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This book introduces a new approach in the field of macroeconomic inventory studies: the use of multivariate statistics to evaluate long-term characteristics of inventory investments in developed countries. By analyzing a 44-year period series of annual inventory change in percentage of GDP in a set of OECD countries, disclosing their relationship to growth, industry structure and alternative uses of GDP (fixed capital investments, foreign trade and consumption), it fills a gap in the economic literature. It is generally accepted that inventories play an important role in all levels of the economy. However, while there is extensive literature on micro- (and even item-) level inventory problems, macroeconomic inventory studies are scarce. Both the long-term processes of inventory formation and their correlation with other macroeconomic factors provide interesting conclusions about economic changes and policies in our immediate past, and present important insights for the future.
Bringing together a collection of previously published work, this book provides a discussion of major considerations relating to the construction of econometric models that work well to explain economic phenomena, predict future outcomes and be useful for policy-making. Analytical relations between dynamic econometric structural models and empirical time series MVARMA, VAR, transfer function, and univariate ARIMA models are established with important application for model-checking and model construction. The theory and applications of these procedures to a variety of econometric modeling and forecasting problems as well as Bayesian and non-Bayesian testing, shrinkage estimation and forecasting procedures are also presented and applied. Finally, attention is focused on the effects of disaggregation on forecasting precision and the Marshallian Macroeconomic Model that features demand, supply and entry equations for major sectors of economies is analysed and described. This volume will prove invaluable to professionals, academics and students alike.
In this book on disequilibrium, growth and labor market dynamics we take predominantly a macroeconomic perspective. We present a working model that can easily be varied in different directions in order to subsume innovations in the literature on macroeconomics, old and new, and to contribute to important currently discussed macroeconomic issues. Our working model is set up in a way that there is a close relationship between our presented dynamic models and modern macro econometric models with disequilibrium both in the labor and the goods markets. One of our objectives is, therefore, to narrow the gap between theoretical and applied structural macrodynamic model building. We hope that the book will be a useful reference for all researchers, academic teachers and practitioners of macroeconomic and macro econometric model building who are interested in economic dynamics, independently of whether they use equilibrium or disequilibrium methods in their own research. We base this hope on the fact that our approach contains a number of unique features. The emphasis on the identification and analysis of the basic feedback mechanisms at work in modern macro economies. A detailed study of the partial as well as integrated dynamic interaction between these feedback mechanisms that consti tute the interdependence of markets and sectors of the modern macro economy. The rela tionship between the macroeconomic framework of our working model and the Walrasian, Non-Walrasian and New-Keynesian reformulations of macroeconomics.
This book shows the potential of entropy and information theory in forecasting, including both theoretical developments and empirical applications. The contents cover a great diversity of topics, such as the aggregation and combination of individual forecasts, the comparison of forecasting performance, and the debate concerning the tradeoff between complexity and accuracy. Analyses of forecasting uncertainty, robustness, and inconsistency are also included, as are proposals for new forecasting approaches. The proposed methods encompass a variety of time series techniques (e.g., ARIMA, VAR, state space models) as well as econometric methods and machine learning algorithms. The empirical contents include both simulated experiments and real-world applications focusing on GDP, M4-Competition series, confidence and industrial trend surveys, and stock exchange composite indices, among others. In summary, this collection provides an engaging insight into entropy applications for forecasting, offering an interesting overview of the current situation and suggesting possibilities for further research in this field.
Teknika: Jurnal Sains dan Teknologi Volume 17, Number 2, 2021
As large physical capital stock projects need long periods to be built, a time-to-build specification is incorporated in factor demand models. Time-to-build and adjustment costs dynamics are identified since by the first moving average dynamics, whereas by the latter autoregressive dynamics are induced. Empirical evidence for time-to-build is obtained from data from the Dutch construction industry and by the estimation result from the manufacturing industry of six OECD countries.