In this book, we attempt to make a comparative evaluation of modern macroeconomic schools: Monetarism and New Classical School based on the Classical System that envisage automatic full employment or natural rate of unemployment (NRU) equilibrium (AFNE or ANRUE) vs. New Keynesian and Post-Keynesian Economics based on the Keynesian System which gives unemployment equilibrium (UNE) or non-automatic NRU equilibrium (NANRUE) due to insufficiency of aggregate demand.

In order to determine which school is relevant, first the basic assumptions of these systems are compared: i) rational vs. adjusted vs. heterogeneous expectations, ii) existence of perfect competition in all markets leading to flexibility of prices and wages vs. imperfect competition giving rise to rigidities, and iii) presence or lack of coordination between markets.

In the final phase of our evaluations the performance of the developed economies are surveyed to establish whether we meet with AFNE or ANRUE or else UNE or NANRUE; and whether policy prescriptions devised by respective schools solve or alleviate the problem at hand when implemented.

Our investigations point out that New Keynesian and Post-Keynesian schools are more relevant compared to Monetarism and New Classical School. The choice between New Keynesian and Post-Keynesian Economics, however, is more difficult to make although New Keynesian Economics seems more widespread than Post-Keynesian Economics. Objectively, Post-Keynesian assumptions seem more realistic; normatively, however, New Keynesian stands seems more fit to the present day move towards globalization.

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.