appendix d: econometric estimation · supported by fisher center for the strategic use of...

1

254

Appendix D:

Econometric

Estimation

255

• Method requires cross-section or time series data

• Regression

–E.g., survey data or test marketing data, or already existing market data

Econometrics is Estimation of

Statistical Relations of

Economic Variables

*

256

• Synthesize large amounts of info

in an effective way

• provides framework for

systematic thought

– assumptions explicit

*

257

Typical Regression Analysis

Unit sales = a + b1 price + b2

advertising + bi other variable + e

or

Thomas T. Nagle & Reed K. Holden, “The Strategy and Tactics of Pricing: A Guide to Profitable

Decision Making,” Second Edition 1995

Market share = a + b1 lagged market share

+ b2 price + bi other variable + e

*

2

258

Other Control Variables

• Adding variables that might

affect sales

–Growth in GNP

–Growth in population

–Season

–Income levelThomas T. Nagle & Reed K. Holden, “The Strategy and Tactics of Pricing: A Guide to Profitable


*

259

Analyzing Sales & Panel

• Usually by linear regression

analysis.

260

Problems with Regressions

• If only little historical variation, price statistics cannot reveal the effect of price changes.



http://www.freebsd.org/g

ifs/bug.jpg

N

261

• Need to assume a specific

mathematical model for the

relationship between price and

sale.

• If specification is incorrect, the

results will be incorrect



3

262

• Also, the results can only be

values over the range of price

levels for which data was

available

263

Econometrics

• Predicting the future requires

assumption that behavior is like

the past.



264

Ordinary Least Squares (OLS)

• Use linear regression models

to quantify linear relationships

among variables

• Can estimate OLS regression

using any statistical software

package (STATA, SAS,

EXCEL, Minitab, etc.)http://www.chass.utoronto.ca/~murdockj/eco310/F03_310_six.pdf

*

265

4

266

267

268

*

269

Interpretation of Results

• Standard Error (s.e.):

• – Indicates how precisely the

coefficient is estimated

• – Used in calculation of test

statistics and in constructing

the confidence intervals

5

270

Interpretation of Results,

cont’d• T-statistic

• – Tests hypothesis that

coefficient is (statistically) =β0

(null hypothesis)

271

Example: Demand Estimation

for Newsprint

- For newspapers,

directories etc.

http://www.andrewdegrandpre.com/newspaper_roll_centered1.jpg

http://homepage.mac.com/albertkwa

n/Chronicle_Blog/C1258471436/E1

867671640/Media/newspaper%20ro

ll.gif

Lauri Hetemäki & Michael Obersteiner, “US Newsprint Demand Forecasts to 2020,”

supported by Fisher Center for the Strategic Use of Information Technology.

*

272

• Of great importance to

newspaper companies:

- What will be the price of its

largest non-personnel cost item?

• Also of great importance to paper

and forestry companies which

must make investments in new

trees.

*

273

Approaches to Forecast

Newsprint Demand1. The “classical model”:

GDP and newsprint price

determine newsprint

demand.



*

6

274

Current Trends

• The various projections

conclude that the US

newsprint demand will most

likely decline in the next two

decades.



275

U.N. Food and Agriculture

Organization (FAO)

Projections• The Global Forest Products

Model

- explains 98% of historical

variations in newsprint use

- simple to useLauri Hetemäki & Michael Obersteiner, “US Newsprint Demand Forecasts to 2020,”


276

• The FAO model is simple to

use and has a strong theoretical

basis.

++− )(45.0).(02.0 GDPpricenews

Newsprint Consumption=

Newsprint = News.

Consumption = Cons.



)(46.0 ltdemand lag −

277

Trends

• But the elasticity of

newsprint demand in terms of

GDP may has turned negative

after 1987.



http://unadorned.org/morningpaper/images/papers/mp_200

30707_2.jpg

*

7

278

In contrast, the Regional Plan

Association (RPA) Demand

Equation

• RPA is an independent, non-

profit regional planning

organization of the NY, NJ,

and CONN region.

279

RPA Demand Equation

• Derived from a 2 stage Almost

Ideal Demand System (AIDS)

−+ price) (capital price) media (print 0.280.95

capita) per (GDP price) (news. ++− 1.23 0.22−− change) ical(technolog n)(populatio 0.021.0

+− price) (computer price RadioTV 0.06)/(0.07

Newspaper Consumption=



dummy) ncalibratio (demand0.1

280

• “Print media price index” –

calculates the impact of changes

in print material prices, which

affects the printing and

publishing industries, and in

turn newsprint demand



*

281

• The “Price of TVs, radios, and

computers” reflect the potential

substitution impacts of

electronic media.

• Technological change

represents innovations in

products



8

282

• Adjusts for the decrease in

newsprint demand post 2000

and the recession in the US

economy.

Demand Calibration Dummy



283

Figure 1. US Newsprint Consumption Projections: FAO (1995-2010 and

RPA (2001-2020)



*

284

• The RPA model

accounts for the

importance of the

relative prices of

newsprint and

electronic media

as substitutes.

http://www.newspapercatalog.com/newspapers/images/newspapercatalog/ad02bg.gif



285

• FAO projections reflect the

pre-1987 trend:

increasing newsprint demand

• RPA projections reflect the

post-1987 trend:

decreasing demand



9

286

Alternative Econometric

Approach:

Bayesian Model

287

The Bayesian Model

• Incorporates other information

(e.g. subjective expert

knowledge) into econometric

forecasting models.

