Tam Chun Yu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 141-147). / Abstracts in English and Chinese. / Abstract --- p.I / Acknowledgements --- p.III / Table of Contents --- p.IV / List of Tables --- p.V / List of Figures --- p.VI / Chapter Chapter 1. --- Introduction --- p.1 / Chapter Chapter 2. --- Literature Review --- p.5 / Chapter 2.1 --- Real Estate Literature --- p.5 / Chapter 2.2 --- Financial Literature --- p.6 / Chapter 2.3 --- Impulse Response --- p.10 / Chapter Chapter 3. --- Methodology --- p.12 / Chapter 3.1 --- Augmented Dickey Fuller Test --- p.12 / Chapter 3.2 --- GARCH Model --- p.14 / Chapter 3.3 --- VAR Model --- p.16 / Chapter Chapter 4. --- Data Description --- p.18 / Chapter Chapter 5. --- Empirical Results --- p.20 / Chapter 5.1 --- Overview for the Data Set --- p.21 / Chapter 5.2 --- ADF Test --- p.22 / Chapter 5.3 --- GARCH Model --- p.22 / Chapter 5.4 --- VAR Model --- p.24 / Chapter 5.5 --- Impulse Response (IR) --- p.34 / Chapter Chapter 6. --- Conclusion --- p.38 / Appendix 1. Variable Definition --- p.41 / Appendix 2. Tables --- p.44 / Appendix 3. Figures --- p.61 / Appendix 4. Comparison of IR for different model in full sample case --- p.93 / Appendix 5. Comparison of IR for different model in first sub period --- p.109 / Appendix 6. Comparison of IR for different model in second sub period --- p.125 / Bibliography --- p.141

by Moon-Tong Chan. / Thesis submitted in: December 1997. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 123-124). / Abstract also in Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Data Set --- p.8 / Chapter Chapter 2 --- The Box-Jenkins Time Series Models --- p.15 / Chapter 2.1 --- The White-noise Process --- p.16 / Chapter 2.2 --- Stationarity of Time Series --- p.17 / Chapter 2.3 --- Differencing --- p.19 / Chapter 2.4 --- Seasonal ARIMA Models - SARIMA Models --- p.20 / Chapter 2.5 --- Intervention Models --- p.22 / Chapter 2.6 --- The Three Phases of ARMA Procedure --- p.24 / Chapter Chapter 3 --- Seasonal ARMA Models with Several Mean Levels --- p.38 / Chapter 3.1 --- Review of Linear Models --- p.40 / Chapter 3.1.1 --- Method of Weighted Least Squares --- p.41 / Chapter 3.2 --- The Proposed Model --- p.41 / Chapter 3.2.1 --- The Weightings --- p.43 / Chapter 3.2.2 --- Selection of Submodels --- p.45 / Chapter 3.2.3 --- Estimation of Model (3.4) --- p.46 / Chapter 3.3 --- Model Adequacy Checking --- p.55 / Chapter 3.3.1 --- Checking of Independence of Residuals --- p.56 / Chapter 3.3.2 --- Checking of Normality of Residuals --- p.58 / Chapter 3.4 --- Forecasting --- p.62 / Chapter Chapter 4 --- Comparison --- p.77 / Chapter 4.1 --- Similarities and Differences Between the Two Models --- p.78 / Chapter 4.2 --- Model Comparative Criterion --- p.81 / Chapter 4.2.1 --- Model Fitting Comparison --- p.82 / Chapter 4.2.2 --- Model Forecasting Comparison --- p.83 / Chapter 4.3 --- Conclusion --- p.90 / Chapter 4.4 --- Generation of Predicted Hourly Calls --- p.91 / Chapter 4.5 --- Extension --- p.92 / Appendix A --- p.97 / Appendix B --- p.105 / Appendix C --- p.122 / References --- p.123

