Refine
Has Fulltext
- yes (36)
Is part of the Bibliography
- yes (36)
Year of publication
Document Type
- Doctoral Thesis (36) (remove)
Language
- English (36) (remove)
Keywords
- Geldpolitik (7)
- Makroökonomie (5)
- Monetary Policy (4)
- Devisenmarkt (3)
- Einkommensverteilung (3)
- Globalisierung (3)
- Mergers and Acquisitions (3)
- Notenbank (3)
- Ungleichheit (3)
- Währungsunion (3)
Institute
- Volkswirtschaftliches Institut (36) (remove)
Sonstige beteiligte Institutionen
ResearcherID
- B-4606-2017 (1)
This thesis is about composite-based structural equation modeling. Structural equation modeling in general can be used to model both theoretical concepts and their relations to one another. In traditional factor-based structural equation modeling, these theoretical concepts are modeled as common factors, i.e., as latent variables which explain the covariance structure of their observed variables. In contrast, in composite-based structural equation modeling, the theoretical concepts can be modeled both as common factors and as composites, i.e., as linear combinations of observed variables that convey all the information between their observed variables and all other variables in the model. This thesis presents some methodological advancements in the field of composite-based structural equation modeling. In all, this thesis is made up of seven chapters. Chapter 1 provides an overview of the underlying model, as well as explicating the meaning of the term composite-based structural equation modeling. Chapter 2 gives guidelines on how to perform Monte Carlo simulations in the statistic software R using the package “cSEM” with various estimators in the context of composite-based structural equation modeling. These guidelines are illustrated by an example simulation study that investigates the finite sample behavior of partial least squares path modeling (PLS-PM) and consistent partial least squares (PLSc) estimates, particularly regarding the consequences of sample correlations between measurement errors on statistical inference. The third Chapter presents estimators of composite-based structural equation modeling that are robust in responding to outlier distortion. For this purpose, estimators of composite-based structural equation modeling, PLS-PM and PLSc, are adapted. Unlike the original estimators, these adjustments can avoid distortion that could arise from random outliers in samples, as is demonstrated through a simulation study. Chapter 4 presents an approach to performing predictions based on models estimated with ordinal partial least squares and ordinal consistent partial least squares. Here, the observed variables lie on an ordinal categorical scale which is explicitly taken into account in both estimation and prediction. The prediction performance is evaluated by means of a simulation study. In addition, the chapter gives guidelines on how to perform such predictions using the R package “cSEM”. This is demonstrated by means of an empirical example. Chapter 5 introduces confirmatory composite analysis (CCA) for research in “Human Development”. Using CCA, composite models can be estimated and assessed. This chapter uses the Henseler-Ogasawara specification for composite models, allowing, for example, the maximum likelihood method to be used for parameter estimation. Since the maximum likelihood estimator based on the Henseler-Ogasawara specification has limitations, Chapter 6 presents another specification of the composite model by means of which composite models can be estimated with the maximum likelihood method. The results of this maximum likelihood estimator are compared with those of PLS-PM, thus showing that this maximum likelihood estimator gives valid results even in finite samples. The last chapter, Chapter 7, gives an overview of the development and different strands of composite-based structural equation modeling. Additionally, here I examine the contribution the previous chapters make to the wider distribution of composite-based structural equation modeling.
Within three self-contained studies, this dissertation studies the impact and interactions between different macroeconomic policy measures in the context of financial markets empirically and quantitatively. The first study of this dissertation sheds light on the financial market effects of unconventional central bank asset purchase programs in the Eurozone, in particular sovereign bond asset purchase programs. The second study quantifies the direct implications of unconventional monetary policy on decisions by German public debt management regarding the maturity structure of gross issuance. The third study provides novel evidence on the role of private credit markets in the propagation of public spending toward private consumption in the U.S. economy. Across these three studies a set of different time-series econometric methods is applied including error correction models and event study frameworks to analyze contemporaneous interactions in financial and macroeconomic data in the context of unconventional monetary policy, as well as vector auto regressions (VARs) and local projections to trace the dynamic consequences of macroeconomic policies over time.
The contribution of this dissertation is to empirically analyze the link between income distribution, sectoral financial balances, and the current account. Firstly, it examines the relationship between the personal and the functional income distribution which may have rather different implications for aggregate demand and the current account. Secondly, it analyzes the importance of different sectors of the economy for current account balances and tests whether households are able to fully pierce the institutional veils of the corporate and the government sector. Thirdly, it investigates how changes in the personal and the functional income distribution affect the saving and investment decisions of the household and the corporate sector, and hence the current account. Finally, it shows how different growth regimes are linked to different patterns of personal and functional income distribution, and how differences in wage bargaining institutions contribute to explaining these different patterns of income distribution.
