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Economists can rarely perform controlled experiments to generate data. Existing information in the form of real-life observations simply has to be utilized in the best possible way. Given this, it is advantageous to make use of the increasing availability and accessibility of combinations of time-series and cross-sectional data in the estimation of economic models. But such data call for a new methodology of estimation and hence for the development of new econometric models. This book proposes one such new model which introduces error components in a system of simultaneous equations to take into account the temporal and cross-sectional heterogeneity of panel data. After a substantial survey of panel data models, the newly proposed model is presented in detail and indirect estimations, full information and limited information estimations, and estimations with and without the assumption of normal distribution errors. These estimation methods are then applied using a computer to estimate a model of residential electricity demand using data on American households. The results are analysed both from an economic and from a statistical point of view.
This book had its conception in 1975in a friendly tavern near the School of Businessand PublicAdministration at the UniversityofMissouri-Columbia. Two of the authors (Fomby and Hill) were graduate students of the third (Johnson), and were (and are) concerned about teaching econometrics effectively at the graduate level. We decided then to write a book to serve as a comprehensive text for graduate econometrics. Generally, the material included in the bookand itsorganization have been governed by the question, " Howcould the subject be best presented in a graduate class?" For content, this has meant that we have tried to cover " all the bases " and yet have not attempted to be encyclopedic. The intended purpose has also affected the levelofmathematical rigor. We have tended to prove only those results that are basic and/or relatively straightforward. Proofs that would demand inordinant amounts of class time have simply been referenced. The book is intended for a two-semester course and paced to admit more extensive treatment of areas of specific interest to the instructor and students. We have great confidence in the ability, industry, and persistence of graduate students in ferreting out and understanding the omitted proofs and results. In the end, this is how one gains maturity and a fuller appreciation for the subject in any case. It is assumed that the readers of the book will have had an econometric methods course, using texts like J. Johnston's Econometric Methods, 2nd ed.
Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification. This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software’s SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the “how-tos” of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM.
This report portrays the results of experimental research on dynamic duopoly markets with demand inertia. Two methods of experimentation are studied: game-playing experiments where subjects interact spontaneously via computer terminals, and computer tournaments between strategies designed by subjects. The principal aim of this study is the understanding of boundedly rational decision making in the dynamic duopoly situation. 1. 1 Motivation The experiments examine a multistage duopoly game where prices in each period are the only decision variables. Sales depend on current prices and also on past sales (demand inertia). Applying the game-theoretic concept of subgame perfect equilibrium, the game is solved by backward induction. The result is a uniquely determined system of decision rules. However, we can hardly expect that human beings behave according to the equilibrium strategy of this game. It is unlikely that subjects are able to compute the equilibrium. And even if a subject is able to compute it, he might not make use of this knowledge. Only if he expects the others to behave according to the equilibrium, it is optimal for him to play the equilibrium strategy. We have evidence from several earlier experimental studies on oligopoly markets that, even in less complex oligopoly situations where the equilibrium solutions are very easy to compute, human behavior often is different from what is prescribed by normative theory. ! Normative theory is based on the concept of ideal rationality. However, human capabilities impose cognitive limits on rationality.
International commodity markets have traditionally attracted the attention of economists, econometricians, and policy makers especially in and following politically tumultuous times. For instance, the primary commodity price boom of 1973/74 and the subsequent period of highly volatile world market prices initiated increased research on commodity markets which quickly focused on possible price stabilization schemes, particularly on buffer stocks. Simultaneously, the issue clearly advanced in priority on the political agenda, such that the United Nations Conference on Trade and Development (UNCTAD) proposed an "Integrated Program for Commodities" (IPC) intended to stabilize the world market prices of ten so-called "core commodities"l (UNCTAD (1974, 1976a), Behrman (1979)). Many developing nations welcomed the IPC almost enthusiastically, but it did not receive more than lukewarm support by major industrialized countries, apparently due to the experience with some thirty international commodity agreements past World War II2. Critical evaluations have, among others, been presented by McNicol (1978), Gordon-Ashworth (1984), and Macbean & Nguyen (1987). The most detailed of these studies is Gordon-Ashworth's, who concludes that "on balance ... the performance of international commodity agreements has been too unreliable and their distributive effects too uneven to secure the development goals that have been set" (1984, p. 284)3. Consequently, the IPC turned out to be quite controversial a topic on the UNCTAD's 1976 meeting in Nairobi and has not been able to gain any impetus since. lThese were cocoa, coffee, copper, cotton, jute, rubber, sisal, sugar, tea, and tin.