Les problèmes de sélection de modèles se posent couramment dans un grand nombre de domaines applicatifs tels que la compression de données ou le traitement du signal et de l'image. Un des outils les plus utilisés pour résoudre ces problèmes se présente sous la forme d'une quantité réelle à minimiser appelée critère d'information ou critère entropique pénalisé.<br /><br />La principale motivation de ce travail de thèse est de justifier l'utilisation d'un tel critère face à un problème de sélection de modèles typiquement issu d'un contexte de traitement du signal. La justification attendue se doit, elle, d'avoir un solide fondement mathématique. <br /><br />Nous abordons ainsi le problème classique de la détermination de l'ordre d'une autorégression. La régression gaussienne, permettant de détecter les harmoniques principales d'un signal bruité, est également abordée. Pour ces problèmes, nous donnons un critère dont l'utilisation est justifiée par la minimisation du coût résultant de l'estimation obtenue. Les chaînes de Markov multiples modélisent la plupart des signaux discrets, comme les séquences de lettres ou les niveaux de gris d'une image. Nous nous intéressons au problème de la détermination de l'ordre d'une telle chaîne. Dans la continuité de ce problème nous considérons celui, a priori éloigné, de l'estimation d'une densité par un histogramme. Dans ces deux domaines, nous justifions l'utilisation d'un critère par des notions de codage auxquelles nous appliquons une forme simple du principe de Minimum Description Length.<br /><br />Nous nous efforçons également, à travers ces différents domaines d'application, de présenter des méthodes alternatives d'utilisation des critères d'information. Ces méthodes, dites comparatives, présentent une complexité d'utilisation moindre que les méthodes rencontrées habituellement, tout en permettant une description précise du modèle.

In this paper, we have employed non-linear model reexamine real interest parity (RIP) of five European economies with respect to the US. We focus on using linear and nonlinear unit root tests to test real interest rate differentials (RIRD). And we add time trend in the logistic and exponential smooth transition regression models to monthly data. The results are as follows. First, the evidence for the full-sample is favorable using three traditional unit root tests and one powerful nonlinear unit root test. Almost all economics are support real interest parity. Second, we use nonlinear error correction model to find which factors influence on RIRD. There are three economics influenced by both domestic and foreign factors at the same time.

