7e

Page 185

162

Chapter 4

Estimating and Forecasting Demand

Of course, the speculative run-up in housing prices, after peaking in 2006, culminated in unprecedented price declines over the next two years of 30 to 50 percent. Why did so many home buyers, homeowners, lenders, and financial institutions believe that housing prices could go nowhere but up? Simple psychology accounts for a large part of the answer. Such beliefs are supported by a strong (often unconscious) bias toward overoptimism. According to surveys taken over the last 20 years, homeowners report that they expect housing prices to increase in the future by some 10 percent per year. These predictions have been very stable—before and during the price run-up and even after housing prices plunged. Moreover, individuals selectively cling to reasons—more qualified buyers, high demand in growing cities, the scarcity of land and housing in the most desirable locations—that support these beliefs, while overlooking or dismissing disconfirming evidence. To sum up, the way to overcome these psychological biases is to keep firmly in mind the 50-year “big picture” of house price movements.

Barometric Models Barometric models search for patterns among different variables over time. Consider a firm that produces oil drilling equipment. Management naturally would like to forecast demand for its product. It turns out that the seismic crew count, an index of the number of teams surveying possible drilling sites, gives a good indication as to changes in future demand for drilling equipment. For this reason, we call the seismic crew a leading indicator of the demand for drilling equipment. Economists have identified many well-known leading indicators. The number of building permits lead the number of housing starts. Stock market indices (such as the Dow Jones Industrial Average) indicate future increases and decreases in economic activity (expansions or recessions). Such indicators, however, are not without certain problems. 1. Leading indicators are not always accurate. According to one humorous economic saying, declines in the stock market have predicted 14 of the last 8 recessions. 2. The amount of time between the change in the leading indicator and the change in the forecasted series varies. Leading indicators may say a change is coming, but they often cannot say exactly when. 3. The change in the leading indicator rarely gives much information about the size of the change in the forecasted series. Frequently, leading indicators are averaged to form a composite leading indicator. This helps eliminate some of the randomness and makes the indicator


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Bargaining

1min
page 439

Market Entry

4min
pages 437-438

Equilibrium Strategies

18min
pages 428-436

Strategic Commitments

4min
pages 399-400

Price Rigidity and Kinked Demand

3min
pages 389-390

Price Wars and the Prisoner’s Dilemma

17min
pages 391-398

Competition among Symmetric Firms

5min
pages 386-388

Concentration and Prices

6min
pages 381-383

Industry Concentration

8min
pages 376-380

Natural Monopolies

32min
pages 355-371

Five-Forces Framework

3min
pages 374-375

Barriers to Entry

14min
pages 345-351

Cartels

6min
pages 352-354

Tariffs and Quotas

22min
pages 329-341

Private Markets: Benefits and Costs

21min
pages 319-328

Decisions of the Competitive Firm

4min
pages 312-314

Multiple Products

37min
pages 282-303

Shifts in Demand and Supply

2min
pages 310-311

Market Equilibrium

8min
pages 315-318

Economies of Scope

6min
pages 275-277

Returns to Scale

8min
pages 270-274

A Single Product

3min
pages 278-279

The Shut-Down Rule

3min
pages 280-281

Short-Run Costs

8min
pages 260-264

Long-Run Costs

10min
pages 265-269

Profit Maximization with Limited Capacity: Ordering a Best Seller

6min
pages 257-259

Fixed and Sunk Costs

7min
pages 254-256

Opportunity Costs and Economic Profits

8min
pages 250-253

Multiple Plants

1min
page 234

Returns to Scale

4min
pages 221-222

Estimating Production Functions

1min
page 233

Forecasting Performance

5min
pages 186-188

Optimal Use of an Input

4min
pages 219-220

Barometric Models

2min
page 185

Fitting a Simple Trend

14min
pages 176-184

Interpreting Regression Statistics

10min
pages 164-168

Potential Problems in Regression

8min
pages 169-173

Time-Series Models

2min
pages 174-175

Uncontrolled Market Data

2min
page 155

Price Discrimination

9min
pages 122-125

Consumer Surveys

4min
pages 152-153

Optimal Markup Pricing

8min
pages 118-121

Controlled Market Studies

2min
page 154

Other Elasticities

4min
pages 111-112

Maximizing Revenue

1min
page 117

General Determinants of Demand

2min
page 105

The Demand Function

4min
pages 101-102

Step 6: Perform Sensitivity Analysis

9min
pages 35-38

The Aim of This Book

10min
pages 43-47

Public Decisions

8min
pages 39-42

Step 2: Determine the Objective

4min
pages 30-31

Step 3: Explore the Alternatives

2min
page 32

Step 4: Predict the Consequences

2min
page 33

Marginal Revenue

1min
page 67

Step 5: Make a Choice

2min
page 34
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