• This model has gained

popularity.Lauri Hetemäki & Michael Obersteiner, “US Newsprint Demand Forecasts to 2020,”


288

• Expert opinion will often

require one to reset the equation

accordingly.

- If GDP growth is believed not

to impact future demand, the

mead value of GDP distribution

of the previous year should be

reset.Lauri Hetemäki & Michael Obersteiner, “US Newsprint Demand Forecasts to 2020,”


289

• This shift moves from the

classical model, based on the

“prior” p(GDPt|Dt-1) to the

Bayesian post-intervention prior

p(GDPt|Dt-1, It) where It denotes

the external information available

from the experts at time t. It is

called the “prior” information set.Lauri Hetemäki & Michael Obersteiner, “US Newsprint Demand Forecasts to 2020,”


10

290

• Such “prior information by

experts is derived from

simulations produced by

exercises.

• Participants develop

consumption scenarios from

1998 up to year 2013 in 5-year

intervals.Lauri Hetemäki & Michael Obersteiner, “US Newsprint Demand Forecasts to 2020,”


291

Deriving the “Bayesian prior”

• Experts create scenarios of

future demand.

• The experts are asked to

give quantitative responses

to three factors of the US

newsprint market:Lauri Hetemäki & Michael Obersteiner, “US Newsprint Demand Forecasts to 2020,”


292

1. economic and

lifestyle development

2. the trend from

paper media to

electronic media

3. future changes in the

weight and size of

newspapers.

http://istresults.cordis.lu/Pictures/200402/61220_001.jpg

Lauri Hetemäki & Michael Obersteiner, “US Newsprint Demand Forecasts to 2020,” supported by

Fisher Center for the Strategic Use of Information Technology.

293

• After the first scenarios are

created, they are discussed and

improved by the participants.

The process continues until the

experts come to a consensus on

the ideal scenario



11

294

The Classical Approach• Newsprints are defined as forest

products.

• A behavioral hypothesis derives

the demand by an optimization

problem, producing a demand

function:ikik

ik

a

ik

ik

ikikik DXPaDησ σ

1,−=Lauri Hetemäki & Michael Obersteiner, “US Newsprint Demand Forecasts to 2020,”


295

Newspaper Circulation Model

• Rests on logical reasoning:

If more people read

newspapers → newspaper

circulation will increase →

higher demand for newsprint.



296

• Since 1987, there has been a

decline in the volume of

newspaper circulation.



*

297

• The logarithmic form of this

model is:

ttnewstnewstnews dcircd µγγγ ++∆+= − )ln()ln()ln( 1,2,10,

*

12

298

• (dnews,t)= the quantity of

newsprint consumption in the

US

• ∆(circnews,t)= change in the

volume of newspaper

circulation



299

• dnews,t-1= lagged dependent

variable measuring short-run

dynamics in demand

• µt= error term

• t= subscript denoting time

period



300

The Results for

Estimating Newsprint

Demand

301

Figure 4. US Newsprint Consumption, Real GDP, and Real Newsprint

Price, 1971-2000

N



13

302N



303

3. Newspaper Circulation

Model• The absolute value in the changes

in newspaper circulation

parameter show that a 1%

increase in circulation would lead

to a very large (3.1%) increase in

demand for newsprintLauri Hetemäki & Michael Obersteiner, “US Newsprint Demand Forecasts to 2020,”


*

304Lauri Hetemäki & Michael Obersteiner, US Newsprint Demand

Forecasts to 2020, p.30.

*

305

Conclusions

• The Classical forest products

demand model could not

explain the recent structural

change in the US newsprint

consumption.

Lauri Hetemäki & Michael Obersteiner, US Newsprint Demand


14

306Lauri Hetemäki & Michael Obersteiner, US Newsprint Demand Forecasts to 2020, p.31.

307

Conclusions

• Both GDP and newsprint price

proved to be insignificant

determinants of demand.

• Results also point out that a

negative income elasticity is

possible.Lauri Hetemäki & Michael Obersteiner, US Newsprint Demand


308

309

Interpretation of Results– Rule of thumb: t-stats >2 or <-2

a statistically significant

difference (reject null)

– Most software reports t-test

where β0=0. In general “a

parameter is statistically

significant” means that it is

statistically different from 0

15

310

Interpretation of Results

• R-square

– Measure of overall fit of the

model: what percent of the

observed variation in the

dependent variable explained

by independent variables

(rather than error term)

311

Problems of Econometric Demand Estimation

• Data

–Often insufficient

–Often unreliable

*

312

• Need to assume a specific

mathematical model for the

relationship between price and sale.

• If specification is incorrect, the

results will be incorrect

• Predicting the future requires

assumption that behavior is like the

past.Thomas T. Nagle & Reed K. Holden, “The Strategy and Tactics of Pricing: A Guide to Profitable


*

313

• Model

–is the model that is estimated properly defined and coherent?

–Is it stable?


16

314

Other Types Of Models For

Econometric Demand

Estimation• Inverse• Stone-Geary• Quadratic• Stochastic• Discrete• Dynamic• Inter-temporal

•Engel•Log-linear•Semi-log•Constant elasticity•2 stage least

square•Etc., etc.

315

• Econometric problems–Serial correlation–Multicollinearity–Homoscedasticity–lags–exogeneity


*

316


• Results–statistically significant?–conclusion justified?–Can one claim causality–stable over time, for forecasting?

*

appendix d: econometric estimation · supported by fisher center for the strategic use of...

Documents