The objective of the present study is to forecast annual rainfall taking into account the periodicities and structure of the stochastic component. This study has six Chapters. Chapter 1 presents introduction to the problem and objectives of the study. Chapter 2 consists of review of literature. Chapter 3 deals with the model formulation and development. Chapter 4 gives an account of the application of the model. Chapter 5 presents results and discussions. Chapter 6 gives the conclusions drawn from the study. In this thesis the following model formulations are presented in order to achieve the objective. Fourier analysis model is used to identify periodicities that are present in the rainfall series.1 These periodic components are used to obtain discrotized ranges which is an essential input for the Fourier series model. Auto power regression model is developed for estimation of rainfall and hence to compute the first order residuals errlt The parameters of the model are estimated using genetic algorithm. The auto power regression model is of the form, ( Refer the PDF File for Formula) where αi and βi are parameters and M indicates modular value. Fourier series model is formulated and solved through genetic algorithm to estimate the parameters amplitude R, phase Φ and periodic frequency wj for the residual series errlt. The ranges for the parameters R, Φ and wj were obtained from Fourier analysis model. errl't= /µerrlt+ Σj Rcos(wjt+ Φ) Further, an integrated auto power regression and Fourier series model developed (with parameters of the model being known from the above analysis) to estimate new rainfall series Zesťt=Zµ Σ t αi(ZMi-t ) βi+µerrl+ Σj Rcos(wjt+ Φ) and the second order residuals, err2t is computed using, err2t = (zt –Zesťt) Thus, the periodicities are removed in the errlt series and the second order residuals err'2f obtained represents the stochastic component of the actual rainfall series. Auto regressive model is formulated to study the structure of the stochastic component err2t. The auto regressive model of order two AR(2) is found to fit well. The parameters of the AR(2) model were estimated using method of least squares. An exponential weighting function is developed to compute the weight considering weight as a function of AR{2) parameters. The product of weight and Gaussian white noise N(0, óerr2) is termed as weighted stochastic component. Also, drought analysis is performed considering annual (January to December) and summer monsoon (June to September) rainfall totals, to determine average drought interval (idrt) which is used in assigning signs to the random component of the forecasting model. In the final form of the forecasting model. Zest”t = Z µ Σ t αi(ZMi-t ) βi+µerrl+ Σj Rcos(wjt+ Φ) ± WT(Φ1, Φ2)N(0, óerr2) The weighted stochastic component is added or subtracted considering two criteria. Criterion I is used for all rainfall series except all-India series for which criterion II is used. The criteria also consider average drought interval Further, it can be seen that a ± sign is introduced to add or subtract the weighted stochastic component, albeit the stochastic component itself can either be positive or negative. The introduction of ± sign on the already signed value (instead of absolute value) is found to improve the forecast in the sense of obtaining more number of point rainfall estimates within 20 percent error. Incorporating significant periodicities, and weighted stochastic component along with average drought interval into the forecasting formulation is the main feature of the model. Thus, in the process of rainfall prediction, the genetic algorithm is used as an efficient tool in estimating optimal parameters of the auto power regression and the Fourier series models, without the use of an expensive nonlinear least square algorithm. The model application is demonstrated considering different annual rainfall series relating to IMD-Regions (RI...R5), all-india (AI), IMD-Subdivisions (S1...S29), Zones (Z1...Z10) and all-Karnataka (AK). The results of the proposed model are encouraging in providing improved forecasts. The model considers periodicity, average critical drought frequency and weighted stochastic component in forecasting the rainfall series. The model performed well in achieving success-rate of 70 percent with percentage error less than 20 percent in 4 out of 5 IMD Regions (R2 to R5), all-India, 17 out of 29 IMD Subdivisions (S1 to S5, S7 to S9, S18, S19, S21, S24 to S29) and all-Karnataka rainfall series. The model performance for Zones was not that-satisfactory as only 2 out of 10 Zones [Z1 and Z2) met the criterion. In a separate study, an effort was made to forecast annual rainfall using IMSL subroutine SPWF -which estimates Wiener forecast parameters. Monthly data is considered for the study. The Wiener parameters obtained were used to estimate monthly rainfall. The annual estimates obtained by simple aggregation of the monthly estimates compared extremely well with the actual annual rainfall values. A success rate of more than 80 percent with percentage error less than 10 percent is achieved in 4 out of 5 IMD Regions (R2 to R5), all-India, 18 out of 29 IMD Subdivisions (S1 to S8, S14, S18, S19, S22 to S24, S26 to S29) and all-Karnataka rainfall series. Whereas a success rate of 80 percent within 20 percent error is achieved in 4 out of 5 IMD Regions (except R1), all-India, 25 outof 29 IMD Subdivisions (except S10, S11, S12 and S17), all- Karnataka and 8 out of 10 Zones (except Z6 and Z8)(Please refer PDF File for Formulas)