Structural equation modeling (SEM) has been used and developed for decades across various domains and research fields such as, among others, psychology, sociology, and business research. Although no unique definition exists, SEM is best understood as the entirety of a set of related theories, mathematical models, methods, algorithms, and terminologies related to analyzing the relationships between theoretical entities -- so-called concepts --, their statistical representations -- referred to as constructs --, and observables -- usually called indicators, items or manifest variables.
This thesis is concerned with aspects of a particular strain of research within SEM -- namely, composite-based SEM. Composite-based SEM is defined as SEM involving linear compounds, i.e., linear combinations of observables when estimating parameters of interest.
The content of the thesis is based on a working paper (Chapter 2), a published refereed journal article (Chapter 3), a working paper that is, at the time of submission of this thesis, under review for publication (Chapter 4), and a steadily growing documentation that I am writing for the R package cSEM (Chapter 5). The cSEM package -- written by myself and my former colleague at the University of Wuerzburg, Florian Schuberth -- provides functions to estimate, analyze, assess, and test nonlinear, hierarchical and multigroup structural equation models using composite-based approaches and procedures.
In Chapter 1, I briefly discuss some of the key SEM terminology.
Chapter 2 is based on a working paper to be submitted to the Journal of Business Research titled “Assessing overall model fit of composite models in structural equation modeling”. The article is concerned with the topic of overall model fit assessment of the composite model. Three main contributions to the literature are made. First, we discuss the concept of model fit in SEM in general and composite-based SEM in particular. Second, we review common fit indices and explain if and how they can be applied to assess composite models. Third, we show that, if used for overall model fit assessment, the root mean square outer residual covariance (RMS_theta) is identical to another well-known index called the standardized root mean square residual (SRMR).
Chapter 3 is based on a journal article published in Internet Research called “Measurement error correlation within blocks of indicators in consistent partial least squares: Issues and remedies”. The article enhances consistent partial least squares (PLSc) to yield consistent parameter estimates for population models whose indicator blocks contain a subset of correlated measurement errors. This is achieved by modifying the correction for attenuation as originally applied by PLSc to include a priori assumptions on the structure of the measurement error correlations within blocks of indicators. To assess the efficacy of the modification, a Monte Carlo simulation is conducted. The paper is joint work with Florian Schuberth and Theo Dijkstra.
Chapter 4 is based on a journal article under review for publication in Industrial Management & Data Systems called “Estimating and testing second-order constructs using PLS-PM: the case of composites of composites”. The purpose of this article is threefold: (i) evaluate and compare common approaches to estimate models containing second-order constructs modeled as composites of composites, (ii) provide and statistically assess a two-step testing procedure to test the overall model fit of such models, and (iii) formulate recommendation for practitioners based on our findings. Moreover, a Monte Carlo simulation to compare the approaches in terms of Fisher consistency, estimated bias, and RMSE is conducted. The paper is joint work with Florian Schuberth and Jörg Henseler.
This thesis contributes to the understanding of the labor market effects of international trade, with a focus on the effects on wage and earnings inequality. The thesis draws on high-quality micro data and applies modern econometric techniques and theoretical concepts to improve our understanding of the distributional effects of international trade. The thesis focuses on the effects in Germany and the USA.
This dissertation deals with composite-based methods for structural equation models with latent variables and their enhancement. It comprises five chapters. Besides a brief introduction in the first chapter, the remaining chapters consisting of four essays cover the results of my PhD studies.Two of the essays have already been published in an international journal.
The first essay considers an alternative way of construct modeling in structural equation modeling.While in social and behavioral sciences theoretical constructs are typically modeled as common factors, in other sciences the common factor model is an inadequate way construct modeling due to its assumptions. This essay introduces the confirmatory composite analysis (CCA) analogous to confirmatory factor analysis (CFA). In contrast to CFA, CCA models theoretical constructs as composites instead of common factors. Besides the theoretical presentation of CCA and its assumptions, a Monte Carlo simulation is conducted which demonstrates that misspecifications of the composite model can be detected by the introduced test for overall model fit.
