This paper uses a factor-augmented vector autoregressive model to examine the impact of monetary policy shocks on housing prices across metropolitan and micropolitan regions. To simultaneously estimate the model parameters and unobserved factors we rely on Bayesian estimation and inference. Policy shocks are identified using high-frequency suprises around policy announcements as an external instrument. Impulse reponse functions reveal differences in regional housing price responses, which in some cases are substantial. The heterogeneity in policy responses is found to be significantly related to local regulatory environments and housing supply elasticities. Moreover, housing prices responses tend to be similar within states and adjacent regions in neighboring states. / Series: Working Papers in Regional Science

The epilepsy is not a recent phenomenon, even its has being approached and Inves- tigated, this area still demands several researches and it is far away from being totally explained. The obtaining of the primordial features to di®erentiate the epileptic events of the others, in coming signs EEG of scalp, it represents a great challenge, since exist to many artifacts, and these are confused with epileptic events. In this sense, this study presents the development of architectures destined to detect events of epilepsy in coming signs EEG of scalp, capable to aid the professionals of the health in the study of this pathology To accomplish the objectives, ¯rstly was developed an application capable to visualize EEG and to segment the electroencephalogram plan to form the base of data Concerning to the detection of the pathological signs, four architectures were proposed. The architecture with analysis multi-resolution used the \ wavelet " (WT) for extraction of features, as well as neural networks and specialist system for recognition. For that architecture the best gotten results obtained a rate of 71,6 % of success, with 28,3 % of error. The sensibility was around 83,3 %, the speci¯city 70,5 % and the precision 76,9 %. The statistical architecture is directly composed of tools for features extraction of the sign. The best success rate was around 85,3 %, the obtained error was of 14,3 % and the inde¯nite ones around 1 %. The sensibility was of 97,4 %, the speci¯city 82,1 % and the precision 89,75 %. The architecture of analysis multi-resolution and AR possesses two stages for extraction of feature: the \ wavelet ", following by the AR models. For that architecture they used two AR models . The best success rate for the \ Yule-Walker"model was around 87,9 %, with order 10. Already in the results of the \ Burg"model, the best success rate was of 88,5 % with order 7. For the last architecture is a hybrid model with several tools of extraction of features in the domain of the time, frequency (FFT) and time-frequency (WT). In that architecture the success rate was in 95,1 %, the error 4,1 % the inde¯nite ones 5,5 %. The speci¯city was of 91,5 %, the obtained sensibility was of 90,5 % and the precision around 91,1 %. Therefore all of the developed systems presented quite coherent results among the phenomena demarcated by the professionals of the medical area and those revealed by the architectures, mainly for the case of the hybrid architecture that presented the best rates. / A identificação de fenômenos epileptogênicos por meio de registros eletroencefalográficos (EEG) não invasivos se constitui numa área de pesquisa que apresenta grandes desafios devido µa presença de diversos distúrbios (artefatos) que dificultam a análise destes registros. Tal tarefa é de extrema importância uma vez que o diagnóstico e o tratamento da epilepsia requer uma avaliação clínica baseada no EEG do paciente. Neste contexto, este trabalho apresenta alguns sistemas para melhorar a identificação dos sinais de crise epilépticas baseados em técnicas de processamento de sinais e de inteligência artificial. Estas propostas são baseadas em uma plataforma que permite a visualização e análise dos arquivos de EEG. Para a detecção de eventos patológicos, são propostas quatro arquiteturas. Na arquitetura com análise multi-resolução foram utilizadas duas famílias wavelet (WT) para a extração de características, redes neurais artificiais e sistema especialista para o reconhecimento dos sinais de crise. Com essa arquitetura, o melhor resultado conseguido foi uma