Ec2610 (IO) Problem Set I Differentiated Product Demand Suggested solutions
|
|
- Shona Chambers
- 7 years ago
- Views:
Transcription
1 Ec2610 (IO) Problem Set I Differentiated Product Demand Suggested solutions Ariel Pakes, TF Ashvin Gandhi * Due: October 14, 2015 October 14, 2015 Preliminaries Cut from solutions. 1 Background on demand estimation Cut from solutions. 2 Estimation exercise 2.1 Setting Cut from solutions. 2.2 Data description For the empirical exercise, we are giving you data on T = 10 markets. In these markets, 11 different firms sell a total of J = 247 products. All of the products are unique, so none of them are offered in * agandhi@fas.harvard.edu. These solutions are attributable to Daniel Pollmann. 1
2 multiple markets. The dataset is simulated, but you can still think of a product as a passenger vehicle with a set of characteristics if you like, although the units do not have an interpretation. The dataset contains the following pieces of data, where products are ordered by market (1-10): ˆ prodsmarket : T -vector of the number of products in each market ˆ share : J-vector of market shares ˆ f : J-vector denoting the firm that sells the product ˆ ch : J 4-matrix of constant and three product characteristics ˆ pr : J-vector of prices ˆ costshifters : J 2-matrix of cost shifters 2.3 Basic summary statistics 1. Prepare a table with the following pieces of information for each market: How many firms are active? How many products do they market in total? What fraction of agents bought one of the goods in the sample period? This question just tries to get you acquainted with the market structure in the data. The last question has some significance because it relates to the definition of the outside good, which is not innocuous. Some of you asked about the definition of the outside good. That s a good point, since it is true that this may involve a decision on the part of the researcher. First, note that whether you care about this depends a bit on what you re trying to model/estimate. For the demand side parameters without supply side info, this doesn t matter that much, since it just affects the value of the constant term in the utility (verify this). One way of thinking about the potential market is how many people would purchase a product if the vector utilities went to infinity (then the 1 in the share denominator gets dominated). This could happen if the prices of all products went to negative infinity. That seems like a sweet deal for consumers, and we d think they d all want one or more units. So you see the trouble here. Sometimes, it s okay to assume that everyone is potentially in the market; the potential market for newspapers could sensibly be the number of households, at least in a local area. In certain cases, survey data tells us who considered buying a product in a given year, so Ec Problem Set I
3 that s something (though more people might have considered with prices at negative infinity). In the new cars setting in BLP, part of the trouble stems from the fact that we re looking at sales of a durable good, so consumers may really like a product, they just don t buy every year (notice how this is different from modeling, e.g., market shares of cable TV providers, or cereal). If we don t want to set up a full-blown model of dynamic demand, we need to make some assumption on how many people were in the market for a new car in a given year. Since we re not actually interested in market shares at prices equal to negative infinity, we want to choose a value that makes sense when considering local price variation. 2. Prepare a table with summary statistics for market share, characteristics, price, and cost shifters. Please include mean, median, minimum, maximum, and standard deviation. You can inspect these statistics separately for each market, but in what you report, you may pool all markets. This is always a useful thing to do when working with a new dataset. In part to get a sense for the variables you have at your disposal, in part to make sure there aren t any obvious errors in your data (never trust your TF). Some of you said that it was difficult to think about which orders of magnitude made sense for the coefficients in the discrete choice model. One first guide is obviously the orders of magnitude of the characteristics. Almost more importantly though, we want to think about the variation in each variable, because this variation, scaled by its coefficient, determines the variation in utility across the different choices arising from that particular characteristic (admittedly, it is difficult to have a prior for this when nobody has told you what the characteristics are). Given the quasilinear specification, I suppose the utility we model is even not purely ordinal. In the pure logit model, an increase in mean utility by one means that, given a large number of products in the market, the share will multiply by e , so if your characteristic is horsepower, a coefficient of.01 probably makes more sense than a coefficient of 1. For the purposes of the statistical model, the more important normalization arises from the fact that we have set the scale parameter of the Type-I EV distributed errors to one, resulting in logit errors. Similarly, a probit model typically assumes standard normal errors. It is important to be aware that in discrete choice, it is not just mean utility that matters for probabilities and shares, but also the variance, and the covariance with the utility of other products. Modeling the latter flexibly is one of the defining features of BLP. Ec Problem Set I
4 2.4 Pure logit model 1. Suppose agents have the following utility function, where i denotes the agent, and j denotes the product: u ij = δ j }{{} x j β αp j+ξ j +ɛ ij, where ɛ ij is an iid error following a standard Type-I Extreme Value distribution with F (ɛ) = e e ɛ ( logit errors). Suppose further that the firms know ξ when setting prices, but that they cannot use this information to adjust characteristics in the short run. (a) What statistical assumptions can you make based on this? Which of your conditions, based on data provided to you, identify the parameter vector of interest, θ = (α, β)? In other words, what are valid (and relevant) instruments? Is the model over-identified? The usual assumption is that characteristics and cost shifts are orthogonal to the unobserved product characteristic, but price is not. In this case, even with just the cost shifters as additional instruments, the model is over-identified. If we strengthen orthogonality to mean independence, we can use any function of these instruments as instruments, though they would only be helpful for identification if they brought in additional variation. (b) Show how you can invert market shares to obtain the mean utility level δ j for each product. See the BLP slides. (c) Estimate θ = (α, β) and provide standard errors for your estimate. You can try different combinations of instruments, but please use all the different types of instruments that are included or can be constructed from the data (i.e., BLP instruments ). See the GMM slides for estimation and inference. 2. Estimate and present the matrix of cross- and own-price elasticities for market 10 based on your model and parameter estimates. 1 1 The symmetry of the matrix of share derivatives with respect to price (though not of elasticities) may have reminded you of the symmetry of the Slutsky matrix of the derivative of uncompensated demand with respect to price (Mas-Colell, Whinston and Green, 1995, Proposition 3.G.2). Anderson, de Palma and Thisse (1992, p. 67) (available on Hollis) show that it also holds in discrete choice settings with constant marginal utility of income in the region of interest, e.g., with quasilinear utility. Goolsbee and Petrin (2004) take advantage of symmetry to identify a cross-price derivative which would not be identified otherwise due to lack of variation in one of the prices. Ec Problem Set I
