ECON 6012 Cost Benefit Analysis Memorial University of Newfoundland

Slides:



Advertisements
Similar presentations
Multiple Regression and Model Building
Advertisements

The Multiple Regression Model.
Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and l Chapter 12 l Multiple Regression: Predicting One Factor from Several Others.
4.3 Confidence Intervals -Using our CLM assumptions, we can construct CONFIDENCE INTERVALS or CONFIDENCE INTERVAL ESTIMATES of the form: -Given a significance.
Objectives (BPS chapter 24)
8. Heteroskedasticity We have already seen that homoskedasticity exists when the error term’s variance, conditional on all x variables, is constant: Homoskedasticity.
The Use and Interpretation of the Constant Term
Choosing a Functional Form
Chapter 13 Multiple Regression
Linear Regression.
Multiple Linear Regression Model
Chapter 10 Simple Regression.
Chapter 12 Simple Regression
Chapter 12 Multiple Regression
Chapter 5 Heteroskedasticity. What is in this Chapter? How do we detect this problem What are the consequences of this problem? What are the solutions?
CHAPTER 12 VALUING IMPACTS FROM OBSERVED BEHAVIOR: DIRECT ESTIMATION OF DEMAND CURVES.
Chapter 4 Multiple Regression.
Chapter 9 Simultaneous Equations Models. What is in this Chapter? In Chapter 4 we mentioned that one of the assumptions in the basic regression model.
Econ 140 Lecture 181 Multiple Regression Applications III Lecture 18.
CHAPTER 4 ECONOMETRICS x x x x x Multiple Regression = more than one explanatory variable Independent variables are X 2 and X 3. Y i = B 1 + B 2 X 2i +
Learning Objectives Define and measure elasticity
SIMPLE LINEAR REGRESSION
AGEC 608 Lecture 12, p. 1 AGEC 608: Lecture 12 Objective: Illustrate how to value project impacts via direct estimation of demand curves Readings: –Boardman,
Chapter 11 Multiple Regression.
Topic 3: Regression.
THE IDENTIFICATION PROBLEM
SIMPLE LINEAR REGRESSION
C HAPTER 13 Valuing Impacts from Observed Behavior: Direct Estimation of Demand Curves.
Demand Estimation & Forecasting
Christopher Dougherty EC220 - Introduction to econometrics (chapter 3) Slideshow: prediction Original citation: Dougherty, C. (2012) EC220 - Introduction.
Introduction to Regression Analysis, Chapter 13,
Simple Linear Regression Analysis
Multiple Linear Regression A method for analyzing the effects of several predictor variables concurrently. - Simultaneously - Stepwise Minimizing the squared.
1 PREDICTION In the previous sequence, we saw how to predict the price of a good or asset given the composition of its characteristics. In this sequence,
Ordinary Least Squares
Chapter 5 Valuing Benefits and Costs in Secondary Markets
Chapter 15 Forecasting Copyright © 2011 Pearson Addison-Wesley. All rights reserved. Slides by Niels-Hugo Blunch Washington and Lee University.
SIMPLE LINEAR REGRESSION
Mathematics for Economics and Business Jean Soper chapter two Equations in Economics 1.
Introduction to Linear Regression and Correlation Analysis
Inference for regression - Simple linear regression
Chapter 11 Simple Regression
3 CHAPTER Cost Behavior 3-1.
Hypothesis Testing in Linear Regression Analysis
ECON 6012 Cost Benefit Analysis Memorial University of Newfoundland
Quantitative Methods Heteroskedasticity.
6.1 Chapter 7 – The Theory of Consumer Behavior  The Theory of Consumer behavior provides the theoretical basis for buyer decision- making and the foundation.
Statistics for Business and Economics 7 th Edition Chapter 11 Simple Regression Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Ch.
Chapter 10 Hetero- skedasticity Copyright © 2011 Pearson Addison-Wesley. All rights reserved. Slides by Niels-Hugo Blunch Washington and Lee University.
Correlation and Linear Regression. Evaluating Relations Between Interval Level Variables Up to now you have learned to evaluate differences between the.
Random Regressors and Moment Based Estimation Prepared by Vera Tabakova, East Carolina University.
Various topics Petter Mostad Overview Epidemiology Study types / data types Econometrics Time series data More about sampling –Estimation.
Managerial Economics Demand Estimation & Forecasting.
Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.
Chapter 4 Linear Regression 1. Introduction Managerial decisions are often based on the relationship between two or more variables. For example, after.
2.4 Units of Measurement and Functional Form -Two important econometric issues are: 1) Changing measurement -When does scaling variables have an effect.
Issues in Estimation Data Generating Process:
Copyright © 2005 by the McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Managerial Economics Thomas Maurice eighth edition Chapter 7.
Chapter 8: Simple Linear Regression Yang Zhenlin.
I271B QUANTITATIVE METHODS Regression and Diagnostics.
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 14-1 Chapter 14 Multiple Regression Model Building Statistics for Managers.
10-1 MGMG 522 : Session #10 Simultaneous Equations (Ch. 14 & the Appendix 14.6)
All Rights Reserved to Kardan University 2014 Kardan University Kardan.edu.af.
Quantitative Methods. Bivariate Regression (OLS) We’ll start with OLS regression. Stands for  Ordinary Least Squares Regression. Relatively basic multivariate.
Heteroscedasticity Chapter 8
Chapter 11 Simple Regression
9. Valuing Impacts from Observed Behavior: Direct
Undergraduated Econometrics
Heteroskedasticity.
Presentation transcript:

ECON 6012 Cost Benefit Analysis Memorial University of Newfoundland Lesson 8 (Chapter 13) VALUING IMPACTS FROM OBSERVED BEHAVIOR: DIRECT ESTIMATION OF DEMAND CURVES

Purpose The key concept for valuing policy impacts is change in social surplus Changes in social surplus are represented by areas bounded by supply and demand curves Measuring these changes is relatively easy when we know the shape and positions of the supply and demand curves in the relevant primary market, before and after the policy change In practice, however, these curves are usually not known, so we have to estimate them or find alternative ways to measure benefits and costs Now we will discuss direct estimation of these curves, focusing on estimating demand curves

PROJECT REVENUES AS THE MEASURE OF (GROSS) BENEFITS Revenues are a natural measure of benefits to firms in the private sector. But in CBA, as we already discussed, revenues do not always equal (gross) consumer benefits. Revenues may be used to measure benefits when there are no consumers with standing (and hence no CS) such as when all output is exported. They can also be used when the government sells goods in an undistorted market without affecting the market price – Unfortunately this usually is not the case in projects evaluated by CBA.

ESTIMATION KNOWING ONE POINT ON THE DEMAND CURVE AND ITS SLOPE OR ELASTICITY Suppose we know only one point on the demand curve, but previous research provides an estimate of either the elasticity or slope of the demand curve. We first suppose the demand curve is linear and then that it has constant elasticity instead. Linear Demand Curve A linear demand curve assumes that the relationship between the quantity demanded and the price is linear; that is: q = α0 + α1p (12.1) where, q is the quantity demanded at price p, α0 is the quantity that would be demanded if price were zero (the intercept), and α1 indicates the change in the quantity demanded as a result of a one unit increase in price (the slope). If you know one point on the demand curve and its slope , then you can compute other points on the curve

ESTIMATION KNOWING ONE POINT ON THE DEMAND CURVE AND ITS SLOPE OR ELASTICITY If the demand curve is linear and we have an estimate of its elasticity then we also need to know the price and quantity at which the elasticity was calculated. The price elasticity of demand, εd, measures the responsiveness of the quantity demanded to changes in price -- the more it responds, the higher the elasticity.

ESTIMATION KNOWING ONE POINT ON THE DEMAND CURVE AND ITS SLOPE OR ELASTICITY For a linear demand curve, equation (12.1), the price elasticity of demand equals: εd = α1 times p/q (12.3) Thus, the elasticity is non-constant -- it varies with both price and quantity. If we know the elasticity and p and q, we can use equation (12.3) to estimate the slope of the demand curve and then it is straightforward to compute other points on the demand curve, as before.

Constant Elasticity Demand Curve Economists have found that many goods have a constant elasticity demand curve, that is, (12.4) where, q denotes quantity demanded and p is price, as before, and β0 and β1 are parameters. In order to interpret β1 it is useful to take the natural logarithm, denoted by ln, of both sides of equation (12.4), which gives: lnq = lnβ0 + β1lnp (12.5)

Constant Elasticity Demand Curve We see now that the constant elasticity demand curve is linear in logarithms  Furthermore, β1, the slope of this linear in logarithms demand curve, equals εd, the price elasticity of demand. As εd equals the slope of a linear curve, which is a constant, it follows that the price elasticity of demand is constant; hence the name of this demand curve. Again, given one point on the constant elasticity demand curve and an estimate of its elasticity, the whole curve can be plotted straightforwardly. (12.5)

Constant Elasticity Demand Curve Useful to note that the area under a constant elasticity demand curve from quantity q0 to quantity q1 is given exactly by: where ρ = [1 + (1/β1)].