Η παρούσα μελέτη εξετάζει τη δυναμική σχέση μεταξύ των τιμών των ζωοτροφών και παραγωγού, καταναλωτή για τέσσερα είδη κρέατος: μοσχάρι, χοιρινό, αρνί και κοτόπουλο. Η σχετική βιβλιογραφία δείχνει ότι πολλοί παράγοντες επιδρούν στις τιμές των αγροτικών προϊόντων αλλά οι τιμές των ζωοτροφών είναι ο κυριότερος. Αυτό συμβαίνει γιατί οι ζωοτροφές αποτελούν πρώτη ύλη για την παραγωγή κρέατος και κατ΄επέκταση θα επηρέασουν τις τιμές παραγωγού και καταναλωτή. Τα δεδομένα αποτελούνται από 279 μηνιαίες τιμές που εκτείνονται από τον Ιανουάριο 1990 έως τον Ιανουάριο 2013. Χρησιμοποιώντας Johansen cointegration tests, Granger causality tests και impulse response functions τα εμπειρικά αποτελέσματα επιβεβαιώνουν πως οι τιμές των ζωοτροφών, οι τιμές παραγωγού και οι τιμές καταναλωτή δεν είναι ανεξάρτητες μεταξύ τους. / The present paper studies the relationship among feed prices, producer prices and consumer prices of meat: beef, pork, poultry and lamb. The literature indicates that there are many factors which affect agricultural commodity prices but the feed prices are the main. This is why feed has a principal role in the production of meat and will affect producer and consumer prices. The data consists of 279 monthly observations extending from January 1990 to January 2013. Using Johansen cointegration tests, Granger causality tests and impulse response functions, the empirical findings confirm that feed prices, consumer prices and producer prices are interdependent.

The supervising institutions do not give to commercial banks indications what models have to be used for stress testing. This research was done in order to find out which mathematical/statistical models are and can be used in credit risk stress testing. Credit risk is one of the biggest financial risks that every bank faces. Stress testing is a tool of credit risk assessment that helps to estimate the consequences of the events that have really small probability to happen but if they occur, banks can have significant losses. This study determined that the most plausible event is adverse macroeconomic conditions. For this reason, models that include macroeconomic impact were presented. Vector autoregression and vector error correction model were tested using the empirical data received from Swedish central bank, Swedish statistics and Eurostat. For financial stability it is worth using vector autoregression or vector error correction model as they describe the macroeconomic environment in the most suitable way and they are appropriate for shock analysis by showing how the impact of any factor can change the whole system. Structure: introduction, main part (credit risk, methods and empirical analysis), publication, conclusions, references. Thesis consists of: 50 p. text without appendices, 13 pictures, 11 tables, 26 bibliographical entries. Appendices included. / Kredito įstaigų priežiūros institucijos nepateikia komerciniams bankams kokius metodus jie turėtų naudoti testavime nepalankiomis sąlygomis. Tiriamasis darbas buvo atliktas tuo tikslu, kad būtų išsiaiškinta kokie matematiniai ir statistiniai metodai yra ir gali būti naudojami kredito rizikos vertinime testuojant nepalankiomis sąlygomis. Kredito rizika yra viena iš didžiausių finansinių rizikų su kuria bankai susiduria. Testavimas nepalankiomis sąlygomis yra kredito rizikos vertinimo įrankis, padedantis nustatyti įvykių, kurių realizavimosi tikimybės yra mažos, tačiau jiems įvykus, bankai patirtų reikšmingus nuostolius, pasekmes. Šis tyrimas nustatė, jog labiausiai tikėtinas įvykis gali būti ypatingai nepalankios ekonominės sąlygos. Dėl šios priežasties darbe yra pristatyti metodai, kurie įvertina makroekonominių veiksnių įtaką. Vektorinė autoregresija ir vektorinis paklaidų korekcijos modelis buvo patikrinti naudojant Švedijos centrinio banko, Švedijos statistikos departamento ir Eurostat empirinius duomenis. Finansinio stabilumo įvertinimui vertėtų naudoti vektorinį autoregresijos ar vektorinį paklaidų korekcijos modelius, nes šie modeliai geriausiai aprašo ekonominę aplinką bei yra labai tinkami šokų analizei, kadangi įvertina bet kurio veiksnio įtaką visai sistemai. Struktūra: įvadas, pagrindinė dalis (kredito rizika, metodai ir empirinė analizė), publikacija, išvados, literatūros sąrašas. Tiriamasis darbas sudarytas iš: 50 psl. teksto be priedų, 13 paveikslų, 11... [toliau žr. visą tekstą]