5 3. In the next question, we are going to free up the substitution pattern by introducing random coefficients as in BLP. Alternatively, we could think about implementing nested logit, the pure characteristics model, or multinomial probit. Would they be appealing in this setting? Why or why not? See the BLP slides and lecture notes. 4. Bonus question: Explain how Hausman instruments work, and under which assumptions they are valid. Can you form them in this case? How or why not? Hausman instruments (MIT s Jerry Hausman of Hausman test fame) are prices of the same product in other markets (Hausman, 1996) provided these are uncorrelated with the demand shock in the market in question; hard to do here because we do not see the same product in different markets in our data. 2.5 Random-coefficient logit model 1. Suppose agents have the following utility function, where i denotes the agent, and j denotes the product: u ij = δ }{{} j + x j β αp j+ξ j k {1,2} σ k ν i,k x j,k σ p ν i,p p j + ɛ ij, where ɛ ij is an iid error following a standard Type-I Extreme Value distribution, and ν i, iid N (0, 1) is an iid standard normal error. To summarize: the model is as before, but with random coefficients on the constant, the first characteristic, and price. remain the same as before. The orthogonality/exogeneity assumptions (a) What is the contraction mapping used here for the inner loop? Is there a way to reduce the computational burden from the contraction mapping? (Hint: take a look at page 4 of the appendix to Nevo (2000).) In the following, make sure to set the inner tolerance level for the contraction mapping very tight, in your final run ideally on the order of See Nevo s appendix. In practice, the exponentiated version of the contraction mapping tends to be used. (b) Write the parameter vector of interest as θ = (θ 1, θ 2 ), where θ 1 are the linear parameters, and θ 2 are the nonlinear parameters. Which parameters are in θ 1 and which are in θ 2? Ec Problem Set I
6 What does this imply for estimation? The linear parameters are α and β. The nonlinear parameters are σ. (c) Bonus question: Explain how the variance terms σ are identified from variation in the choice set and prices. The ideal experiment would be to randomly remove products from certain markets and observe which fraction of consumers reallocates to the different alternatives. If they chose a product particularly close in one characteristic, but not in others, then we would think that the variance in the random coefficient of that product should be particularly large. Removing a product from the choice set is the same as sending its price to infinity, so we would think that reallocation of market shares based on variation in prices is also useful, although in both cases, we should also ask ourselves why a product is missing from the choice set or its price is higher (the two would probably occur under opposite circumstances). The recent papers I refer to in the preliminary section provide a much more formal treatment. (d) Estimate the model using 2-step optimal GMM. In addition to your point estimates, please provide standard errors. (Hint: take a look at page 6 of the appendix to Nevo (2000) for analytic standard errors, and/or use finite differences for a numerical approximation.) If you try different starting values, do your estimates change? You may have found some sensitivity with respect to starting values. Search algorithms are only guaranteed to find a local minimum. In practice, it is useful to check whether that is the case by trying out different starting values. Provided you can find all the local minima, the minimum of that set is the global minimum, so try to compare the objective function values. You should also look at and interpret the reason that the search algorithm terminated, and you might want to set its termination rules to stricter levels: continue with smaller changes in the objective function value or the value of the argument and allow more function evaluations. Please have a look at my code to see how I use the bootstrap to adjust my standard errors for the simulation error. 2. Compare the cross- and own-price elasticities for market 10 for the RC logit and pure logit model. We should see more substitution to products that are close in the characteristics space. 3. We are assuming here that demand in all markets is identical. With data on the distribution of Ec Problem Set I
7 income within each market, how could you let the distribution of α i vary systematically across markets? BLP parametrizes price sensitivity as a function of income. To simulate income, they draw from a lognormal distribution. If the income distribution varied by market, this would be useful to incorporate to have a better model for price sensitivity. 4. Let s assume, only for this question, that you had micro moments as in Berry, Levinsohn and Pakes (2004): the covariance of consumer attributes and product characteristics as well as the covariance between first- and second-choice characteristics. How could you integrate them into your estimation procedure to improve the precision of your estimates? Which of the coefficients would each of the different sets of moments be particularly useful in pinning down? Your model should predict covariances similar to those in your sample, so you could add these moments to your GMM procedure. The covariance of consumer attributes and product characteristics helps with the coefficients on observed household heterogeneity (the coefficients β o on the interaction terms of household attributes and product characteristics), while the covariance between firstand second-choice characteristics helps with the coefficients on the unobserved heterogeneity (the standard deviations of the random coefficients based on typically normal draws). 5. Suppose in addition that each product has the following marginal cost structure: mc j = [ x j cs j ] γ + ωj, where cs is the J 2 matrix of cost shifters. (a) Explain how you can estimate γ based on your estimates for the demand side and different assumptions on the supply side (product-level profit maximization, firm-level profit maximization, collusion to maximize total profit). How could you obtain valid standard errors? One way to estimate the supply side parameters is to solve for the marginal cost vector implied by the demand side estimates and run a simple regression on the characteristics. You see this done in some papers, when for some reason joint estimation was too burdensome/difficult or somehow not possible. The disadvantage of this two-step estimator is a loss of efficiency relative to the joint GMM estimator. Ec Problem Set I
