ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476528

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

We consider the problem of inference about a quantity of interest given different sources of information linked by a deterministic population dynamics model. Our approach consists of translating all the available information into a joint premodel distribution on all the model inputs and outputs and then restricting this to the submanifold defined by the model to obtain the joint postmodel distribution. Marginalizing this yields inference, conditional on the model, about quantities of interest, which can be functions of model inputs, model outputs, or both. Samples from the postmodel distribution are obtained by importance sampling and Rubin's SIR algorithm. The framework includes as a special case the situation where the pre-model information about the outputs consists of measurements with error; this reduces to standard Bayesian inference. The results are in the form of a sample from the postmodel distribution and so can be examined using the full range of exploratory data analysis techniques. Methods for comparing competing population dynamics models are developed, based on a generalization of the Bayes factor idea. A key quantity used by the International Whaling Commission (IWC) in making decisions about bowhead whales,Balaena mysticetus, is the replacement yield, RY. Information about the species is of three main types: recent census information, historical catch records, and evidence about birth and death rates. These are combined using a special case of the Leslie matrix population dynamics model. Our method yields full inference about RY and also sheds light on other, sometimes controversial, questions of scientific interest. These ideas are also applicable to many simulation models in other areas of science and policy making. Software to implement these methods is available from StatLib.

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476529

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476530

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476531

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476532

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476533

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476534

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

Two-stage least squares (TSLS) is widely used in econometrics to estimate parameters in systems of linear simultaneous equations and to solve problems of omitted-variables bias in single-equation estimation. We show here that TSLS can also be used to estimate the average causal effect of variable treatments such as drug dosage, hours of exam preparation, cigarette smoking, and years of schooling. The average causal effect in which we are interested is a conditional expectation of the difference between the outcomes of the treated and what these outcomes would have been in the absence of treatment. Given mild regularity assumptions, the probability limit of TSLS is a weighted average of per-unit average causal effects along the length of an appropriately defined causal response function. The weighting function is illustrated in an empirical example based on the relationship between schooling and earnings.

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476535

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

We draw attention to two problems associated with the use of instrumental variables (IV), the importance of which for empirical work has not been fully appreciated. First, the use of instruments that explain little of the variation in the endogenous explanatory variables can lead to large inconsistencies in the IV estimates even if only a weak relationship exists between the instruments and the error in the structural equation. Second, in finite samples, IV estimates are biased in the same direction as ordinary least squares (OLS) estimates. The magnitude of the bias of IV estimates approaches that of OLS estimates as theR2between the instruments and the endogenous explanatory variable approaches 0. To illustrate these problems, we reexamine the results of a recent paper by Angrist and Krueger, who used large samples from the U.S. Census to estimate wage equations in which quarter of birth is used as an instrument for educational attainment. We find evidence that, despite huge sample sizes, their IV estimates may suffer from finite-sample bias and may be inconsistent as well. These findings suggest that valid instruments may be more difficult to find than previously imagined. They also indicate that the use of large data sets does not necessarily insulate researchers from quantitatively important finite-sample biases. We suggest that the partialR2and theFstatistic of the identifying instruments in the first-stage estimation are useful indicators of the quality of the IV estimates and should be routinely reported.

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476536

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

In this article we describe the cumulative distribution function of excess returns conditional on a broad set of predictors that summarize the state of the economy. We do so by estimating a sequence of conditional logit models over a grid of values of the response variable. Our method uncovers higher-order multidimensional structure that cannot be found by modeling only the first two moments of the distribution. We compare two approaches to modeling: one based on a conventional linear logit model and the other based on an additive logit. The second approach avoids the “curse of dimensionality” problem of fully nonparametric methods while retaining both interpretability and the ability to let the data determine the shape of the relationship between the response variable and the predictors. We find that the additive logit fits better and reveals aspects of the data that remain undetected by the linear logit. The additive model retains its superiority even in out-of-sample prediction and portfolio selection performance, suggesting that this model captures genuine features of the data that seem to be important to guide investors' optimal portfolio choices.

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476537

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor