The determinants of local government performance have long been of interest, and have been empirically investigated in a number of recent studies. A general question in this area is: why have some local governments across the countries achieved high rates of performance whilst the others remained at lower levels? Evidence from the relevant literature indicates that some local governments or municipalities, particularly in developed countries, achieve very good performance. Others, in particular those from underdeveloped or developing, have very little or almost no performance.

This research empirically investigates the budgeting conditions in North Cyprus and evaluate how budgeting conditions vary from one municipality to the other in the five major municipalities; Lefkosa, Magusa, Girne, Guzelyurt and Iskele over the period 2004Q1 to 2012Q4. Two types of methodologies are conducted. First; it empirically investigates the determinants of local government deficit by using cointegration techniques. The Johansen co-integration and The Vector Error Correction model (VECM) are applied to test the effect of each element such as own-source revenue, grants, expenditures and debt services on the budget deficit. Second, the study test the convergence hypothesis if similar regions within the country converge to the same long-run steady state by considering five major Cypriot municipalities in the North Cyprus economy.

The results from the empirical analysis on budget deficit findings show that there is evidence of a long-run relationship between general deficit and its determinants. The evidence of causality is found from expenditure and debt to general deficit for Lefkosa municipality. There is unidirectional causality from grant and revenue through deficit for Magusa municipality. Third empirical analysis on convergence introduced that the municipalities with the lowest number of people per staff, commonly diverge from the group mean.

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.