8 Your usual standard errors from the above regression would not be valid. Instead, you would need to account for the fact that the marginal cost plugged into the regression is an estimate of the true marginal cost mc j stated above. The true cost includes ω j that is not the issue but there is an additional error that stems from the fact that the demand-side parameter plugged in to solve for marginal cost are estimated themselves. So, since marginal cost is a function of the estimates for the demand-side parameters, which are random, it is random itself. Formally, since the derivative of the moment condition used to estimate the supplyside parameters with respect to the parameters that require plug-in estimates is different from zero. 2 If you want to or have to estimate demand-side parameters and supply-side parameters sequentially, you can do so analytically or using a bootstrap procedure (notes). With joint GMM, you do not have this issue and your standard errors are obtained the usual way. (b) Can you also estimate the demand side and supply side jointly? How would you do so? What are the linear and nonlinear parameters now? Can joint estimation improve the precision of your estimates for the demand side parameters? Please explain how. Is there a caveat? The supply side provides you with additional moment conditions (ω interacted with instruments), which you could add to your GMM procedure. The supply side parameters are linear parameters, but α is now a nonlinear parameter because it appears nonlinearly in the marginal cost equation. This implies that α becomes one of the parameters which we search over in the outer loop. Hayashi ( Econometrics, p. 272) discusses conditions under which single-equation and multipleequation estimates will be numerically or asymptotically equivalent. Clearly, if both equations are just-identified, the results will be numerically identical, but if at least one of them is overidentified, the efficient multiple-equation GMM estimator will generally be more efficient. The exception is if the off-diagonal block matrices are zero; in our case, under conditional homoskedasticity, this would reduce to ξ and ω being uncorrelated. While we did not generate them with any correlation, it is conceivable that the unobserved characteristic ξ valued by consumers actually has a cost of producing that appears in ω. There are several questions which I wasn t able to formally answer in spite of some serious attempts at rewriting the variances matrices applying different results on partitioned matrices 2 The case where this derivative is zero is often referred to as the adaptive case, while the non-zero case is referred to as non-adaptive. However, the term adaptiveness also has a different meaning in the statistics literature on semiparametric efficiency (e.g. van der Vaart, 1998, p. 223). I digress. Ec Problem Set I
9 and matrix equalities: if we add a just-identified system (exhibiting positive cross-correlation with the original system), will the variance of the parameters in the original over-identified system decrease? Does it depend on whether any of the parameters of the original system appear in the new system? I would like to know this because I am adding a just-identified supply side (just-identified in terms of additional parameters) to my over-identified demand side, and I would like to know whether the variance of my estimates improves. I did a simplified numerical experiment in Matlab, since I couldn t prove things formally (yet), and it turns out that for many random draws of covariance and gradient matrices, the demand-side parameter variance never changed if we added a supply side, only that the supply-side parameters had a lower variance than otherwise because of joint estimation (the latter assuming no common parameters). The demand-side variance did not decrease even when I put demand-side parameters in the supplyside equation as is true in BLP. This is in line with some notes I have seen, but which gave no proof. The above is no proof either, but I find it fairly convincing because I randomly generated many systems and tried different configurations. Try it yourself; the code is in the appendix. This implies that to get any asymptotic efficiency gains from using the supply side, we better make sure it is over-identified. BLP uses the whole set of instruments on the supply side, but it is not clear whether the additional moment conditions are actually over-identified, since the additional instruments need to actually be relevant for the supply-side regressors. The other caveat is that additional moments only help when they are valid. Otherwise, they will bias the parameter estimates. In this example, if the supply side is incorrectly specified it requires us to make an assumption on the market structure this contamination will also feed into the estimation of the demand side parameters. The next issue is that most of the above is a large-sample analysis, i.e., as the number of observations tends to infinity. In finite sample, we may well encounter issues related to weak or many instruments problems, in which we are essentially overfitting the first stage of the IV problem and thereby allowing too much noise to pass through nonlinearly, leading to bias. (c) Estimate the demand side and supply side parameters jointly under the assumption that firms maximize total firm profits. You don t need to provide standard errors. (Hint: since we have two sets of moment conditions, we can use multiple-equation GMM for the linear Ec Problem Set I
10 parameters. 3 ) (d) Bonus question: suppose you had to select among the different pricing assumptions; how could you do so? Hint: have a look at Nevo (2001). Nevo compares margins from the model to accounting margins and concludes that multiproduct Bertrand-Nash cannot be rejected. Alternative tests are discussed in Nevo (2001, pp ). One issue with these statistical tests, including tests of overidentifying restrictions, but generally any test of narrow or point hypotheses, is that any model will be misspecified to some extent, since it is just a model. Failure to reject may then simply imply that we do not have enough observations. 2.6 Counterfactual (bonus exercise) Firms 1 and 10 have announced their intention to merge. 4 As the regulator in market 9, you are concerned that this will lead to an increase in prices and a decrease in product variety. Are these concerns justified? Please explain carefully and show your estimates. What concerns do you have about this counterfactual? For this question, you need to recompute the pricing equilibrium given your estimates of the demand side and the cost structure, but with a matrix in the FOC that takes into account the change in the ownership structure. One issue is what to do with the vector ξ; since we think of it not as noise, but a characteristic that households observe (just the econometrician doesn t), it probably makes sense to use the residuals ˆξ. In order to recompute the pricing equilibrium, you could either solve the system of nonlinear equations given by the set of FOCs or use fixed-point iteration. To speak to the two concerns of the regulator, you should compare prices before and after in some disciplined way. The question about product variety essentially asks whether any firm would decide to remove their product from the market. One way to check this would have been to go through all the possible combinations of sets of products which can be offered and compare implied profits. However, removing a product from the market is the same as setting its price to, so if the latter is not a pricing equilibrium, no firm would want to remove a product from the market. Indeed, it seems that with logit demand and in the absence of fixed costs, no product will ever be removed. This is clearly an important 3 See, e.g., Eric Zivot s notes on multiple-equation linear GMM notice that he flips the notation for X and Z relative to their typical use. Hayashi s unmistakably titled textbook Econometrics (2004) devotes its entire chapter 4 to the topic, and Zivot s notes appear to be based on it. 4 Mergers will be discussed in lecture later on in the course. Ec Problem Set I