The second essay rises the question of how parameter differences can be assessed in the framework of partial least squares path modeling. Since the standard errors of the estimated parameters have no analytical closed-form, the t- and F-test known from regression analysis cannot be directly used to test for parameter differences. However, bootstrapping provides a solution to this problem. It can be employed to construct confidence intervals for the estimated parameter differences, which can be used for making inferences about the parameter difference in the population. To guide practitioners, guidelines were developed and demonstrated by means of empirical examples.
The third essay answers the question of how ordinal categorical indicators can be dealt with in partial least squares path modeling. A new consistent estimator is developed which combines the polychoric correlation and partial least squares path modeling to appropriately deal with the qualitative character of ordinal categorical indicators. The new estimator named ordinal consistent partial least squares combines consistent partial least squares with ordinal partial least squares. Besides its derivation, a Monte Carlo simulation is conducted which shows that the new estimator performs well in finite samples. Moreover, for illustration, an empirical example is estimated by ordinal consistent partial least squares.
The last essay introduces a new consistent estimator for polynomial factor models.
Similarly to consistent partial least squares, weights are determined to build stand-ins for the latent variables, however a non-iterative approach is used.
A Monte Carlo simulation shows that the new estimator behaves well in finite samples.
The present thesis analyzes whether and - if so - under which conditions mergers result in merger-specific efficiency gains. The analysis concentrates on manufacturing firms in Europe that participate in horizontal mergers as either buyer or target in the years 2005 to 2014.
The result of the present study is that mergers are idiosyncratic processes. Thus, the possibilities to define general conditions that predict merger-specific efficiency gains are limited.
However, the results of the present study indicate that efficiency gains are possible as a direct consequence of a merger. Efficiency changes can be measured by a Total Factor Productivity (TFP) approach. Significant merger-specific efficiency gains are more likely for targets than for buyers. Moreover, mergers of firms that mainly operate in the same segment are likely to generate efficiency losses. Efficiency gains most likely result from reductions in material and labor costs, especially on a short- and mid-term perspective. The analysis of conditions that predict efficiency gains indicates that firm that announce the merger themselves are capable to generate efficiency gains in a short- and mid-term perspective. Furthermore, buyers that are mid-sized firms are more likely to generate efficiency gains than small or large buyers. Results also indicate that capital intense firms are likely to generate efficiency gains after a merger.
The present study is structured as follows.
Chapter 1 motivates the analysis of merger-specific efficiency gains. The definition of conditions that reasonably likely predict when and to which extent mergers will result in merger-specific efficiency gains, would improve the merger approval or denial process.
Chapter 2 gives a literature review of some relevant empirical studies that analyzed merger-specific efficiency gains. None of the empirical studies have analyzed horizontal mergers of European firms in the manufacturing sector in the years 2005 to 2014. Thus, the present study contributes to the existing literature by analyzing efficiency gains from those mergers.
Chapter 3 focuses on the identification of mergers. The merger term is defined according to the EC Merger Regulation and the Horizontal Merger Guidelines. The definition and the requirements of mergers according to legislation provides the framework of merger identification.
Chapter 4 concentrates on the efficiency measurement methodology. Most empirical studies apply a Total Factor Productivity (TFP) approach to estimate efficiency. The TFP approach uses linear regression in combination with a control function approach. The estimation of coefficients is done by a General Method of Moments approach.
The resulting efficiency estimates are used in the analysis of merger-specific efficiency gains in chapter 5. This analysis is done separately for buyers and targets by applying a Difference-In-Difference (DID) approach.
Chapter 6 concentrates on an alternative approach to estimate efficiency, that is a Stochastic Frontier Analysis (SFA) approach. Comparable to the TFP approach, the SFA approach is a stochastic efficiency estimation methodology. In contrast to TFP, SFA estimates the production function as a frontier function instead of an average function. The frontier function allows to estimate efficiency in percent.
Chapter 7 analyses the impact of different merger- and firm-specific characteristics on efficiency changes of buyers and targets. The analysis is based on a multiple regression, which is applied for short-, mid- and long-term efficiency changes of buyers and targets.
Chapter 8 concludes.
This dissertation consists of three contributions. Each addresses one specific aspect of intergenerational income mobility and is intended to be a stand-alone analysis. All chapters use comparable data for Germany and the United States to conduct country comparisons. As there are usually a large number of studies available for the United States, this approach is useful for comparing the empirical results to the existing literature.
