Recovering Individual Data In The Presence Of Group And Individual Effects

The ecological fallacy of relating variables on the group level, when the individual-level relationship is desired, can only be avoided by using individual-level data. This paper gives some conditions for occasions when individual-level data can successfully be recovered from grouped data. Such a recovery is illustrated using data on urban or rural residence and participation or not in the labor force as an example. The conditions are given in terms of the distinction between individual-and group-level effects of one variable on another. Recovering individual data, on the one hand, and the study of individual and group-level effects, on the other hand, epresent two separate areas of thought that have received considerable attention. Here a link is made between the two lines of development to facilitate the recovery of individual-level data. Some consequences of the models for research design and recovery of historical data are explored

Individual analysis of laterality data

Graphical and statistical analyses are presented that allow one to check for an individual subject whether the performance during a session is stable. whether the difference between the left and the right visual half-field is significant. and whether the performance is uniform over different sessions. Analyses are given for accuracy data and for latency data. Though the analyses are described for a visual half-field experiment, they can easily be adapted for other laterality tasks

Individual effects and dynamics in count data models

In this paper we examine the panel data estimation of dynamic models for count data that include correlated fixed effects and predetermined variables. Use of a linear feedback model ls proposed. The standard Poisson conditional maximum llkelihood estimator for non-dynamic models, which ls shown to be the same as the Poisson maximum llkelihood estimator in a model with individual specific constants, ls inconsistent when regressors are predetermined. A quasi-differenced GMM estimator ls consistent for the parameters in the dynamic model, but when series are highly persistent, there ls a problem of weak instrument bias. An estimator ls proposed that utilises pre-sample information of the dependent count variable, which is shown in Monte Carlo simulations to possess desirable small sample properties. The models and estimators are applied to data on US patents and R&D expenditure

Identification of individual demands from market data under uncertainty

We show that, even under incomplete markets, the equilibrium manifold identifies individual demands everywhere in their domains. Under partial observation of the manifold, we determine maximal subsets of the domains on which identification holds. For this, we assume conditions of smoothness, interiority and regularity. It is crucial that there be date-zero consumption. As a by-product, we develop some duality theory under incomplete markets

“Singling out individual inventors from patent data”

An increasing number of studies in recent years have sought to identify individual inventors from patent data. A variety of heuristics have been proposed for using the names and other information disclosed in patent documents to establish “who is who” in patents. This paper contributes to this literature by describing a methodology for identifying inventors using patents applied to the European Patent Office (EPO hereafter). As in much of this literature, we basically follow a three-step procedure: (1) the parsing stage, aimed at reducing the noise in the inventor’s name and other fields of the patent; (2) the matching stage, where name matching algorithms are used to group similar names; and (3) the filtering stage, where additional information and various scoring schemes are used to filter out these similarly-named inventors. The paper presents the results obtained by using the algorithms with the set of European inventors applying to the EPO over a long period of time.“Names game”, patent data, unique inventors, name matching algorithms. JEL classification:C8, J61, O31, O33, R0.

Cognitive Beamforming for Multiple Secondary Data Streams With Individual SNR Constraints

In this paper, we consider cognitive beamforming for multiple secondary data streams subject to individual signal-to-noise ratio (SNR) requirements for each secondary data stream. In such a cognitive radio system, the secondary user is permitted to use the spectrum allocated to the primary user as long as the caused interference at the primary receiver is tolerable. With both secondary SNR constraint and primary interference power constraint, we aim to minimize the secondary transmit power consumption. By exploiting the individual SNR requirements, we formulate this cognitive beamforming problem as an optimization problem on the Stiefel manifold. Both zero forcing beamforming (ZFB) and nonzero forcing beamforming (NFB) are considered. For the ZFB case, we derive a closed form beamforming solution. For the NFB case, we prove that the strong duality holds for the nonconvex primal problem and thus the optimal solution can be easily obtained by solving the dual problem. Finally, numerical results are presented to illustrate the performance of the proposed cognitive beamforming solutions.Comment: This is the longer version of a paper to appear in the IEEE Transactions on Signal Processin

Panel Data Models with Multiple Time-Varying Individual Effects

This paper considers a panel data model with time-varying individual effects. The data are assumed to contain a large number of cross-sectional units repeatedly observed over a fixed number of time periods. The model has a feature of the fixed-effects model in that the effects are assumed to be correlated with the regressors. The unobservable individual effects are assumed to have a factor structure. For consistent estimation of the model, it is important to estimate the true number of factors. We propose a generalized methods of moments procedure by which both the number of factors and the regression coefficients can be consistently estimated. Some important identification issues are also discussed. Our simulation results indicate that the proposed methods produce reliable estimates.panel data, time-varying individual effects, factor models