11 modelling concern, since we do not want our model to rule out situations we deem important in reality. The impact of entry and exit on prices of incumbents is actually ambiguous with heterogeneous consumers. Suppose a low-price product exits the market. An incumbent product loses a competitor, so should it increase its price? Not necessarily, because it may now sell to a greater number of more price sensitive consumers. BLP-type models are thus also much richer in terms of the competitive responses they can rationalize, though in your specific application, you would still want to verify how restrictive your model is. 3 Feedback Thanks to those of who you provided feedback, it s much appreciated. References Anderson, Simon P., André de Palma and Jacques-Francois Thisse product differentiation. Cambridge, Mass.: MIT Press. Discrete choice theory of Berry, Steven, James Levinsohn and Ariel Pakes Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market. Journal of Political Economy 112(1): URL: Goolsbee, Austan and Amil Petrin The Consumer Gains from Direct Broadcast Satellites and the Competition with Cable TV. Econometrica 72(2): URL: Hausman, Jerry A Valuation of New Goods under Perfect and Imperfect Competition. In The Economics of New Goods. NBER Chapters National Bureau of Economic Research, Inc pp URL: Mas-Colell, Andreu, Michael D. Whinston and Jerry R. Green Microeconomic Theory. Oxford University Press. Nevo, Aviv A Practitioner s Guide to Estimation of Random-Coefficients Logit Models of Ec Problem Set I
12 Demand. Journal of Economics & Management Strategy 9(4): URL: Nevo, Aviv Measuring Market Power in the Ready-to-Eat Cereal Industry. Econometrica 69(2): URL: van der Vaart, A. W Asymptotic Statistics. Cambridge University Press. A GMM variance experiment A.1 Introduction Recall that given the optimal choice of weighting matrix, ) d n (ˆθ θ0 N 0, ( Γ 1 Γ ) 1. }{{} V Now we divide the set of moment conditions. In BLP, the first set could be the set of demand-side moment conditions, while the second set could be the set of supply-side moment conditions. We also divide the set of parameters into two sets. In BLP, we would put (α, β, σ) into the first set and γ into the second set. Accordingly, we write Γ = = Γ 11 Γ 12 Γ 21 Γ For example, each column of Γ 12 is the derivative of the first set of moment conditions with respect to an element of the second parameter set. Suppose in the following that Γ 12 = 0, while Γ 21 need not. In BLP, this makes sense because the demand-side parameters appear in the supply-side equation (common parameters), but not vice versa. Clearly, 21 = ( 12 ), since is a covariance matrix and as such symmetric. In the BLP example, under conditional homoskedasticity, ξ and ω need to be correlated for Ec Problem Set I
13 We are particularly interested in the case where the second set of moment conditions is just-identified (in the additional parameters). A.2 Code 1 %% original overidentified system 2 3 % one parameter, two equations 4 Gam1 = [1; 5 -.7]; 6 7 Del1 = [1, -.3; 8 -.3, 2]; 9 10 % optimal GMM variance 11 inv(gam1'*inv(del1)*gam1) %% add just identified system % new variance 16 a = rand(2); 17 b = triu(a) + triu(a,1)'; 18 c = rand(2); 19 d = triu(c) + triu(c,1)'; Del2 = [Del1, b; 22 b', d]; % no common parameters 25 Gam2 = blkdiag(gam1,[.9, -.3; ]); % optimal GMM variance 28 % result: variance of original parameter constant 29 inv(gam2'*inv(del2)*gam2) % common parameters Ec Problem Set I
14 32 Gam2(3,1) = rand(1); % optimal GMM variance 35 % result: variance of original parameter constant 36 inv(gam2'*inv(del2)*gam2) Ec Problem Set I
Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model
Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model 1 September 004 A. Introduction and assumptions The classical normal linear regression model can be written
More informationWhat s New in Econometrics? Lecture 8 Cluster and Stratified Sampling
What s New in Econometrics? Lecture 8 Cluster and Stratified Sampling Jeff Wooldridge NBER Summer Institute, 2007 1. The Linear Model with Cluster Effects 2. Estimation with a Small Number of Groups and
More information1 Teaching notes on GMM 1.
Bent E. Sørensen January 23, 2007 1 Teaching notes on GMM 1. Generalized Method of Moment (GMM) estimation is one of two developments in econometrics in the 80ies that revolutionized empirical work in
More informationSYSTEMS OF REGRESSION EQUATIONS
SYSTEMS OF REGRESSION EQUATIONS 1. MULTIPLE EQUATIONS y nt = x nt n + u nt, n = 1,...,N, t = 1,...,T, x nt is 1 k, and n is k 1. This is a version of the standard regression model where the observations
More informationEstimating the Effect of Tax Reform in Differentiated Product Oligopolistic Markets
Estimating the Effect of Tax Reform in Differentiated Product Oligopolistic Markets by Chaim Fershtman, Tel Aviv University & CentER, Tilburg University Neil Gandal*, Tel Aviv University & CEPR, and Sarit
More informationEstimating the random coefficients logit model of demand using aggregate data
Estimating the random coefficients logit model of demand using aggregate data David Vincent Deloitte Economic Consulting London, UK davivincent@deloitte.co.uk September 14, 2012 Introduction Estimation
More informationLecture 3: Finding integer solutions to systems of linear equations
Lecture 3: Finding integer solutions to systems of linear equations Algorithmic Number Theory (Fall 2014) Rutgers University Swastik Kopparty Scribe: Abhishek Bhrushundi 1 Overview The goal of this lecture
More informationEconometric analysis of the Belgian car market
Econometric analysis of the Belgian car market By: Prof. dr. D. Czarnitzki/ Ms. Céline Arts Tim Verheyden Introduction In contrast to typical examples from microeconomics textbooks on homogeneous goods
More informationCovariance and Correlation
Covariance and Correlation ( c Robert J. Serfling Not for reproduction or distribution) We have seen how to summarize a data-based relative frequency distribution by measures of location and spread, such
More informationNotes V General Equilibrium: Positive Theory. 1 Walrasian Equilibrium and Excess Demand
Notes V General Equilibrium: Positive Theory In this lecture we go on considering a general equilibrium model of a private ownership economy. In contrast to the Notes IV, we focus on positive issues such
More informationSimple Regression Theory II 2010 Samuel L. Baker
SIMPLE REGRESSION THEORY II 1 Simple Regression Theory II 2010 Samuel L. Baker Assessing how good the regression equation is likely to be Assignment 1A gets into drawing inferences about how close the
More informationChapter 27: Taxation. 27.1: Introduction. 27.2: The Two Prices with a Tax. 27.2: The Pre-Tax Position
Chapter 27: Taxation 27.1: Introduction We consider the effect of taxation on some good on the market for that good. We ask the questions: who pays the tax? what effect does it have on the equilibrium
More informationMATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1.