The first part conducts a direct country comparison of the structure and extent of intergenerational income mobility in Germany and the United States. In line with existing results, the estimated intergenerational income mobility of 0.49 in the United States is significantly higher than that of 0.31 in Germany. While the results for the intergenerational rank mobility are relatively similar, the level of intergenerational income share mobility is higher in the United States than in Germany. There are no significant indications of a nonlinear run of intergenerational income elasticity. A final decomposition of intergenerational income inequality shows both greater income mobility and stronger progressive income growth for Germany compared to the United States. Overall, no clear ranking of the two countries can be identified. To conclude, several economic policy recommendations to increase intergenerational income mobility in Germany are discussed.
The second part examines the transmission channels of intergenerational income persistence in Germany and the United States. In principle, there are two ways in which well-off families may influence the adult incomes of their children: first through direct investments in their children's human capital (investment effect ), and second through the indirect transmission of human capital from parents to children (endowment effect ). In order to disentangle these two effects, a descriptive as well as a structural decomposition method are utilized. The results suggest that the investment effect and the endowment effect each account for approximately half of the estimated intergenerational income elasticity in Germany, while the investment effect is substantially more influential in the United States with a share of around 70 percent. With regard to economic policy, these results imply that equality of opportunity for children born to poor parents cannot be reached by the supply of financial means alone. Conversely, an efficient policy must additionally substitute for the missing direct transmission of human capital within socio-economically weak families.
The third part explicitly focuses on the intergenerational income mobility among daughters. The restriction to men is commonly made in the empirical literature due to women‘s lower labor market participation. While most men work full-time, the majority of (married) women still work only part-time or not at all. Especially with the occurrence of assortative mating, daughters from well-off families are likely to marry rich men and might decide to reduce their labor supply as a result. Thus, the individual labor income of a daughter might not be a good indicator for her actual economic status. The baseline regression analysis shows a higher intergenerational income elasticity in Germany and a lower intergenerational income elasticity in the United States for women as compared to men. However, a separation by marital status reveals that in both countries unmarried women exhibit a higher intergenerational income elasticity than unmarried men, while married women feature a lower intergenerational income elasticity than married men. The reason for the lower mobility of unmarried women turns out to be a stronger human capital transmission from fathers to daughters than to sons. The higher mobility of married women is driven by a weaker human capital transmission and a higher labor supply elasticity with respect to spousal income for women as compared to men. In order to further study the effects of assortative mating, the subsample of married children is analyzed by different types of income. It shows that the estimated intergenerational income elasticity of children's household incomes is even higher than that of their individual incomes. This can be seen as an indication for strong assortative mating. If household income is interpreted as a measure of children‘s actual economic welfare, there are barely any differences between sons and daughters. The intergenerational income elasticity of spousal income with respect to parental income is again relatively high, which in turn supports the hypothesis of strong assortative mating. The elasticity of the sons-in-law with respect to their fathers-in-law in Germany is even higher than that of the sons with respect to their own fathers.
Economists (should) care about regions! On the one hand this is true because macroeconomic shocks have vastly different effects across regions. The pressing topics of robotization
and artificial intelligence, Brexit, or U.S. tariffs will affect Würzburg differently than Berlin,
implying varying interests among its population, firms and politicians. On the other hand,
shocks in individual regions, such as inventions, bankruptcies or the attraction of a major
plant can, through trade and input-output linkages, magnify to aggregate effects of macroe-
conomic importance. Yet, regional heterogeneities in Germany and the complicated network
of linkages that connect regions are still not well documented nor understood. A fact that
is especially true for local labor markets that are of core interest to regional policy makers
and that also feature substantial heterogeneity.
This thesis provides a thorough quantification of such heterogeneities and an in-depth analysis of the sources and mechanisms that drive these differences.
The main subject of this dissertation is the analysis of the impact of the creation of the Eurozone on its member countries. This analysis comprises two studies that analyze this research agenda from different perspectives.
The first study compares the monetary policy of the ECB with the respective monetary policy of selected central banks of the European Monetary System (EMS). More precisely, conditional on aggregate demand and supply shocks, are there differences in the systematic central bank reaction function of the ECB and the four most important central banks of the EMS (Germany, France, Italy and Spain).
The second study analyzes the built-up of internal and external imbalances in Spain, i.e., on the housing market and in the current account, during the run-up to the financial crisis in 2007/08. The analysis differentiates between domestic Spain-specific factors and foreign Eurozone-factors that led to the macroeconomic imbalances.
The third and last study develops a price-theoretic credit supply model. In order to validate the model empirically, a credit market is estimated on the basis of data from the German credit market for enterprises. Finally, the results from the empirical exercise are compared to the predictions of the theoretic model.
Methodologically, all studies draw heavily on time series methods such as (multi-country) vector autoregressions (VARs) and time series regressions.