This thesis presents methodological contributions to the modeling of regimes in the time or space domain of economic data by introducing a number of algorithms from engineering applications and substantially modifying them so that can be used in economic applications. The objective is twofold: to estimate the parameters of such models, and to identify the corresponding boundaries between regimes. The models used belong to the class of Finite Mixture Models and their natural extensions for the case of dependent data, Hidden Markov Models (see McLachlan and Peel 2000). Mixture models are extremely useful in the modeling of heterogeneity in a cluster analysis context; the components of the mixtures, or the states, will correspond to the different latent groups, e.g. homogeneous regions such as the housing submarkets or regimes in the case of stock market returns. / The thesis discusses issues of alternative estimation algorithms that provide larger model flexibility in capturing the underlying data dynamics, and of procedures that allow the selection of the number of the regimes in the data. / The first part introduces a model of spatial association for housing markets, which is approached in the context of spatial heterogeneity. A Hedonic Price Index model is considered, i.e. a model where the price of the dwelling is determined by its structural and neighborhood characteristics. Remaining spatial heterogeneity is modeled as a Finite Mixture Model for the residuals of the Hedonic Index. The Finite Mixture Model is estimated using the Figueiredo and Jain (2002) approach. The overall ability of the model to identify spatial heterogeneity is evaluated through a set of simulations. The model was applied to Los Angeles County housing prices data for the year 2002. The statistically identified number of submarkets, after taking into account the dwellings' structural characteristics, are found to be considerably fewer than the ones imposed either by geographical or administrative boundaries, thus making it more suitable for mass assessment applications. / The second part of the thesis introduces a Duration Hidden Markov Model to represent regime switches in the stock market; the duration of each state of the Markov Chain is explicitly modeled as a random variable that depends on a set of exogenous variables. Therefore, the model not only allows the endogenous determination of the different regimes but also estimates the effect of the explanatory variables on the regimes' durations. The model is estimated on NYSE returns using the short-term interest rate and the interest rate spread as exogenous variables. The estimation results coincide with existing findings in the literature, in terms of regimes' characteristics, and are compatible with basic economic intuition, in terms of the effect of the exogenous variables on regimes' durations. / The final part of the thesis considers a Hidden Markov Model (HMM) approach in order to perform the task of detecting structural breaks, which are defined as the data points where the underlying Markov Chain switches from one state to another: A new methodology is proposed in order to estimate all aspects of the model: number of regimes, parameters of the model corresponding to each regime, and the locations of regime switches. One of the main advantages of the proposed methodology is that it allows for different model specifications across regimes. The performance of the overall procedure, denoted IMI by the initials of the component algorithms is validated by two sets of simulations: one in which only the parameters are permitted to differ across regimes, and one that also permits differences in the functional forms. The IMI method performs very well across all specifications in both sets of simulations.

Model selection criteria are used in many contexts in economics. The issue of determining an appropriate criterion, or alternative method, for model selection is a topic of much interest for applied econometricians. These criteria are used when formal testing methods are difficult due to a large number of models being compared, or when a sequential modelling strategy is being used. In econometrics, we are familiar with the use of model selection criteria for determining the order of an ARMA process and the number of dependent variable lags in Augmented Dickey-Fuller equations. The latter application is examined as an interesting example of the sensitivity of results to the choice of criterion. An application of model selection criteria to spline fitting is also considered, introducing a new, flexible, modelling strategy for technical progress in a production economy and for returns to scale in a resource economics context. In this latter context we have a system of estimating equations. Two of the criteria which are compared are the Cross-Validation score (CV) and the Generalized Cross- Validation Criterion (GCV), which until now have only had single equation context expressions. Multiple equation expressions for these criteria are introduced, and are used in the two applications. Comparison of the models selected by the different criteria in each context reveals that results can differ greatly with the choice of criterion. In the unit root test application, the choice of criterion influences the number of times the false hypothesis is not rejected. In the production economy and resource applications, measures of technical progress and returns to scale differ greatly, as do own and cross price elasticities, depending on which criterion is used for selecting the appropriate spline structure. An overview of the literature on model selection is given, with new expressions and interpretations for some model selection criteria, and historical notes.

Thesis (Ph. D.)--University of Iowa, 2007. / Supervisor: John Geweke. Includes bibliographical references (leaves 138-140).

Model selection criteria are used in many contexts in economics. The issue of determining an appropriate criterion, or alternative method, for model selection is a topic of much interest for applied econometricians. These criteria are used when formal testing methods are difficult due to a large number of models being compared, or when a sequential modelling strategy is being used. In econometrics, we are familiar with the use of model selection criteria for determining the order of an ARMA process and the number of dependent variable lags in Augmented Dickey-Fuller equations. The latter application is examined as an interesting example of the sensitivity of results to the choice of criterion. An application of model selection criteria to spline fitting is also considered, introducing a new, flexible, modelling strategy for technical progress in a production economy and for returns to scale in a resource economics context. In this latter context we have a system of estimating equations. Two of the criteria which are compared are the Cross-Validation score (CV) and the Generalized Cross- Validation Criterion (GCV), which until now have only had single equation context expressions. Multiple equation expressions for these criteria are introduced, and are used in the two applications. Comparison of the models selected by the different criteria in each context reveals that results can differ greatly with the choice of criterion. In the unit root test application, the choice of criterion influences the number of times the false hypothesis is not rejected. In the production economy and resource applications, measures of technical progress and returns to scale differ greatly, as do own and cross price elasticities, depending on which criterion is used for selecting the appropriate spline structure. An overview of the literature on model selection is given, with new expressions and interpretations for some model selection criteria, and historical notes. / Arts, Faculty of / Vancouver School of Economics / Graduate

No description available.