MATH10212 Linear Algebra Textbook: D. Poole, Linear Algebra: A Modern Introduction. Thompson, 2006. ISBN 0-534-40596-7. Systems of Linear Equations Definition. An n-dimensional vector is a row or a column
More information1 Short Introduction to Time Series
ECONOMICS 7344, Spring 202 Bent E. Sørensen January 24, 202 Short Introduction to Time Series A time series is a collection of stochastic variables x,.., x t,.., x T indexed by an integer value t. The
More informationEconometric Tools for Analyzing Market Outcomes.
Econometric Tools for Analyzing Market Outcomes. Daniel Ackerberg, C. Lanier Benkard, Steven Berry, and Ariel Pakes. June 14, 2006 Abstract This paper outlines recently developed techniques for estimating
More informationRegression III: Advanced Methods
Lecture 16: Generalized Additive Models Regression III: Advanced Methods Bill Jacoby Michigan State University http://polisci.msu.edu/jacoby/icpsr/regress3 Goals of the Lecture Introduce Additive Models
More informationLecture notes: single-agent dynamics 1
Lecture notes: single-agent dynamics 1 Single-agent dynamic optimization models In these lecture notes we consider specification and estimation of dynamic optimization models. Focus on single-agent models.
More informationOnline Appendices to the Corporate Propensity to Save
Online Appendices to the Corporate Propensity to Save Appendix A: Monte Carlo Experiments In order to allay skepticism of empirical results that have been produced by unusual estimators on fairly small
More informationc. Given your answer in part (b), what do you anticipate will happen in this market in the long-run?
Perfect Competition Questions Question 1 Suppose there is a perfectly competitive industry where all the firms are identical with identical cost curves. Furthermore, suppose that a representative firm
More informationEstimating Price Elasticities in Differentiated Product Demand Models with Endogenous Characteristics
Estimating Price Elasticities in Differentiated Product Demand Models with Endogenous Characteristics Daniel A. Ackerberg UCLA Gregory S. Crawford University of Arizona March 27, 2009 Preliminary and Incomplete
More informationOn Marginal Effects in Semiparametric Censored Regression Models
On Marginal Effects in Semiparametric Censored Regression Models Bo E. Honoré September 3, 2008 Introduction It is often argued that estimation of semiparametric censored regression models such as the
More informationLeast Squares Estimation
Least Squares Estimation SARA A VAN DE GEER Volume 2, pp 1041 1045 in Encyclopedia of Statistics in Behavioral Science ISBN-13: 978-0-470-86080-9 ISBN-10: 0-470-86080-4 Editors Brian S Everitt & David
More information1.7 Graphs of Functions
64 Relations and Functions 1.7 Graphs of Functions In Section 1.4 we defined a function as a special type of relation; one in which each x-coordinate was matched with only one y-coordinate. We spent most
More informationSolution to Homework 2
Solution to Homework 2 Olena Bormashenko September 23, 2011 Section 1.4: 1(a)(b)(i)(k), 4, 5, 14; Section 1.5: 1(a)(b)(c)(d)(e)(n), 2(a)(c), 13, 16, 17, 18, 27 Section 1.4 1. Compute the following, if
More informationTesting Cost Inefficiency under Free Entry in the Real Estate Brokerage Industry
Web Appendix to Testing Cost Inefficiency under Free Entry in the Real Estate Brokerage Industry Lu Han University of Toronto lu.han@rotman.utoronto.ca Seung-Hyun Hong University of Illinois hyunhong@ad.uiuc.edu
More informationMULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS
MULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS MSR = Mean Regression Sum of Squares MSE = Mean Squared Error RSS = Regression Sum of Squares SSE = Sum of Squared Errors/Residuals α = Level of Significance
More informationLS.6 Solution Matrices
LS.6 Solution Matrices In the literature, solutions to linear systems often are expressed using square matrices rather than vectors. You need to get used to the terminology. As before, we state the definitions
More informationIDENTIFICATION IN A CLASS OF NONPARAMETRIC SIMULTANEOUS EQUATIONS MODELS. Steven T. Berry and Philip A. Haile. March 2011 Revised April 2011
IDENTIFICATION IN A CLASS OF NONPARAMETRIC SIMULTANEOUS EQUATIONS MODELS By Steven T. Berry and Philip A. Haile March 2011 Revised April 2011 COWLES FOUNDATION DISCUSSION PAPER NO. 1787R COWLES FOUNDATION
More informationPrincipal components analysis
CS229 Lecture notes Andrew Ng Part XI Principal components analysis In our discussion of factor analysis, we gave a way to model data x R n as approximately lying in some k-dimension subspace, where k
More informationMATRIX ALGEBRA AND SYSTEMS OF EQUATIONS
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS Systems of Equations and Matrices Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a
More information2. Linear regression with multiple regressors
2. Linear regression with multiple regressors Aim of this section: Introduction of the multiple regression model OLS estimation in multiple regression Measures-of-fit in multiple regression Assumptions
More informationOn the Efficiency of Competitive Stock Markets Where Traders Have Diverse Information
Finance 400 A. Penati - G. Pennacchi Notes on On the Efficiency of Competitive Stock Markets Where Traders Have Diverse Information by Sanford Grossman This model shows how the heterogeneous information
More informationFIXED EFFECTS AND RELATED ESTIMATORS FOR CORRELATED RANDOM COEFFICIENT AND TREATMENT EFFECT PANEL DATA MODELS
FIXED EFFECTS AND RELATED ESTIMATORS FOR CORRELATED RANDOM COEFFICIENT AND TREATMENT EFFECT PANEL DATA MODELS Jeffrey M. Wooldridge Department of Economics Michigan State University East Lansing, MI 48824-1038
More informationComparing Features of Convenient Estimators for Binary Choice Models With Endogenous Regressors
Comparing Features of Convenient Estimators for Binary Choice Models With Endogenous Regressors Arthur Lewbel, Yingying Dong, and Thomas Tao Yang Boston College, University of California Irvine, and Boston
More informationSolutions to Math 51 First Exam January 29, 2015
Solutions to Math 5 First Exam January 29, 25. ( points) (a) Complete the following sentence: A set of vectors {v,..., v k } is defined to be linearly dependent if (2 points) there exist c,... c k R, not
More informationCompetition Between Cable and Satellite: A Brief History. Demand Estimation. Amil Petrin University of Texas, Austin.
