CONVERGENCE OF THE G-7: A COINTEGRATION APPROACH

Income convergence among the G-7 countries was demonstrated using Theil's inequality (entropy) index. G-7 convergence was also found for three potential factors of influence on economic growth: government expenditure, investment expenditure, and industrial employment. Pairwise cointegration tests indicated that income inequality was cointegrated with the other three inequality measures for the time period of 1950-88. Finally, Johansen's I(2) multi-cointegration tests indicated that three of the four inequality measures (i.e. income, investment expenditure, and industrial employment) were cointegrated suggesting that there exists a long-run equilibrium between the inequality in income, investment expenditure, and industrial employment.Agricultural and Food Policy,

WHEN IS EXPENDITURE "EXOGENOUS" IN SEPARABLE DEMAND MODELS?

The separability hypothesis and expenditure as an exogenous variable in a system of conditional demands are analyzed. Expenditure cannot be weakly exogenous in a system of conditional demands specified as functions of the prices of the separable goods and total expenditure on those goods. Furthermore, expenditure is uncorrelated with the residuals of the conditional demand equations only when severe restrictions are satisfied. Therefore, expenditure will seldom be strictly exogenous. Econometric methods are presented for the consistent and efficient estimation of the unknown parameters when expenditures is correlated with the residuals and when it is not.Demand and Price Analysis,

Forecasting USAF JP-8 Fuel Needs

Oil is still one of the strategic energy resources for both the U.S. and the USAF today. Accurate oil prediction is important for the U.S. in order to improve the national strategy and the related budget concerns. Today, the U.S. is roughly importing 58% of its petroleum products. Moreover, in Fiscal Year (FY) 2007 the USAF total energy costs exceeded $6.9 billion. Aviation fuel accounted for approximately 81% of the total AF energy costs. Fluctuations in oil prices have huge impacts on the USAF’s JP-8 budgetary calculations. In order to handle this problem, the need for accurate forecasts arises. In this study, we forecast the USAF’s JP-8 consumption and costs for the next five year period. The study shows that JP-8 consumption figures will go on to follow the recent trend via Holt’s Linear Method. Also, the study shows that good short-term predictions can be obtained with more simple and easy-to-implement methods, versus complex ones. When we consider long-term forecasts, 5-years in this case, multiple regression outperforms ANN modeling within the specified forecast accuracy measures. Our results indicate that the USAF’s JP-8 cost for each of the next 5 years will be somewhere between 6.3 and 7.5 billion dollars

An Analysis of Forecasting Methods on Supply Discrepancy Reporting

The Department of Defense (DoD) tracks and records all cargo shipments as they move from one location to the next. Inevitably, there are mistakes that are made when dealing with these shipments. Currently the Air Force does not use any forecasting techniques to predict these shipping discrepancies, thus it has no way to prepare for them other than employing remedial measures after errors occur. The purpose of this research is to study the current Air Force shipping processes, specifically shipping discrepancies, and determine if any trends emerge. By examining historical shipment discrepancy data, a trend analysis was accomplished and from this data a relatively accurate forecast was developed. In the final analysis, it was concluded that three models most accurately forecasted the behavior of the discrepancy codes studied. These three models can be utilized in determining the root causes of these discrepancy trends. If employed, focused training events should reduce costs to the Air Force through cost avoidance through by circumventing lost time and resources normally expended correcting shipping errors

A chaos theory and nonlinear dynamics approach to the analysis of financial series : a comparative study of Athens and London stock markets

This dissertation presents an effort to implement nonlinear dynamic tools adapted from chaos theory in financial applications. Chaos theory might be useful in explaining the dynamics of financial markets, since chaotic models are capable of exhibiting behaviour similar to that observed in empirical financial data. In this context, the scope of this research is to provide an insight into the role that nonlinearities and, in particular, chaos theory may play in explaining the dynamics of financial markets. From a theoretical point of view, the basic features of chaos theory, as well as, the rationales for bringing chaos theory to the attention of financial researchers are discussed. Empirically, the fundamental issue of determining whether chaos can be observed in financial time series is addressed. Regarding the latter, empirical literature has been controversial. A quite exhaustive analysis of the existing literature is provided, revealing the inadequacies in terms of methodology and the testing framework adopted, so far. A new "multiple testing" methodology is developed combining methods and techniques from the fields of both Natural Sciences and the Economics, most of which have not been applied to financial data before. A serious effort has been made to fill, as much as possible, the gap which results from the lack of a proper statistical framework for the chaotic methods. To achieve this the bootstrap methodology is adopted. The empirical part of this work focuses on the comparison of two markets with different levels of maturity; the Athens Stock Exchange (ASE), an emerging market, and London Stock Exchange (LSE). Our aim is to determine whether structural differences exist in these markets in terms of chaotic dynamics. In the empirical level we find nonlinearities in both markets by the use of the BDS test. R/S analysis reveals fractality and long term memory for the ASE series only. Chaotic methods, such as the correlation dimension (and related methods and techniques) and the largest Lyapunov exponent estimation, cannot rule out a chaotic explanation for the ASE market, but no such indication could be found for the LSE market. Noise filtering by the SVD method does not alter these findings. Alternative techniques based on nonlinear nearest neighbour forecasting methods, such as the "piecewise polynomial approximation" and the "simplex" methods, support our aforementioned conclusion concerning the ASE series. In all, our results suggest that, although nonlinearities are present, chaos is not a widespread phenomenon in financial markets and it is more likely to exist in less developed markets such as the ASE. Even then, chaos is strongly mixed with noise and the existence of low-dimensional chaos is highly unlikely. Finally, short-term forecasts trying to exploit the dependencies found in both markets seem to be of no economic importance after accounting for transaction costs, a result which supports further our conclusions about the limited scope and practical implications of chaos in Finance

Criteria for Evaluation of Econometric Models

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91910/1/Kmenta-Criteria_Evaluation_Econometric_Models.pd