This book is about economic growth and development through a new way of analysis. It is a research that explains many phenomena seen in the growth process such as productivity, economic stagnation, under-investment, ineffectiveness of foreign aid, wage trends, factor flows and concentrations, technology adoption, and convergence by “relational analysis”.

Relational analysis is a general alternative analysis that focuses on the relationships between factors and uses their vital consequences to extract knowledge rather than ignore them artificially and fatally. It emphasizes that there are important interrelationships between factors such as complementarity, substitution, embodiment, directing, input-output, and facilitation. Taking into account the relations enable to get more complete and accurate answers to important questions, especially in applied works that require real predictions such as forecasting and policy formulation. The analysis can be fruitfully applied in any field, and as the growth analysis shows it fundamentally enriches the ideas, and protect from misleading assumptions.

In the book the readers will find:

– Origins and alternative definitions of complementarity and substitutions,

– Eleven implications of these relationships for economic growth and development,

– Many explanations and references for inferences regarding growth accounting, poverty traps and thresholds, multiple eqilibria, obsolescence, financial development, growth accelerations, imitations and barriers, and empirical anaysis.

The book is for anyone looking for an alternative way to learn about the basics of the growth process. Researchers, students, and staffs at universities and research institutions and other social scientists in government, business, international organizations should greatly benefit from reading the book.

Financial time series including high frequency structures like jumps, spikes and stochastic volatility are usually modeled in an ad-hoc manner by stochastic differential equations together with Levy processes. Estimating the parameters and determining the jump size distributions do not have a precise and universally accepted method. Under these circumstances, complexities and confusions usually arise. This book, approaches to this issue from a very different angle through introducing an autocorrelation one process together with finite number of Fourier series terms. Introduction of Fourier series to estimate the dynamics of the process is not done in an ad-hoc manner or as done before in dealing with seasonality. Here the moving average is transformed to a “moving and fluctuating” average by the help of Fourier series. Instead of adding jump structures to the model which makes the parameter estimation quite cumbersome, our model in discrete time can easily be transformed to a well-known mean reverting continuous time process. Moreover, our alternative model turned out to be a quite powerful and accurate forecasting technique.