Competition Between Cable and Satellite: A Brief History Demand Estimation Amil Petrin University of Texas, Austin January, 2012 Early Cable Television: Era of Regulated Prices (pre-1996) Starts in 1950
More informationELASTICITY OF LONG DISTANCE TRAVELLING
Mette Aagaard Knudsen, DTU Transport, mak@transport.dtu.dk ELASTICITY OF LONG DISTANCE TRAVELLING ABSTRACT With data from the Danish expenditure survey for 12 years 1996 through 2007, this study analyses
More informationECON 459 Game Theory. Lecture Notes Auctions. Luca Anderlini Spring 2015
ECON 459 Game Theory Lecture Notes Auctions Luca Anderlini Spring 2015 These notes have been used before. If you can still spot any errors or have any suggestions for improvement, please let me know. 1
More informationMultiple Choice Models II
Multiple Choice Models II Laura Magazzini University of Verona laura.magazzini@univr.it http://dse.univr.it/magazzini Laura Magazzini (@univr.it) Multiple Choice Models II 1 / 28 Categorical data Categorical
More informationFULLY MODIFIED OLS FOR HETEROGENEOUS COINTEGRATED PANELS
FULLY MODIFIED OLS FOR HEEROGENEOUS COINEGRAED PANELS Peter Pedroni ABSRAC his chapter uses fully modified OLS principles to develop new methods for estimating and testing hypotheses for cointegrating
More informationThe RBC methodology also comes down to two principles:
Chapter 5 Real business cycles 5.1 Real business cycles The most well known paper in the Real Business Cycles (RBC) literature is Kydland and Prescott (1982). That paper introduces both a specific theory
More informationThe Bivariate Normal Distribution
The Bivariate Normal Distribution This is Section 4.7 of the st edition (2002) of the book Introduction to Probability, by D. P. Bertsekas and J. N. Tsitsiklis. The material in this section was not included
More information15.062 Data Mining: Algorithms and Applications Matrix Math Review
.6 Data Mining: Algorithms and Applications Matrix Math Review The purpose of this document is to give a brief review of selected linear algebra concepts that will be useful for the course and to develop
More informationClustering in the Linear Model
Short Guides to Microeconometrics Fall 2014 Kurt Schmidheiny Universität Basel Clustering in the Linear Model 2 1 Introduction Clustering in the Linear Model This handout extends the handout on The Multiple
More informationChapter 1 Introduction. 1.1 Introduction
Chapter 1 Introduction 1.1 Introduction 1 1.2 What Is a Monte Carlo Study? 2 1.2.1 Simulating the Rolling of Two Dice 2 1.3 Why Is Monte Carlo Simulation Often Necessary? 4 1.4 What Are Some Typical Situations
More informationMarketing Mix Modelling and Big Data P. M Cain
1) Introduction Marketing Mix Modelling and Big Data P. M Cain Big data is generally defined in terms of the volume and variety of structured and unstructured information. Whereas structured data is stored
More informationECON 600 Lecture 5: Market Structure - Monopoly. Monopoly: a firm that is the only seller of a good or service with no close substitutes.
I. The Definition of Monopoly ECON 600 Lecture 5: Market Structure - Monopoly Monopoly: a firm that is the only seller of a good or service with no close substitutes. This definition is abstract, just
More informationMath 4310 Handout - Quotient Vector Spaces
Math 4310 Handout - Quotient Vector Spaces Dan Collins The textbook defines a subspace of a vector space in Chapter 4, but it avoids ever discussing the notion of a quotient space. This is understandable
More information[1] Diagonal factorization
8.03 LA.6: Diagonalization and Orthogonal Matrices [ Diagonal factorization [2 Solving systems of first order differential equations [3 Symmetric and Orthonormal Matrices [ Diagonal factorization Recall:
More informationCentre for Central Banking Studies
Centre for Central Banking Studies Technical Handbook No. 4 Applied Bayesian econometrics for central bankers Andrew Blake and Haroon Mumtaz CCBS Technical Handbook No. 4 Applied Bayesian econometrics
More informationINDIRECT INFERENCE (prepared for: The New Palgrave Dictionary of Economics, Second Edition)
INDIRECT INFERENCE (prepared for: The New Palgrave Dictionary of Economics, Second Edition) Abstract Indirect inference is a simulation-based method for estimating the parameters of economic models. Its
More informationIDENTIFICATION IN DIFFERENTIATED PRODUCTS MARKETS USING MARKET LEVEL DATA
Econometrica, Vol. 82, No. 5 (September, 2014), 1749 1797 IDENTIFICATION IN DIFFERENTIATED PRODUCTS MARKETS USING MARKET LEVEL DATA BY STEVEN T. BERRY AND PHILIP A. HAILE 1 We present new identification
More informationCOMP6053 lecture: Relationship between two variables: correlation, covariance and r-squared. jn2@ecs.soton.ac.uk
COMP6053 lecture: Relationship between two variables: correlation, covariance and r-squared jn2@ecs.soton.ac.uk Relationships between variables So far we have looked at ways of characterizing the distribution
More informationMoral Hazard. Itay Goldstein. Wharton School, University of Pennsylvania
Moral Hazard Itay Goldstein Wharton School, University of Pennsylvania 1 Principal-Agent Problem Basic problem in corporate finance: separation of ownership and control: o The owners of the firm are typically
More informationECON 312: Oligopolisitic Competition 1. Industrial Organization Oligopolistic Competition
ECON 312: Oligopolisitic Competition 1 Industrial Organization Oligopolistic Competition Both the monopoly and the perfectly competitive market structure has in common is that neither has to concern itself
More informationGasoline Taxes and Fuel Economy: A Preference Heterogeneity Approach
Gasoline Taxes and Fuel Economy: A Preference Heterogeneity Approach Mario Samano March, 2012 Abstract This paper estimates an equilibrium model for the U.S. car market to measure the value of two policies
More informationDepartment of Economics
Department of Economics On Testing for Diagonality of Large Dimensional Covariance Matrices George Kapetanios Working Paper No. 526 October 2004 ISSN 1473-0278 On Testing for Diagonality of Large Dimensional
More informationSystems of Linear Equations
Systems of Linear Equations Beifang Chen Systems of linear equations Linear systems A linear equation in variables x, x,, x n is an equation of the form a x + a x + + a n x n = b, where a, a,, a n and
More informationThis puzzle is based on the following anecdote concerning a Hungarian sociologist and his observations of circles of friends among children.
