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Template-Type: ReDIF-Paper 1.0
Title: On the Estimation of Structural Hedonic Price Models
Author-Name: James N. Brown
Author-Name: Harvey S. Rosen
Author-Person: pro55
Note: PE
Number: 0018
Creation-Date: 1982-05
Order-URL: http://www.nber.org/papers/t0018
File-URL: http://www.nber.org/papers/t0018.pdf
File-Format: application/pdf
Publication-Status: published as Brown, James N. and Rosen, Harvey S. "On the Estimation of Structural Hedonic Price Models." Econometrica, Vol. 50, No. 3, (May 1982), pp. 765-768.
Abstract: MANY COMMODITIES can be viewed as bundles of individual attributes for which no explicit markets exist. It is often of interest to estimate structural demand and supply functions for these attributes, but the absence of directly observable attribute prices poses a problem for such estimation. In an influential paper published several years ago, Rosen [3] proposed an estimation procedure to surmount this problem. This procedure has since been used in a number of applications (see, for example, Harrison and Rubinfeld [2] or Witte, et al. [4]). The purpose of this note is to point out certain pitfalls in Rosen's procedure, which, if ignored, could lead to major identification problems. In Section 2 we summarize briefly the key aspects of Rosen's method as it has been applied in the literature. Section 3 discusses the potential problems inherent in this procedure and provides an example. Section 4 concludes with a few suggestions for future research.
Handle: RePEc:nbr:nberte:0018
Template-Type: ReDIF-Paper 1.0
Title: Using Information on the Moments of Disturbances to Increase the Efficiency of Estimation
Author-Name: Thomas E. MaCurdy
Note: LS
Number: 0022
Creation-Date: 1982-05
Order-URL: http://www.nber.org/papers/t0022
File-URL: http://www.nber.org/papers/t0022.pdf
File-Format: application/pdf
Abstract: Econometric analyses of treatment response commonly use instrumental variable (IV) assumptions to identify treatment effects. Yet the credibility of IV assumptions is often a matter of considerable disagreement, with much debate about whether some covariate is or is not a 'valid instrument' in an application of interest. There is therefore good reason to consider weaker but more credible assumptions. To this end, we introduce monotone instrumental variable (MIV) assumptions. A particularly interesting special case of an MIV assumption is monotone treatment selection (MTS). IV and MIV assumptions may be imposed alone or in combination with other assumptions. We study the identifying power of MIV assumptions in three informational settings: MIV alone; MIV combined with the classical linear response assumption; MIV combined with the monotone treatment response (MTR) assumption. We apply the results to the problem of inference on the returns to schooling. We analyze wage data reported by white male respondents to the National Longitudinal Survey of Youth (NLSY) and use the respondent's AFQT score as an MIV. We find that this MIV assumption has little identifying power when imposed alone. However, combining the MIV assumption with the MTR and MTS assumptions yields fairly tight bounds on two distinct measures of the returns to schooling.
Handle: RePEc:nbr:nberte:0022
Template-Type: ReDIF-Paper 1.0
Title: Stochastic Capital Theory I. Comparative Statics
Author-Name: William A. Brock
Author-Person: pbr142
Author-Name: Michael Rothschild
Author-Person: pro48
Author-Name: Joseph E. Stiglitz
Note: ME
Number: 0023
Creation-Date: 1982-05
Order-URL: http://www.nber.org/papers/t0023
File-URL: http://www.nber.org/papers/t0023.pdf
File-Format: application/pdf
Publication-Status: published as pp. 591-622, 1989. MacMillan Press: LOndon. Joan Robinson and Modern Aconomic Theory
Abstract: Introductory lectures on capital theory often begin by analyzing the following problem: I have a tree which will be worth X(t) if cut down at time t. If the discount rate is r, when should the tree be cut down? What is the present value of such a tree? The answers to these questions are straightforward. Since at time t a tree which I plan to cut down at time T is worth e[to the power of rt]e[to the power of ?rT]X(T), I should choose the cutting date T* to maximize e[to the power of -rT]X(T); at t < T* a tree is worth e[to the power of rt]e[to the power of -rT*]X(T*). In this paper we analyze how the answers to these questions of timing and evaluation change when the tree's growth is stochastic rather than deterministic. Suppose a tree will be worth X(t,w) if cut down at time t when X(t,w) is a stochastic process. When should it be cut down? What is its present value? We study these questions for trees which grow according to both discrete and continuous stochastic processes. The approach to continuous time stochastic processes contrasts with much of the finance literature in two respects. First, we obtain sharp aomparative statics results without restricting ourselves to particu,ar stochastic specifications. Second, while the option pricing literature seems to imply that increases in variance always increase value, we show that an increase in the variance of a Tree's growth has ambiguous effects on its value.
