Combining Panel Data Sets With Attrition And Refreshment Samples

Download Combining Panel Data Sets With Attrition And Refreshment Samples PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Combining Panel Data Sets With Attrition And Refreshment Samples book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Combining Panel Data Sets With Attrition And Refreshment Samples

Combining Panel Data Sets With Attrition And Refreshment Samples written by Keisuke Hirano and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Economics categories.

In many fields researchers wish to consider statistical models that allow for more complex relationships than can be inferred using only cross-sectional data. Panel or longitudinal data where the same units are observed repeatedly at different points in time can often provide the richer data needed for such models. Although such data allows researchers to identify more complex models than cross-sectional data, missing data problems can be more severe in panels. In particular, even units who respond in initial waves of the panel may drop out in subsequent waves, so that the subsample with complete data for all waves of the panel can be less representative of the population than the original sample. Sometimes, in the hope of mitigating the effects of attrition without losing the advantages of panel data over cross-sections, panel data sets are augmented by replacing units who have dropped out with new units randomly sampled from the original population. Following Ridder (1992), who used these replacement units to test some models for attrition, we call such additional samples refreshment samples. We explore the benefits of these samples for estimating models of attrition. We describe the manner in which the presence of refreshment samples allows the researcher to test various models for attrition in panel data, including models based on the assumption that missing data are missing at random (MAR, Rubin, 1976; Little and Rubin, 1987). The main result in the paper makes precise the extent to which refreshment samples are informative about the attrition process; a class of non-ignorable missing data models can be identified without making strong distributional or functional form assumptions if refreshment samples are available.

Moment Estimation With Attrition

Moment Estimation With Attrition written by John M. Abowd and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Labor demand categories.

We present a method that accommodates missing data in longitudinal datasets of the type usually encountered in economic and social applications. The technique uses various extensions of missing at random' assumptions that we customize for dynamic models. Our method, applicable to longitudinal data on persons or firms, is implemented using the Generalized Method of Moments with reweighting that appropriately corrects for the attrition bias caused by the missing data. We apply the method to the estimation of dynamic labor demand models. The results demonstrate that the correction is extremely important.

A New Use Of Importance Sampling To Reduce Computational Burden In Simulation Estimation

A New Use Of Importance Sampling To Reduce Computational Burden In Simulation Estimation written by Daniel A. Ackerberg and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Econometrics categories.

Method of Simulated Moments (MSM) estimators introduced by McFadden (1989)and Pakes and Pollard (1989) are of great use to applied economists. They are relatively easy to use even for estimating very complicated economic models. One simply needs to generate simulated data according to the model and choose parameters that make moments of this simulated data as close as possible to moments of the true data. This paper uses importance sampling techniques to address a significant computational caveat regarding these MSM estimators - that often one's economic model is hard to solve. Examples include complicated equilibrium models and dynamic programming problems. We show that importance sampling can reduce he number of times a particular model needs to be solved in an estimation procedure, significantly decreasing computational burden.

Using Weights To Adjust For Sample Selection When Auxiliary Information Is Available

Using Weights To Adjust For Sample Selection When Auxiliary Information Is Available written by Aviv Nevo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Economics categories.

In this paper I analyze GMM estimation when the sample is not a random draw from the population of interest. I exploit auxiliary information, in the form of moments from the population of interest, in order to compute weights that are proportional to the inverse probability of selection. The essential idea is to construct weights, for each observation in the primary data, such that the moments of the weighted data are set equal to the additional moments. The estimator is applied to the Dutch Transportation Panel, in which refreshment draws were taken from the population of interest in order to deal with heavy attrition of the original panel. I show how these additional samples can be used to adjust for sample selection.

Robust Covariance Matrix Estimation With Data Dependent Var Prewhitening Order

Robust Covariance Matrix Estimation With Data Dependent Var Prewhitening Order written by Wouter J. Den Haan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Analysis of covariance categories.

This paper analyzes the performance of heteroskedasticity-and-autocorrelation-consistent (HAC) covariance matrix estimators in which the residuals are prewhitened using a vector autoregressive (VAR) filter. We highlight the pitfalls of using an arbitrarily fixed lag order for the VAR filter, and we demonstrate the benefits of using a model selection criterion (either AIC or BIC) to determine its lag structure. Furthermore, once data-dependent VAR prewhitening has been utilized, we find negligible or even counter-productive effects of applying standard kernel-based methods to the prewhitened residuals; that is, the performance of the prewhitened kernel estimator is virtually indistinguishable from that of the VARHAC estimator.

