This book presents an unusual view on one of the most influential periods in world economic history: the Early Globalization. By this term, the notion that a process of genuine globalization took place in the Early Modern Era is defended. The authors propose that the canonical globalization-that of the nineteenth and early twentieth centuries-was preceded by a century-long increasing economic integration between continents that were non-existent before 1492. The economic aspects of the Early Globalization, like market integration, price co-movements and international silver circulation, were very important. Notwithstanding, other dimensions of human life, which were affected by unprecedented intercontinental contacts, including free and forced migrations, changes in tastes and consumption, etc. The Fruits of Globalisation deals with some of the most important issues among the former and the latter. The book combines approaches from different disciplines, including quantitative and non-quantitative economic history, econometrics, international trade and demography. Overall, the vision of the Early Globalisation offered in this book is less pessimistic than in mainstream literature on the period.

The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values. Exploring the advantages of this approach, State-Space Methods for Time Series Analysis: Theory, Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form. After discussing the formulation of the state-space model, the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction, describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model, and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here, and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables. Web Resource The authors' E4 MATLAB (R) toolbox offers all the computational procedures, administrative and analytical functions, and related materials for time series analysis. This flexible, powerful, and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work.

The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values. Exploring the advantages of this approach, State-Space Methods for Time Series Analysis: Theory, Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form. After discussing the formulation of the state-space model, the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction, describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model, and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here, and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables. Web Resource The authors' E4 MATLAB (R) toolbox offers all the computational procedures, administrative and analytical functions, and related materials for time series analysis. This flexible, powerful, and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work.

This book presents an unusual view on one of the most influential periods in world economic history: the Early Globalization. By this term, the notion that a process of genuine globalization took place in the Early Modern Era is defended. The authors propose that the canonical globalization-that of the nineteenth and early twentieth centuries-was preceded by a century-long increasing economic integration between continents that were non-existent before 1492. The economic aspects of the Early Globalization, like market integration, price co-movements and international silver circulation, were very important. Notwithstanding, other dimensions of human life, which were affected by unprecedented intercontinental contacts, including free and forced migrations, changes in tastes and consumption, etc. The Fruits of Globalisation deals with some of the most important issues among the former and the latter. The book combines approaches from different disciplines, including quantitative and non-quantitative economic history, econometrics, international trade and demography. Overall, the vision of the Early Globalisation offered in this book is less pessimistic than in mainstream literature on the period.