Handle: RePEc:nbr:nberte:0023
Template-Type: ReDIF-Paper 1.0
Title: Identification in Dynamic Linear Models with Rational Expectations
Author-Name: Olivier J. Blanchard
Author-Person: pbl2
Note: EFG
Number: 0024
Creation-Date: 1982-07
Order-URL: http://www.nber.org/papers/t0024
File-URL: http://www.nber.org/papers/t0024.pdf
File-Format: application/pdf
Abstract: This paper characterizes identification in dynamic linear models. It shows that identification restrictions are linear in the structural parameters and are therefore easy to use. Using these restrictions, it analyzes the role of exogenous variables in helping to achieve identification.
Handle: RePEc:nbr:nberte:0024
Template-Type: ReDIF-Paper 1.0
Title: Smoothness Priors and Nonlinear Regression
Author-Name: Robert J. Shiller
Author-Person: psh69
Number: 0025
Creation-Date: 1982-08
Order-URL: http://www.nber.org/papers/t0025
File-URL: http://www.nber.org/papers/t0025.pdf
File-Format: application/pdf
Publication-Status: published as Shiller, Robert J. "Smoothness Priors and Nonlinear Regression." Journal of the American Statistical Association, Vol. 79, No. 387, (September 1984) .
Abstract: In applications, the linear multiple regression model is often modified to allow for nonlinearity in an independent variable. It is argued here that in practice it may often be desirable to specify a Bayesian prior that the unknown functional form is "simple" or "uncomplicated" rather than to parametize the nonlinearity. "Discrete smoothness priors" and "continuous smoothness priors" are defined and it is shown how posterior mean estimates can easily be derived using ordinary multiple linear regression modified with dummy variables and dummy observations. Relationships with spline and polynomial interpolation are pointed out. Illustrative examples of cost function estimation are provided.
Handle: RePEc:nbr:nberte:0025
Template-Type: ReDIF-Paper 1.0
Title: Formulation and Estimation of Dynamic Factor Demand Equations Under Non-Static Expectations: A Finite Horizon Model
Author-Name: Ingmar R. Prucha
Author-Person: ppr355
Author-Name: M. Ishaq Nadiri
Note: PR
Number: 0026
Creation-Date: 1982-10
Order-URL: http://www.nber.org/papers/t0026
File-URL: http://www.nber.org/papers/t0026.pdf
File-Format: application/pdf
Publication-Status: published as Journal of Econometrics, Vol. 33, no. 1/2, pp. 187-211, 1986.
Abstract: This paper proposes a discrete model of investment behavior that incorporates general nonstatic expectations with a general cost of adjustment technology. The combination of these two features usually leads to a set of highly nonlinear first order conditions for the optimal input plan; the expectational variables work in addition as shift parameters. Consequently, an explicit analytic solution for derived factor demand is in general difficult if not impossible to obtain. Simplifying assumptions on the technology and/or the form of the expectational process are therefore typically made in the literature. In this paper we develop an algorithm for the estimation of flexible forms of derived factor demand equations within the above general setting. By solving the first order conditions numerically at each iteration step this algorithm avoids the need for an explicit analytic solution. In particular we consider a model with a finite planning horizon. The relationship between the optimal input plans of the finite and infinite planning horizon model is explored. Due to the discrete setting of the model the forward looking behavior of investment is brought out very clearly. As a byproduct a consistent framework for the use of anticipation data on planned investment is developed.
Handle: RePEc:nbr:nberte:0026