Journal Of The American Statistical Association

Journal Of The American Statistical Association written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Electronic journals categories.

Econometric Methods For Endogenously Sampled Time Series

Econometric Methods For Endogenously Sampled Time Series written by George J. Hall and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Sampling (Statistics) categories.

This paper studies the econometric problems associated with estimation of a stochastic process that is endogenously sampled. Our interest is to infer the law of motion of a discrete-time stochastic process {pt} that is observed only at a subset of times {t1,..., tn} that depend on the outcome of a probabilistic sampling rule that depends on the history of the process as well as other observed covariates xt . We focus on a particular example where pt denotes the daily wholesale price of a standardized steel product. However there are no formal exchanges or centralized markets where steel is traded and pt can be observed. Instead nearly all steel transaction prices are a result of private bilateral negotiations between buyers and sellers, typically intermediated by middlemen known as steel service centers. Even though there is no central record of daily transactions prices in the steel market, we do observe transaction prices for a particular firm -- a steel service center that purchases large quantities of steel in the wholesale market for subsequent resale in the retail market. The endogenous sampling problem arises from the fact that the firm only records pt on the days that it purchases steel. We present a parametric analysis of this problem under the assumption that the timing of steel purchases is part of an optimal trading strategy that maximizes the firm's expected discounted trading profits. We derive a parametric partial information maximum likelihood (PIML) estimator that solves the endogenous sampling problem and efficiently estimates the unknown parameters of a Markov transition probability that determines the law of motion for the underlying {pt} process. The PIML estimator also yields estimates of the structural parameters that determine the optimal trading rule. We also introduce an alternative consistent, less efficient, but computationally simpler simulated minimum distance (SMD) estimator that avoids high dimensional numerical integrations required by the PIML estimator. Using the SMD estimator, we provide estimates of a truncated lognormal AR(1) model of the wholesale price processes for particular types of steel plate. We use this to infer the share of the middleman's discounted profits that are due to markups paid by its retail customers, and the share due to price speculation. The latter measures the firm's success in forecasting steel prices and in timing its purchases in order to buy low and sell high'. The more successful the firm is in speculation (i.e. in strategically timing its purchases), the more serious are the potential biases that would result from failing to account for the endogeneity of the sampling process.

The Bias Of The Rsr Estimator And The Accuracy Of Some Alternatives

The Bias Of The Rsr Estimator And The Accuracy Of Some Alternatives written by William N. Goetzmann and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Assets (Accounting) categories.

This paper analyzes the implications of cross-sectional heteroskedasticity in repeat sales regression (RSR). RSR estimators are essentially geometric averages of individual asset returns because of the logarithmic transformation of price relatives. We show that the cross sectional variance of asset returns affects the magnitude of bias in the average return estimate for that period, while reducing the bias for the surrounding periods. It is not easy to use an approximation method to correct the bias problem. We suggest a maximum-likelihood alternative to the RSR that directly estimates index returns that are analogous to the RSR estimators but are arithmetic averages of individual returns. Simulations show that these estimators are robust to time-varying cross-sectional variance and may be more accurate than RSR and some alternative methods of RSR.

Semiparametric Estimation Of Instrumental Variable Models For Casual Effects

Semiparametric Estimation Of Instrumental Variable Models For Casual Effects written by Alberto Abadie and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Causation categories.

The Effects Of Random And Discrete Sampling When Estimating Continuous Time Diffusions

The Effects Of Random And Discrete Sampling When Estimating Continuous Time Diffusions written by Yacine Aït-Sahalia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Economics categories.

High-frequency financial data are not only discretely sampled in time but the time separating successive observations is often random. We analyze the consequences of this dual feature of the data when estimating a continuous-time model. In particular, we measure the additional effects of the randomness of the sampling intervals over and beyond those due to the discreteness of the data. We also examine the effect of simply ignoring the sampling randomness. We find that in many situations the randomness of the sampling has a larger impact than the discreteness of the data.