0.1 Friend Trends This puzzle is based on the following anecdote concerning a Hungarian sociologist and his observations of circles of friends among children. In the 1950s, a Hungarian sociologist S. Szalai
More informationFEGYVERNEKI SÁNDOR, PROBABILITY THEORY AND MATHEmATICAL
FEGYVERNEKI SÁNDOR, PROBABILITY THEORY AND MATHEmATICAL STATIsTICs 4 IV. RANDOm VECTORs 1. JOINTLY DIsTRIBUTED RANDOm VARIABLEs If are two rom variables defined on the same sample space we define the joint
More informationRow Echelon Form and Reduced Row Echelon Form
These notes closely follow the presentation of the material given in David C Lay s textbook Linear Algebra and its Applications (3rd edition) These notes are intended primarily for in-class presentation
More information6.3 Conditional Probability and Independence
222 CHAPTER 6. PROBABILITY 6.3 Conditional Probability and Independence Conditional Probability Two cubical dice each have a triangle painted on one side, a circle painted on two sides and a square painted
More informationSample Size and Power in Clinical Trials
Sample Size and Power in Clinical Trials Version 1.0 May 011 1. Power of a Test. Factors affecting Power 3. Required Sample Size RELATED ISSUES 1. Effect Size. Test Statistics 3. Variation 4. Significance
More informationChapter 2 Portfolio Management and the Capital Asset Pricing Model
Chapter 2 Portfolio Management and the Capital Asset Pricing Model In this chapter, we explore the issue of risk management in a portfolio of assets. The main issue is how to balance a portfolio, that
More informationWHERE DOES THE 10% CONDITION COME FROM?
1 WHERE DOES THE 10% CONDITION COME FROM? The text has mentioned The 10% Condition (at least) twice so far: p. 407 Bernoulli trials must be independent. If that assumption is violated, it is still okay
More information3. Mathematical Induction
3. MATHEMATICAL INDUCTION 83 3. Mathematical Induction 3.1. First Principle of Mathematical Induction. Let P (n) be a predicate with domain of discourse (over) the natural numbers N = {0, 1,,...}. If (1)
More informationChapter 10. Key Ideas Correlation, Correlation Coefficient (r),
Chapter 0 Key Ideas Correlation, Correlation Coefficient (r), Section 0-: Overview We have already explored the basics of describing single variable data sets. However, when two quantitative variables
More informationRating music: Accounting for rating preferences Manish Gupte Purdue University (mmgupte@purdue.edu)
Rating music: Accounting for rating preferences Manh Gupte Purdue University (mmgupte@purdue.edu) Th paper investigates how consumers likes and dlikes influence their decion to rate a song. Yahoo! s survey
More informationChapter 1. Vector autoregressions. 1.1 VARs and the identi cation problem
Chapter Vector autoregressions We begin by taking a look at the data of macroeconomics. A way to summarize the dynamics of macroeconomic data is to make use of vector autoregressions. VAR models have become
More informationModule 3: Correlation and Covariance
Using Statistical Data to Make Decisions Module 3: Correlation and Covariance Tom Ilvento Dr. Mugdim Pašiƒ University of Delaware Sarajevo Graduate School of Business O ften our interest in data analysis
More informationStatistical tests for SPSS
Statistical tests for SPSS Paolo Coletti A.Y. 2010/11 Free University of Bolzano Bozen Premise This book is a very quick, rough and fast description of statistical tests and their usage. It is explicitly
More informationON THE ROBUSTNESS OF FIXED EFFECTS AND RELATED ESTIMATORS IN CORRELATED RANDOM COEFFICIENT PANEL DATA MODELS
ON THE ROBUSTNESS OF FIXED EFFECTS AND RELATED ESTIMATORS IN CORRELATED RANDOM COEFFICIENT PANEL DATA MODELS Jeffrey M. Wooldridge THE INSTITUTE FOR FISCAL STUDIES DEPARTMENT OF ECONOMICS, UCL cemmap working
More informationStatistics 104: Section 6!
Page 1 Statistics 104: Section 6! TF: Deirdre (say: Dear-dra) Bloome Email: dbloome@fas.harvard.edu Section Times Thursday 2pm-3pm in SC 109, Thursday 5pm-6pm in SC 705 Office Hours: Thursday 6pm-7pm SC
More informationIntroduction to General and Generalized Linear Models
Introduction to General and Generalized Linear Models General Linear Models - part I Henrik Madsen Poul Thyregod Informatics and Mathematical Modelling Technical University of Denmark DK-2800 Kgs. Lyngby
More informationSUPPLEMENT TO USAGE-BASED PRICING AND DEMAND FOR RESIDENTIAL BROADBAND : APPENDIX (Econometrica, Vol. 84, No. 2, March 2016, 411 443)
Econometrica Supplementary Material SUPPLEMENT TO USAGE-BASED PRICING AND DEMAND FOR RESIDENTIAL BROADBAND : APPENDIX (Econometrica, Vol. 84, No. 2, March 2016, 411 443) BY AVIV NEVO, JOHN L. TURNER, AND
More informationChapter 3: The Multiple Linear Regression Model
Chapter 3: The Multiple Linear Regression Model Advanced Econometrics - HEC Lausanne Christophe Hurlin University of Orléans November 23, 2013 Christophe Hurlin (University of Orléans) Advanced Econometrics
More informationMarkups and Firm-Level Export Status: Appendix
Markups and Firm-Level Export Status: Appendix De Loecker Jan - Warzynski Frederic Princeton University, NBER and CEPR - Aarhus School of Business Forthcoming American Economic Review Abstract This is
More informationMATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS 1. SYSTEMS OF EQUATIONS AND MATRICES 1.1. Representation of a linear system. The general system of m equations in n unknowns can be written a 11 x 1 + a 12 x 2 +
More informationCALCULATIONS & STATISTICS
CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 1-5 scale to 0-100 scores When you look at your report, you will notice that the scores are reported on a 0-100 scale, even though respondents
More informationPoint and Interval Estimates
Point and Interval Estimates Suppose we want to estimate a parameter, such as p or µ, based on a finite sample of data. There are two main methods: 1. Point estimate: Summarize the sample by a single number
More informationAn introduction to Value-at-Risk Learning Curve September 2003
An introduction to Value-at-Risk Learning Curve September 2003 Value-at-Risk The introduction of Value-at-Risk (VaR) as an accepted methodology for quantifying market risk is part of the evolution of risk
More informationThe primary goal of this thesis was to understand how the spatial dependence of
5 General discussion 5.1 Introduction The primary goal of this thesis was to understand how the spatial dependence of consumer attitudes can be modeled, what additional benefits the recovering of spatial
More informationEconometrics Simple Linear Regression
Econometrics Simple Linear Regression Burcu Eke UC3M Linear equations with one variable Recall what a linear equation is: y = b 0 + b 1 x is a linear equation with one variable, or equivalently, a straight
More informationElasticity. I. What is Elasticity?
Elasticity I. What is Elasticity? The purpose of this section is to develop some general rules about elasticity, which may them be applied to the four different specific types of elasticity discussed in
More informationNOTES ON LINEAR TRANSFORMATIONS
NOTES ON LINEAR TRANSFORMATIONS Definition 1. Let V and W be vector spaces. A function T : V W is a linear transformation from V to W if the following two properties hold. i T v + v = T v + T v for all
More informationH. Allen Klaiber Department of Agricultural and Resource Economics North Carolina State University
Incorporating Random Coefficients and Alternative Specific Constants into Discrete Choice Models: Implications for In- Sample Fit and Welfare Estimates * H. Allen Klaiber Department of Agricultural and
More informationIt is important to bear in mind that one of the first three subscripts is redundant since k = i -j +3.
IDENTIFICATION AND ESTIMATION OF AGE, PERIOD AND COHORT EFFECTS IN THE ANALYSIS OF DISCRETE ARCHIVAL DATA Stephen E. Fienberg, University of Minnesota William M. Mason, University of Michigan 1. INTRODUCTION
More informationTo give it a definition, an implicit function of x and y is simply any relationship that takes the form:
2 Implicit function theorems and applications 21 Implicit functions The implicit function theorem is one of the most useful single tools you ll meet this year After a while, it will be second nature to
More informationPricing I: Linear Demand
Pricing I: Linear Demand This module covers the relationships between price and quantity, maximum willing to buy, maximum reservation price, profit maximizing price, and price elasticity, assuming a linear
More informationSOLVING LINEAR SYSTEMS
SOLVING LINEAR SYSTEMS Linear systems Ax = b occur widely in applied mathematics They occur as direct formulations of real world problems; but more often, they occur as a part of the numerical analysis
More informationLecture Note: Self-Selection The Roy Model. David H. Autor MIT 14.661 Spring 2003 November 14, 2003
Lecture Note: Self-Selection The Roy Model David H. Autor MIT 14.661 Spring 2003 November 14, 2003 1 1 Introduction A core topic in labor economics is self-selection. What this term means in theory is
More informationEconomics of Insurance
Economics of Insurance In this last lecture, we cover most topics of Economics of Information within a single application. Through this, you will see how the differential informational assumptions allow
More informationA Basic Introduction to Missing Data
John Fox Sociology 740 Winter 2014 Outline Why Missing Data Arise Why Missing Data Arise Global or unit non-response. In a survey, certain respondents may be unreachable or may refuse to participate. Item
More informationA Practitioner s Guide to Estimation of Random-Coef cients Logit Models of Demand
A Practitioner s Guide to Estimation of Random-Coef cients Logit Models of Demand Aviv Nevo University of California Berkeley, Berkeley, CA 94720-3880 and NBER Estimation of demand is at the heart of many
More information4.5 Linear Dependence and Linear Independence
4.5 Linear Dependence and Linear Independence 267 32. {v 1, v 2 }, where v 1, v 2 are collinear vectors in R 3. 33. Prove that if S and S are subsets of a vector space V such that S is a subset of S, then
More informationMachine Learning Methods for Demand Estimation
Machine Learning Methods for Demand Estimation By Patrick Bajari, Denis Nekipelov, Stephen P. Ryan, and Miaoyu Yang Over the past decade, there has been a high level of interest in modeling consumer behavior
More informationOptimal linear-quadratic control
Optimal linear-quadratic control Martin Ellison 1 Motivation The lectures so far have described a general method - value function iterations - for solving dynamic programming problems. However, one problem
More informationSection 14 Simple Linear Regression: Introduction to Least Squares Regression
Slide 1 Section 14 Simple Linear Regression: Introduction to Least Squares Regression There are several different measures of statistical association used for understanding the quantitative relationship
More informationCAPM, Arbitrage, and Linear Factor Models
CAPM, Arbitrage, and Linear Factor Models CAPM, Arbitrage, Linear Factor Models 1/ 41 Introduction We now assume all investors actually choose mean-variance e cient portfolios. By equating these investors
More informationTiers, Preference Similarity, and the Limits on Stable Partners
Tiers, Preference Similarity, and the Limits on Stable Partners KANDORI, Michihiro, KOJIMA, Fuhito, and YASUDA, Yosuke February 7, 2010 Preliminary and incomplete. Do not circulate. Abstract We consider
More information