Quantitative Demand Analysis Flashcards

1
Q

What is the form of a linear demand function and what do the signs determine?

A
  • Qd = α0 + αxPx + αyPy + αMM + αHPH
  • Sign before Py determines subistutes/complelemtns
  • Sign before M determines normal/inferior
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2
Q

In general, what is the formal for elasticity?

A

(∆G/G)/(∆S/S) = ∆G/∆S . S/G

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3
Q

What is the point elasticity of OPE?

A

Point elasticity: ∆Q/∆P . P/Q

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4
Q

What is the total revenue test?

A
  • When demand is elastic, a price increase leads to a decrease in total revenue
  • When demand is inelastic, a price increase leads to an increase in total revenue
  • When demand is unitary elastic, total revenue is maximised
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5
Q

How can total revenue be maximised using the total revenue test?

A

To max profit use OPE of demand = αx(Px/Qxd) and solve for P

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6
Q

How does MR relate to elasticity?

A
  • MR = P(1+E/E)
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7
Q

What factors affect OPE?

A
  • Availability of consumption substitutes
  • Time/Duration of purchase horizon
    • LR more elastic than SR
  • Expenditure share of consumers budgets
    • Goods that comprise a small percentage of the consumer’s total expenditure will be elastic
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8
Q

What is cross price elasticity?

A

%∆Qxd / %∆PY

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9
Q

What does cross price elasticity tell us?

A
  • EQXd, PY > 0: substitutes

- EQXd, PY

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10
Q

What is income elasticity?

A

%∆Qxd / %∆M

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11
Q

What does income elasticity tell us?

A
  • EQXd, M > 0: normal goods

- EQXd, M

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12
Q

What is the shortcut for OPE for a linear demand function?

A

αx(Px/Qxd)

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13
Q

What is the shortcut for CPE for a linear demand function?

A

αy(Py/Qxd)

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14
Q

What is the shortcut for Income Elasticity for a linear demand function?

A

αx(m/Qxd)

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15
Q

What are the components of a regression model function?

A
  • Y = a +bX + e
  • a: unknown intercept parameter
  • b: unknown slope paramater
  • e: random error term with mean zero and st.d σ
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16
Q

What are the components of a least squares regression line?

A
  • Y = â +b̂X + e
  • â: least squares estimate of the unknown parameter a
  • b̂: least squares estimate of the unknown parameter b
  • Both represent the values of a and b that result in the smallest sum of squared errors between a line and the actual data
17
Q

How is statistical significance evaluated?

A
  • Standard Error

* t-stat

18
Q

What is standard error?

A
  • How well the fitted equation fits the sample data

- σ̂ = √[SSR/n-2]

19
Q

What is t-stat?

A

When |t| > 2, we are 95% confident the true parameter is in the regression is not zero (â and b̂)

20
Q

How is overal regression line fit evaluated?

A
  • R-Square

* Adjusted R-Square

21
Q

What is R-Square?

A
  • Coefficient of determination
  • Fraciton of the total variation in the dependent variable that is explained by the regression
  • R2 = explained variation/total variation = 1 - (SSR/(yi - yavg)
  • Ranges between 0 and 1 - values closer to 1 indicate better fit