No description available.

No description available.

M.S.

No description available.

No description available.

No description available.

The effect of movements in the real exchange rate on agricultural prices and agricultural export sales is assessed based on the principle of Granger causality. An out-of-sample forecasting procedure is used to conduct tests for Granger causality from the exchange rate to agricultural prices and export sales. Technical time series issues such as stationarity, the method of lag-length selection, in sample versus out-of-sample tests for Granger causality, and long-range versus short-range forecasting are considered in relation to the outcome of Granger causality tests. Theoretical and empirical studies are reviewed which indicate the importance of working with stationary data series when testing for Granger causality. Differing methods of lag-length selection are found to affect the outcome of both in-sample and out-or-sample tests for Granger causality. The usual in-sample tests for Granger causality are compared to out-of-sample tests; the results of the comparison reveal that the in-sample tests do not in-general agree among themselves, nor do they agree with the out-of-sample tests' results. This indicates the importance of searching the model space for the best specification before conducting Granger causality tests. Long-range forecasts are compared to the I-step ahead forecasts used to test for Granger causality; these forecasts corroborate the out-of- sample tests for Granger causality in finding significant impacts from the exchange rate to agricultural export sales and agricultural prices. / Master of Science

M.S.

no abstract provided by author / Master of Science

This dissertation is a set of related papers on the application of the "principle of statistical adequacy" to single and multi-equation econometric models. The first chapter lays out the intended scope of the dissertation and defines the principle of statistical adequacy. The second chapter reviews the formulation of tests of statistical adequacy for multivariate models, and describes the implementation of these tests. The first approach that is discussed is to select particular functions of the variables involved that should be orthogonal under the null hypothesis of no misspecification, and the sample analog of these functions is used as a basis for constructing misspecification tests. As an extension of this idea, it is argued that viewing the model in explicit probabilistic terms provides a basis for developing a set of orthogonality conditions that can be tested in terms of all the probabilistic assumptions underlying the model. The formulation of misspecification tests via auxiliary regressions using general polynomial functions and the implementation of these tests via a menu-driven econometric modeling computer program is described. The third chapter reports the results of an empirical application of the principle of Statistical adequacy to the modeling of inflation/unemployment trade-offs for the U.S. Using a Statistically adequate “reduced-form" as the basis, a number of competing theoretical models are considered. The use of graphical techniques and formal misspecification tests in determining the adequacy of the statistical model are emphasized. It is found that none of the competing theoretical explanations of aggregate labor market behavior are acceptable in terms of the over-identifying restrictions imposed or their own statistical adequacy. The final chapter is an example of how one might proceed when a specification failures the criteria of statistical adequacy. For U.S. interest rates, it is shown that linear-homoskedastic autoregessions do not adequately account for the leptokurtosis and non-linear temporal dependence in the data. Using the evidence provided by preliminary data analysis as a guide, the Student’s t autoregressive model with dynamic heteroskedasticity is estimated for the log differences in three interest rate series. The estimation and misspecification testing results suggest that the STAR model adequately accounts for the probabilistic features of the data: bell-shape symmetry; leptokurtosis; first and second-order temporal dependence. In contrast, a number of other heteroskedastic specifications are estimated, and found to be statistically inadequate. / Ph. D.