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Valuing Agroforestry Systems : Methods and Applications
Offers practical means for strengthening the economics and policy dimension of the agroforestry discipline. The applied economic methodologies encompass a wide variety of case studies including enterprise/farm budget models through Faustmann models, Policy Analysis Matrix, production function approach, risk assessment models, dynamic programming, linear programming, meta-modeling, contingent valuation, attribute-based choice experiments, econometric modeling, and institutional economic analysis. It is our belief that these methodologies help agroforestry students and professionals conduct rigorous assessment of economic and policy aspects of agroforestry systems and to produce less biased and more credible information. Furthermore, the economic and policy issues explored in the book – profitability, environmental benefits, risk reduction, household constraints, rural development, and institutional arrangements – are central to further agroforestry adoption in both tropical and temperate regions
Topics in dynamic model analysis : Advanced matrix methods and unit-root econometrics representation theorems
Classical econometrics - which plunges its roots in economic theory with simultaneous equations models (SEM) as offshoots - and time series econometrics - which stems from economic data with vector autoregr- sive (VAR) models as offsprings - scour, like the Janus's facing heads, the flowing of economic variables so as to bring to the fore their autonomous and non-autonomous dynamics. It is up to the so-called final form of a dy namic SEM, on the one hand, and to the so-called representation theorems of (unit-root) VAR models, on the other, to provide informative closed form expressions for the trajectories, or time paths, of the economic vari ables of interest.
Topics in Applied Macrodynamic Theory
This book presents topics in applied dynamic macrotheory for closed and open economies. The authors give an advanced treatment of macroeconomic topics such as the Phillips curve, forward and backward looking behavior, open economy macrodynamics, structural macroeconometric model building as well as the empirics of Keynesian oriented macro models. They start from the closed economy and consider open economies for fixed and flexible exchange rate systems with free international capital flows later on. The dynamics of open economies in the context of interacting two country models are treated as well. The macrofounded approach extends and integrates non-market-clearing traditions in macrodynamics. It is compared to New Keynesian approaches which are generally rigorously microfounded, but often neglect to study macroeconomic feedback mechanisms (that may be stabilizing or destabilizing).
Quantitative Finance
Written by accomplished teachers and researchers in the field, this book presents quantitative finance theory through applications to specific practical problems and comes with accompanying coding techniques in R and MATLAB, and some generic pseudo-algorithms to modern finance.
Quantitative Economic Policy : Essays in Honour of Andrew Hughes Hallett
Quantitative economic policy and econometrics were developed along with macroeconomics in the 1930s. Econometric techniques and models are still being used extensively in the business of forecasting and policy advice. In particular, policy simulations with econometric models have become standard tools for evaluating and designing macroeconomic stabilization policies. For instance, such studies provided important arguments for the popularization of the recent steps towards European integration such as the European Single Market, the European Monetary Union, and the Enlargement of the European Union. In this book, some recent advances in the theory and applications of quantitative economic policy are presented, with particular emphasis on fiscal and monetary policies in a European and global context. Andrew Hughes Hallett, a pioneer and major scientist in quantitative economic policy analysis, is being honoured by this volume, whose contributors are among his friends and former students.
New Introduction to Multiple Time Series Analysis
This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive, cointegrated,vector autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic simultaneous equations and state space models. Least squares, maximum likelihood and Bayesian methods are considered for estimating these models. Different procedures for model selection and model specification are treated and a wide range of tests and criteria for model checking are introduced. Causality analysis, impulse response analysis and innovation accounting are presented as tools for structural analysis. The book is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their tasks. It bridges the gap to the difficult technical literature on the topic.
Econometric Analysis of Count Data
The book provides graduate students and researchers with an up-to-date survey of statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. Proper count data probability models allow for rich inferences, both with respect to the stochastic count process that generated the data, and with respect to predicting the distribution of outcomes. The book starts with a presentation of the benchmark Poisson regression model. Alternative models address unobserved heterogeneity, state dependence, selectivity, endogeneity, underreporting, and clustered sampling. Testing and estimation is discussed from frequentist and Bayesian perspectives. Finally, applications are reviewed in fields such as economics, marketing, sociology, demography, and health sciences. The fifth edition contains several new topics, including copula functions, Poisson regression for non-counts, additional semi-parametric methods, and discrete factor models. Other sections have been reorganized, rewritten, and extended.
Computational intelligence in economics and finance ; Vol. II
Computational intelligence (CI), as an alternative to statistical and econometric approaches, has been applied to a wide range of economics and finance problems in recent years, for example to price forecasting and market efficiency. This book contains research ranging from applications in financial markets and business administration to various economics problems. Not only are empirical studies utilizing various CI algorithms presented, but so also are theoretical models based on computational methods. In addition to direct applications of computational intelligence, readers can also observe how these methods are combined with conventional analytical methods such as statistical and econometric models to yield preferred results.
Applied Econometrics with R
This is the first book on applied econometrics using the R system for statistical computing and graphics. It presents hands-on examples for a wide range of econometric models, from classical linear regression models for cross-section, time series or panel data and the common non-linear models of microeconometrics such as logit, probit and tobit models, to recent semiparametric extensions. In addition, it provides a chapter on programming, including simulations, optimization, and an introduction to R tools enabling reproducible econometric research.