In this book binary functions and their representation by implicants or implicates are described. In particular minimal representations by prime implicants or prime implicates are given. Such representations generalize the minimal representations of the usual Boolean functions. It is shown that implicants (implicates) of discrete functions may be constructed with the help of implicants (implicates) of binary functions. One substantial application is the description of the reliability structure of technical systems, another is the use of binary respectively discrete functions to classify objects which are described by the grades of certain attributes. Finally a class of Boolean algebras of practical importance (set algebras, indicator algebras, algebras of classes of propositions) are considered. The elements of such algebras have representations which are strongly connected with the representations of binary functions.
This volume consists of papers presented at the Fifth International Workshop on Computer Aided Scheduling of Public Transport, which was held in Montreal from August 19th to the 23rd, 1990. Since the first Workshop in Chicago in 1975 the field had matured considerably. In 1975, there were no presentations that described systems which had been implemented and used on a regular basis. By 1980, in Leeds, and certainly by 1983, in Montreal, several systems were in regular use. They were based on both heuristics and mathematical programming techniques. In 1990, there were more than one hundred transit companies using computer-aided scheduling tools in their regular operations. The scope of the Workshop was broadened in 1987, in Hamburg, so that topics related to scheduling may be introduced. We find, for example, in this book several papers on the technology related to the collection of data and/or the data bases required for scheduling and planning activities.
Project planning, scheduling, and control are regularly used in business and the service sector of an economy to accomplish outcomes with limited resources under critical time constraints. To aid in solving these problems, network-based planning methods have been developed that now exist in a wide variety of forms, cf. Elmaghraby (1977) and Moder et al. (1983). The so-called "classical" project networks, which are used in the network techniques CPM and PERT and which represent acyclic weighted directed graphs, are able to describe only projects whose evolution in time is uniquely specified in advance. Here every event of the project is realized exactly once during a single project execution and it is not possible to return to activities previously carried out (that is, no feedback is permitted). Many practical projects, however, do not meet those conditions. Consider, for example, a production process where some parts produced by a machine may be poorly manufactured. If an inspection shows that a part does not conform to certain specifications, it must be repaired or replaced by a new item. This means that we have to return to a preceding stage of the production process. In other words, there is feedback. Note that the result of the inspection is that a certain percentage of the parts tested do not conform. That is, there is a positive probability (strictly less than 1) that any part is defective.
In some manufacturing systems significant setups are required to change production from one type of products to another. The setups render the manufacturing system inflexible as for reacting to changes in demand patterns, hence inventories must be maintained to guarantee an acceptable customer service. In this environment, production scheduling faces a number of problems, and this work deals with mathematical models to support the scheduling decisions. Some more background and motivation is given in the following sections, as well as in a case description in Section 1. 3. The synopsis in Section 1. 4 outlines the topics of the work. 1. 1 Motivation of the Planning Problem Consider the production of metal sheets in a rolling mill. If the width of the next type of sheets is greater than the width of the preceding type, then the roll needs a setup: during the rolling process the edges of a sheet cause grooves on the rolls' surface, thus, the surface must be polished if a greater width is run next. Sheets with a smaller width can be run directly, without a setup. Another example in which setups are sequence dependent is a line where cars are sprayed: if the color of the paint changes, the cleaning of the tools requires a setup depending on the sequence of the colors. Only a small setup may be needed for changing from a light to a dark color, but a thorough cleaning of the tools is 2 CHAPTER 1.