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Mobility and the Return to Education: Testing a Roy Model with Multiple Markets
迁移与教育回报:多市场 Roy 模型的检验
Self-selected migration presents one potential explanation for why observed returns to a college education in local labor markets vary widely even though U.S. workers are highly mobile.To assess the impact of self-selection on estimated returns, this paper first develops a Roy model of mobility and earnings where workers choose in which of the 50 states (plus the District of Columbia) to live and work.Available estimation methods are either infeasible for a selection model…
The Law of Large Demand for Information
信息大需求定律
An unresolved problem in Bayesian decision theory is how to value and price information. This paper resolves both problems assuming inexpensive information. Building on Large Deviation Theory, we produce a generically complete asymptotic order on samples of i.i.d. signals in finite-state, finite-action models. Computing the marginal value of an additional signal, we find it is eventually exponentially falling in quantity, and higher for lower quality signals. We provide a…
Equilibrium Wage Dispersion with Worker and Employer Heterogeneity
劳动者与雇主异质性下的均衡工资离散
We construct and estimate an equilibrium search model with on–the–job–search. Firms make take–it–or–leave–it wage offers to workers conditional on their characteristics and they can respond to the outside job offers received by their employees. Unobserved worker productive heterogeneity is introduced in the form of cross–worker differences in a “competence” parameter. On the other side of the market, firms also are heterogeneous with respect to their marginal productivity of…
Two Competing Models of How People Learn in Games
博弈中人类学习的两种竞争模型
Reinforcement learning and stochastic fictitious play are apparent rivals as models of humans learning. They embody quite different assumptions about the processing of information and optimisation. This paper compares their properties and finds that they are far more similar than were thought. In particular, the expected motion of stochastic fictitious play and reinforcement learning with experimentation can both be written as a perturbed form of the evolutionary replicator…
Estimation of a Censored Dynamic Panel Data Model
Mobility and the Return to Education: Testing a Roy Model with Multiple Markets
Self–selected migration presents one potential explanation for why observed returns to a college education in local labor markets vary widely even though U.S. workers are highly mobile. To assess the impact of self–selection on estimated returns, this paper first develops a Roy model of mobility and earnings where workers choose in which of the 50 states (plus the District of Columbia) to live and work. Available estimation methods are either infeasible for a selection model…
Equilibrium Wage Dispersion with Worker and Employer Heterogeneity
劳动者与雇主异质性下的均衡工资离散
We construct and estimate an equilibrium search model with on-the-job-search. Firms make take-it-or-leave-it wage offers to workers conditional on their characteristics and they can respond to the outside job offers received by their employees. Unobserved worker productive heterogeneity is introduced in the form of cross-worker differences in a "competence" parameter. On the other side of the market, firms also are heterogeneous with respect to their marginal productivity of…
Discrete-Time Approximations of the Holmstrom-Milgrom Brownian-Motion Model of Intertemporal Incentive Provision
This paper studies the relation between discrete–time and continuous–time principal–agent models. We derive the continuous–time model as a limit of discrete–time models with ever shorter periods and show that optimal incentive schemes in the discrete–time models approximate the optimal incentive scheme in the continuous model, which is linear in accounts. Under the additional assumption that the principal observes only cumulative total profits at the end and the agent can…
Two Competing Models of How People Learn in Games
Reinforcement learning and stochastic fictitious play are apparent rivals as models of human learning. They embody quite different assumptions about the processing of information and optimization. This paper compares their properties and finds that they are far more similar than were thought. In particular, the expected motion of stochastic fictitious play and reinforcement learning with experimentation can both be written as a perturbed form of the evolutionary replicator…
Identification of Standard Auction Models
This paper presents new identification results for models of first–price, second–price, ascending (English), and descending (Dutch) auctions. We consider a general specification of the latent demand and information structure, nesting both private values and common values models, and allowing correlated types as well as ex ante asymmetry. We address identification of a series of nested models and derive testable restrictions enabling discrimination between models on the basis…
Nonparametric Estimation with Nonlinear Budget Sets
Choice models with nonlinear budget sets provide a precise way of accounting for the nonlinear tax structures present in many applications. In this paper we propose a nonparametric approach to estimation of these models. The basic idea is to think of the choice, in our case hours of labor supply, as being a function of the entire budget set. Then we can do nonparametric regression where the variable in the regression is the budget set. We reduce the dimensionality of this…
Simple Robust Testing of Regression Hypotheses: A Comment
The paper by Kiefer, Vogelsang and Bunzel (2000), KVB henceforth, provides an interesting unconventional application of functional limit theory to a conventional problem. In this note, we point out that the limiting distribution of the t^{∗} test proposed by KVB turns out to be equivalent to the asymptotic distribution of one of the statistics analysed by Abadir and Paruolo (1997), AP henceforth. The mixed-Normal random variables studied in AP and KVB are different, but they…
Heteroskedasticity-Autocorrelation Robust Standard Errors Using The Bartlett Kernel Without Truncation
使用不截断Bartlett核的异方差与自相关稳健标准误
In this paper we analyze heteroskedasticity-autocorrelation (HAC) robust tests constructed using the Bartlett kernel without truncation. We show that while such an HAC estimator is not consistent, asymptotically valid testing is still possible. We show that tests using the Bartlett kernel without truncation are exactly equivalent to recent HAC robust tests proposed by Kiefer, Vogelsang and Bunzel (2000, Econometrica, 68, pp 695-714).
Semiparametric Binary Choice Panel Data Models Without Strictly Exogeneous Regressors
Weighted Minimum Mean-Square Distance from Independence Estimation
Coarse Matching
A Fractional Dickey-Fuller Test for Unit Roots
This paper presents a new test for fractionally integrated (FI) processes. In particular, we propose a testing procedure in the time domain that extends the well–known Dickey–Fuller approach, originally designed for the I(1) versus I(0) case, to the more general setup of FI(d0) versus FI(d1), with d1
The Optimality of a Simple Market Mechanism
Strategic behavior in a finite market can cause inefficiency in the allocation, and market mechanisms differ in how successfully they limit this inefficiency. A method for ranking algorithms in computer science is adapted here to rank market mechanisms according to how quickly inefficiency diminishes as the size of the market increases. It is shown that trade at a single market-clearing price in the "k"-double auction is "worst-case asymptotic optimal" among all plausible…
A Small Sample Correction for the Test of Cointegrating Rank in the Vector Autoregressive Model
With the cointegration formulation of economic long–run relations the test for cointegrating rank has become a useful econometric tool. The limit distribution of the test is often a poor approximation to the finite sample distribution and it is therefore relevant to derive an approximation to the expectation of the likelihood ratio test for cointegration in the vector autoregressive model in order to improve the finite sample properties. The correction factor depends on…
Pivotal Statistics for Testing Structural Parameters in Instrumental Variables Regression
We propose a novel statistic for conducting joint tests on all the structural parameters in instrumental variables regression. The statistic is straightforward to compute and equals a quadratic form of the score of the concentrated log–likelihood. It therefore attains its minimal value equal to zero at the maximum likelihood estimator. The statistic has a χ2 limiting distribution with a degrees of freedom parameter equal to the number of structural parameters. The limiting…
Rank Estimation of Transformation Models
Adapting to Unknown Disturbance Autocorrelation in Regression with Long Memory
We show that it is possible to adapt to nonparametric disturbance autocorrelation in time series regression in the presence of long memory in both regressors and disturbances by using a smoothed nonparametric spectrum estimate in frequency–domain generalized least squares. When the collective memory in regressors and disturbances is sufficiently strong, ordinary least squares is not only asymptotically inefficient but asymptotically non–normal and has a slow rate of…