Constant Elasticity Demand Curve Slope and elasticity estimates of demand curves can often be obtained from prior research When this happens, you need to consider possible internal and external validity problems (i.e., how valid is the estimate [internal – how was it measured and computed] and can it be used in this instance [external – how similar is the case in question to the research case]). Otherwise, you might be able to DIY with some primary or secondary information you obtain through observation of the relevant markets

EXTRAPOLATING FROM A FEW POINTS If we know a few points on the demand curve, we can use them to (geometrically) predict another point of relevance to policy evaluation. There are two important considerations when extrapolating: Different functional forms lead to different answers. Furthermore, the further we extrapolate from past experience, the more sensitive the predictions are to assumptions about the functional form. The validity of attributing an outcome change to the policy change (i.e. other variables are assumed to remain constant) may be questionable. More observations provide greater validity.

ECONOMETRIC ESTIMATION WITH MANY OBSERVATIONS If we have instead many different observations of prices and quantities, we can apply econometric methods to estimate the entire demand curve. Model specification The econometric model should include all explanatory (so-called independent) theoretically-relevant variables, even if one is not specifically interested in their effect Excluding a theoretically important variable is one form of specification error. Using the incorrect functional form is another form of specification error.

ECONOMETRIC ESTIMATION WITH MANY OBSERVATIONS Types of data Sometimes you can generate your own data but, more often, limited resources require one to use data available at lower costs (previously published data, data originally collected for other purposes, and/or sampling administrative records or clients).

ECONOMETRIC ESTIMATION WITH MANY OBSERVATIONS Considerations in the choice of data are: Level of aggregation, whether individual or group. Individual level data are preferred because most theory is based on individual utility maximization. Also, aggregate data can lead to less precise estimates.

ECONOMETRIC ESTIMATION WITH MANY OBSERVATIONS Considerations in the choice of data are: Choice of cross-sectional, time series or panel data Cross-sectional data generally provides estimates of long-run elasticities, while time series data usually provides estimates of short-run elasticities Short-run elasticities are generally smaller than long-run elasticities (because there is less time to adjust to new prices) Cross-section data faces the possible problem of heteroskedasticity, which means the error terms have different variances

ECONOMETRIC ESTIMATION WITH MANY OBSERVATIONS Considerations in the choice of data are: Time-series data may have problems of autocorrelation, which arises if the error terms are correlated over time. Both problems can be tested for and corrected using generalized least squares (GLS) instead of OLS. It is possible to have pooled cross-sectional and time-series data. This provides a rich source of information but requires more complicated econometric methods.

ECONOMETRIC ESTIMATION WITH MANY OBSERVATIONS Identification In a perfectly competitive market, price and quantity result from the simultaneous interaction of supply and demand Changes in price and quantity can result from shifts of the supply curve, shifts of the demand curve, or both In the absence of variables that affect only one side of the market (demand or supply, but not both), it may not be possible to estimate separate supply and demand curves Indeed, if quantity supplied and quantity demanded depended only on price, then the equation for both the demand curve and the supply curve would look identical!

ECONOMETRIC ESTIMATION WITH MANY OBSERVATIONS Identification How can we identify which is the demand curve and which is the supply curve? This is one example of the problem of identification. It occurs in multiple equation models in which some variables, such as price and quantity, are determined simultaneously. Such variables are called endogenous variables. In contrast, variables that are fixed or determined outside of the model are called exogenous variables.

ECONOMETRIC ESTIMATION WITH MANY OBSERVATIONS Identification To identify the demand curve, you need a variable that affects supply but not demand. This variable systematically shifts supply but not demand, thereby tracing out the demand curve. To identify the supply curve you require a variable that affects demand but not supply. The identification problem does not arise when the government supplies a good or sets the price (there is no supply curve – price is exogenous). Identification is only a problem if price is endogenous.

ECONOMETRIC ESTIMATION WITH MANY OBSERVATIONS Confidence Intervals The standard errors of the estimated coefficients can be used to construct confidence intervals. Confidence intervals provide some guidance for sensitivity analysis. Prediction versus Hypothesis Testing In cost-benefit analysis we often have to make a prediction. Thus, we are interested in all of the estimated coefficients, whether or not they are statistically different from zero.

NEXT VALUING IMPACTS FROM OBSERVED BEHAVIOR: INDIRECT MARKET METHODS READ CHAPTER 13