M.Com. (Econometrics) / The main purpose of this study is the combining of forecasts with special reference to major macroeconomic series of South Africa. The study is based on econometric principles and makes use of three macro-economic variables, forecasted with four forecasting techniques. The macroeconomic variables which have been selected are the consumer price index, consumer expenditure on durable and semi-durable products and real M3 money supply. Forecasts of these variables have been generated by applying the Box-Jenkins ARIMA technique, Holt's two parameter exponential smoothing, the regression approach and mUltiplicative decomposition. Subsequently, the results of each individual forecast are combined in order to determine if forecasting errors can be minimized. Traditionally, forecasting involves the identification and application of the best forecasting model. However, in the search for this unique model, it often happens that some important independent information contained in one of the other models, is discarded. To prevent this from happening, researchers have investigated the idea of combining forecasts. A number of researchers used the results from different techniques as inputs into the combination of forecasts. In spite of the differences in their conclusions, three basic principles have been identified in the combination of forecasts, namely: i The considered forecasts should represent the widest range of forecasting techniques possible. Inferior forecasts should be identified. Predictable errors should be modelled and incorporated into a new forecast series. Finally, a method of combining the selected forecasts needs to be chosen. The best way of selecting a m ethod is probably by experimenting to find the best fit over the historical data. Having generated individual forecasts, these are combined by considering the specifications of the three combination methods. The first combination method is the combination of forecasts via weighted averages. The use of weighted averages to combine forecasts allows consideration of the relative accuracy of the individual methods and of the covariances of forecast errors among the methods. Secondly, the combination of exponential smoothing and Box-Jenkins is considered. Past errors of each of the original forecasts are used to determine the weights to attach to the two original forecasts in forming the combined forecasts. Finally, the regression approach is used to combine individual forecasts. Granger en Ramanathan (1984) have shown that weights can be obtained by regressing actual values of the variables of interest on the individual forecasts, without including a constant and with the restriction that weights add up to one. The performance of combination relative to the individual forecasts have been tested, given that the efficiency criterion is the minimization of the mean square errors. The results of both the individual and the combined forecasting methods are acceptable. Although some of the methods prove to be more accurate than others, the conclusion can be made that reliable forecasts are generated by individual and combined forecasting methods. It is up to the researcher to decide whether he wants to use an individual or combined method since the difference, if any, in the root mean square percentage errors (RMSPE) are insignificantly small.

D.Com. (Econometrics) / The main aim with the thesis was to outline the use of regional econometric modelling as a technique for the modelling of the interdependency in the development regions of South Africa. In particular, an regional econometric model of Region H was constructed. There is no doubt that modelling is here to stay - as part of the analytical process used in scholarly studies and in applications, especially for making policy in both the public and privatedomain. Although macro-econometric modelling has been withus for sometime, the art of building large-scale· regional economic models is relatively new, especially in South Africa where such models have never been in use. The problem of forecasting regional economic activity has become an important component of regional research. The most frequently used forecasting techniques have been input-output model. A regional econometric model can be defined as a set of equations, sometimes highly simultaneous, describing the economic structure of a regional economy. The parameters of the equations are estimated economically largely by regression equations.

by Leung Ping Ngok. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 39). / ABSTRACT --- p.ii / TABLE OF CONTENTS --- p.iii / LIST OF ILLUSTRATIONS --- p.v / Chapter / Chapter I. --- INTRODUCTION --- p.1 / Chapter II. --- THE MODEL --- p.11 / Chapter III. --- THE ANALYSIS --- p.16 / Case 1 - Tariff t --- p.16 / Case 2 - Quota --- p.19 / Chapter i. --- The license is distributed among the two countries by a ratio λ whereas 0< λ<1 --- p.19 / Chapter ii. --- The license is distributed among the internal organizations --- p.28 / Chapter IV. --- DISCUSSION AND CONCLUSION --- p.34 / BIBLIOGRAPHY --- p.39

The main purpose of this research is to provide the commercial seafood industry of the Pacific Northwest information on preferences of restaurateurs, retailers, and wholesalers for whole albacore, low-value added albacore products (chunks, medallions, and steaks), albacore loins, and high-value added albacore products (hot smoked and lox). All of these products were categorized as non-traditional market forms of albacore products, except whole albacore. The empirical analysis was based on self explicated and conjoint analysis. The demand models for albacore products were estimated using weighted least squares. Profitability equations for albacore products were estimated using a two-limit Tobit model. From the self explicated section, it was found that the attributes of price, flavor, blood spots/bruising, and bleeding of whole albacore were considered highly important by respondents. From the conjoint analysis section, it was found that, as expected a priori, price had a statistical significant effect on the demand and profitability models for all albacore products. Other variables, such as location of the firm, type of firm, experience with tuna species, and ranking of albacore had statistical significant effects on the demand and profitability equations. Wholesalers, restaurateurs, and retailers agreed that quality is a major concern and will influence their preferences when purchasing albacore can products. Overall, the findings from this research provide guidance to the commercial seafood industry of the Pacific Northwest to enhance the markets for albacore products. / Graduation date: 1997

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