Susanne Rässler

A Non-Iterative Bayesian Approach to Statistical Matching

Data fusion or statistical matching techniques merge datasets from different survey samples to achieve a complete but artificial data file which contains all variables of interest. The merging of datasets is usually done on the basis of variables common to all files, but traditional methods implicitly assume conditional independence between the variables never jointly observed given the common variables. Therefore we suggest using model based approaches tackling the data fusion task by more flexible procedures. By means of suitable multiple imputation techniques, the identification problem which is inherent in statistical matching is reflected. Here a non-iterative Bayesian version of Rubins implicit regression model is presented and compared in a simulation study with imputations from a data augmentation algorithm as well as an iterative approach using chained equations.

Prognoserechnung – Einführung und Überblick

Der Wunsch des Menschen, in die Zukunft zu schauen, durfte so alt sein wie die Menschheit selbst. Nach dem Aufkommen der elektronischen Rechenanlagen war vor allem durch die populare Literatur der Eindruck erweckt worden, dass nunmehr ein neuer Weg zur Erfullung dieses uralten Menschheitstraumes gewiesen sei. Charakteristisch hierfur waren Redewendungen wie z.B. „Computer haben vorhergesagt, dass im Jahr 2000 ...“. Zunachst trat im Hinblick auf „die exakte Prognose“ durch computerunterstutzte Prognoseverfahren eine gewisse Ernuchterung ein. Bekannte Methoden – etwa in Verbindung mit Standardsoftware-Paketen in den Sektoren Absatz, Lagerhaltung und Finanzierung – haben hier zum Teil zu Enttauschungen gefuhrt. In jungerer Zeit wird Wissenschaftlern vorgeworfen, die katastrophale Finanzkrise nicht antizipiert zu haben. Diese kontern oft mit einem Schuss Resignation, indem sie das von Nassim Taleb eingefuhrte und inzwischen haufig benutzte Bild vom „Schwarzen Schwan“ bemuhen [10]; dies ist eine unerwartete Katastrophe, welche Menschen aus psychologischen Grunden aus ihrer Vorstellungswelt verdrangen. Schlimme Terrorattacken gehoren ebenso dazu wie die Zerstorung eines Kernkraftwerkes, der Zusammenbruch einer systemischen Bank oder ein groser Technologiesprung.

Alternative Approaches to Statistical Matching

Throughout this chapter we consider the matching problem as a problem of “file concatenation”, a term coined by Rubin (1986); see Figure 4.1. First the two files A and B are concatenated and then the missing values of each part are multiply imputed to reflect uncertainty about the missing data and the unknown association of the variables never jointly observed. Thus our task is again to impute the missing data of X in file A and the missing data of Y in file B. U obs file A denotes the variables Z and Y and in file B, Z and X, respectively. Basically, this is a classical imputation problem.

Zum Einfluss der Internationalität von Unternehmen auf den Shareholder Value – Eine empirische Untersuchung deutscher börsennotierter Aktiengesellschaften im Zeitraum 1990 bis 2006

Ist die Internationalitat von Unternehmen ein Erfolgsfaktor? Die Betrachtung der Historie dieser wissenschaftlichen Fragestellung zeigt, dass in der fruhen Phase der akademischen Auseinandersetzung kritische Uberlegungen bezuglich des Zusammenhangs von Internationalitat und Unternehmenserfolg mehr oder weniger implizit beiseite gewischt wurden: Internationale Unternehmen mussen erfolgreich sein, sonst waren sie nicht international tatig, so konnte man die zu dieser Zeit in der Wissenschaft latent vorherrschende Auffassung umschreiben. Insofern stelle die Internationalitat entweder einen Erfolgsfaktor im Sinn einer erfolgsverursachenden Bedingung oder zumindest einen zuverlassigen Indikator erfolgreicher Unternehmenstatigkeit dar.

Kulturschock in Deutschland? — Zeitlicher Verlauf und Leistungseffekte der kulturellen Anpassung asiatischer Führungskräfte in Deutschland

SummaryThe problem of cultural incompatibility between international firms’ expatriates and the foreign host country and its consequences for the working performance has been discussed intensively in the literature. Although a number of studies have been conducted on Western expatriates in Asia, studies on Asian expatriates located in European countries are rather rare. In this contribution determinants of expatriates’ adjustment to host country culture are analyzed by a survey of Asian expatriates in Germany. According to the concept of Black/Stephens and its modifications introduced by Selmer several dimensions of adjustment have been differentiated. For three of these four dimensions no U-curve development — as implied in theory — can be discovered. The performance of Asian expatriates in Germany seems to be influenced mainly by their work adjustment which tends to improve with time spent working in the host country.

Frequentist Theory of Statistical Matching

The objective of statistical matching techniques is the generation of a new data set that allows even more flexible analysis than each single data set. In particular, the associations between variables never jointly observed are specified in such a completed data set. In this chapter we show whether a statistically matched file may be analyzed as if it were a single sample.

Practical Applications of Statistical Matching

Much of the literature describing traditional approaches and techniques that are used in practice are working papers, technical or internal reports. Often they are difficult to obtain if available at all. Most of the reports or articles published are less theoretical; details about the final matching algorithms are often best explained in private talks or at conferences. No comprehensive work addressing new and recently used matching techniques is available. In this chapter we summarize and record the history of statistical matching techniques and briefly explain some of its first solutions. Different techniques that had and still have great importance for practical applications are then discussed in more detail. Often our information is based on unpublished reports supplied by experts practicing statistical matching. Thus we hope to fill a gap in the literature and explain what often is left to the reader’s imagination.

Empirical Evaluation of Alternative Approaches

In this chapter we consider a typical European statistical matching task where multiple categorical, continuous, and so-called semicontinuous variables concerning media and television behavior have been recorded in separate surveys in addition to the usual demographic and socioeconomic information. Our goal here is less to analyze or describe the relationship among variables in a meaningful way but to find out whether the procedures for imputing missing values preserve important features of marginal and joint distributions. We want to investigate the performance of the proposed alternative matching techniques discussed in Chapter 4 when applied to real data sets which typically do not follow simplifying assumptions. The validity of a matching technique is measured according to the four levels we introduced in Chapter 2. The data are provided from the television behavior panel run by the largest German market research company.1 This data set is matched regularly with the purchasing behavior panel; the procedure actually applied by the GfK is described in section 3.3.5.

Synopsis and Outlook

In Chapter 1 we point out that the statistical matching task may be viewed as a problem of nonresponse; more precisely, the missing information is regarded as missing at random because the missingness is induced by the study design of the separate samples. The missing data are due to unasked questions and the missingness mechanism is regarded as ignorable which in principle makes the application of conventional multiple imputation techniques obvious. However, contrary to the traditional missingness patterns, statistical matching is characterized by its identification problem. The association of the variables never jointly observed is unidentifiable and cannot be estimated by means of likelihood inference. Therefore prior information has to be embedded in the estimation process. Statistically matched files tend to display conditional independence between the variables only observed in separate files. We show that the validity of the traditional matching techniques concerning the preservation of the true association of the variables never jointly observed depends on the explanatory power of the common variables. Following an approach published by Rubin (1987) we propose the use of multiple imputation techniques using informative prior distributions to overcome the conditional independence assumption. By means of MI, sensitivity of the unconditional association of the (specific) variables not jointly observed can be displayed. In other words, different prior settings of conditional associations allow us to show the extent to which unconditional associations are determined by the common variables.