Darbe „Akcijų kainų kintamumo analizė“ nagrinėjami ir lyginami Baltijos (Lietuvos, Latvijos, Estijos) bei Lotynų Amerikos (Meksikos, Venesuelos) šalių duomenys. Atliekama pasirinktų akcijų kainų grąžų analizė. Jai naudojami trijų metų kiekvienos dienos duomenys (akcijų kainos). Pirmoje darbo dalyje supažindinama su bendra prognozavimo metodų teorija, aprašomi skirtingi, dažnai literatūroje ir praktikoje sutinkami modeliai. Antrojoje dalyje aprašyti prognozavimo metodai taikomi realiems duomenims, t.y. pasirinktoms akcijoms. Prognozuojama akcijų kainų grąža, kuri po to yra palyginama su realia reikšme, apskaičiuojamos prognozavimo metodų paklaidos. Pagrindinis darbo tikslas – atlikti lyginamąją prognozavimo modelių analizę su pasirinktomis akcijomis ir atrinkti tuos metodus, kurie duoda geriausius rezultatus. Darbo tikslui įgyvendinti naudojama SAS statistinio paketo ekonometrikos ir laiko eilučių analizės posistemė SAS/ETS (Time Series Forecasting System). / Most of empirical surveys in macro and financial economics are based on time series analysis. In this work, data of Baltic and Latin America countries is being analyzed and compared. Analysis of stock price returns is presented using daily long term (three years) period data. In the first part of this work general forecasting theory is presented, also different methods, frequently met in the literature and practice, are described. In the second part, forecasting models are being applied for real data. We present results of forecasting stock returns comparing them with real values. Also a precision of forecasts is being calculated, which let us to decide about propriety of each model. Consequently, the aim of this work is to forecast returns of stock price by various time series models and to choose the best one. The analysis was made using SAS statistical package and its econometrics and Time Series Analysis System (SAS/ETS).

To achieve economic development, regional authorities have to address issues that relate to climate change, efficient information flow in the market place, and health care. This dissertation presents three essays on current issues of concern to economic development in Africa. Climate change is examined in terms of its effects on the Egyptian agricultural sector; transmission of world price to small scale growers is examined in Uganda; and the benefits of insecticide-treated bed nets use is examined in Africa. In essay I, to address the impact of climate change on the Egyptian agricultural sector under alternative population growth rates, water use and crop yield assumption; the Egyptian Agricultural Sector Model (EASM) is updated and expanded to improve hydrological modeling and used to portray agricultural activity and hydrological flow. The results indicate that climate change will cause damages (costs) to the Egyptian agricultural sector and these will increase over time. Egypt may reduce these future damages by controlling its population growth rate and using water conservation strategies. In essay II, I use vector autoregressive analysis to examine the transmissions of price information to Uganda coffee growers; using monthly coffee price data on retail, futures, farmgate and world prices from 1994 to 2010. Improved transmission of world prices to farmers may increase their decision making to obtain a better market price. Directed acyclic graphs reveal that there is a causal flow of information from the indicator price to the London futures price to the Uganda grower?s price in contemporaneous time. Forecast error variance decomposition indicates that at moving ahead 12 months, the uncertainty in Uganda grower price is attributable to the indicator price (world spot price), own price (farmgate), London future and Spain retail price in rank order. In essay III, the cost of malaria in children under five years and the use of insecticide treated bed nets is examined in the context of 18 countries in Africa. I examine the direct and indirect cost of malaria in children under five years and the benefit of investing in insecticide treated mosquito nets as a preventative strategy in 18 African countries. The results indicate that the use of mosquito treated nets reduces the number of malaria cases in children; and this can induce 0.5% reduction in outpatient treatment costs, 11% reduction in inpatient treatment costs, 11% reduction in productivity loss, and 15% reduction in disability adjusted life years (DALY) annually.

Humboldt-Univ., Diss.--Berlin, 2003. / Literaturverz. S. [205] - 212.