taxa de acerto de 71,6% no reconhecimento dos sinais patológicos. A sensibilidade ficou em torno de 83,3%, a especificidade 70,5% e a precisão 76,9%. Já a arquitetura estatística é composta de ferramentas para extração de características diretamente do sinal. A melhor taxa de acerto ficou em torno de 85,3%, o erro obtido foi de 14,3% e os indefinidos em torno de 1%. A sensibilidade foi de 97,4%, a especificidade 82,1% e a precisão 89,75%. A arquitetura de análise multi-resolução com modelo auto-regressivo (AR) possui duas etapas para extração de características: a \wavelet" (WT), seguida do modelo AR. Para essa arquitetura foram utilizados dois modelos AR. A melhor taxa de acerto para o modelo \Yule-Walker" ficou em torno de 87,9%, com ordem 10. Já para os resultados do modelo\Burg", a melhor taxa de acerto foi de 88,5% com ordem 7. A última arquitetura é um modelo híbrido com várias ferramentas de extração de características no domínio do tempo, freqüência (FFT) e tempo-freqüência (WT). Nessa arquitetura a taxa de acerto ficou em 95,1%, o erro em 4,1% e os indefinidos em 5,5%. A especificidade foi de 91,5%, a sensibilidade obtida foi de 90,5% e a precisão em torno de 91,1%. Todos os sistemas desenvolvidos apresentaram resultados coerentes com os fenômenos demarcados pelos eletroencefalografistas e aqueles revelados pelas arquiteturas. Dentre as propostas, a arquitetura híbrida apresentou o melhor desempenho. / Doutor em Ciências

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-06-22T12:09:45Z No. of bitstreams: 1 cristinamarciabarrosdecastro.pdf: 9219339 bytes, checksum: 92fbbaf80500b5c629a4e62bcd9aa49d (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-07-13T15:29:14Z (GMT) No. of bitstreams: 1 cristinamarciabarrosdecastro.pdf: 9219339 bytes, checksum: 92fbbaf80500b5c629a4e62bcd9aa49d (MD5) / Made available in DSpace on 2016-07-13T15:29:14Z (GMT). No. of bitstreams: 1 cristinamarciabarrosdecastro.pdf: 9219339 bytes, checksum: 92fbbaf80500b5c629a4e62bcd9aa49d (MD5) Previous issue date: 2012-12-27 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Com o objetivo de atendimento à demanda de energia elétrica, buscando um baixo custo na geração de energia, é imprescindível o desenvolvimento do planejamento da operação do setor elétrico brasileiro. O planejamento da operação no horizonte de médio prazo leva em consideração a alta estocasticidade das afluências e é avaliado através da série histórica de Energia Natural Afluente (ENA). No modelo homologado pelo setor, o estudo da ENA tem sido feito por meio da metodologia Box e Jenkins, para determinar os modelos autorregressivos periódicos (PAR(p)), bem como sua ordem . Aos resíduos gerados na modelagem do PAR(p), são aplicados uma distribuição lognormal três parâmetros, como forma de gerar séries sintéticas hidrológicas semelhantes à série histórica original. Contudo, a transformação lognormal incorpora não linearidades que afetam o processo de convergência da Programação Dinâmica Dual Estocástica (PDDE). Este trabalho incorpora a técnica de bootstrap para a geração de cenários sintéticos que servirão de base para a aplicação da PDDE. A técnica estatística Bootstrap é um método alternativo a ser empregado ao problema de planejamento e que permite tanto determinar a ordem ( ) do modelo PAR(p), quanto gerar novas séries sintéticas hidrológicas. Assim, o objetivo do trabalho é analisar os impactos existentes com o uso do Bootstrap no planejamento da operação dos sistemas hidrotérmicos e, em seguida estabelecer uma comparação com a metodologia que tem sido aplicada no setor. Diante dos resultados foi possível concluir que a técnica bootstrap permite a obtenção de séries hidrológicas bem ajustadas e geram resultados confiáveis quanto ao planejamento da operação de sistemas hidrotérmicos, podendo ser usada como uma técnica alternativa ao problema em questão. / Aiming to match the long term load demand with a low cost in power generation, it is very important to improve more and more the operation planning of the Brazilian electric sector. The operation planning of medium/long term takes into account the water inflows, which are strongly stochastic, and it must be evaluated using the series of Natural Energy Inflows (NEI). In the current computational model applied to Brazilian operation planning of medium/long term, the study of ENA has been done by Box and Jenkins methodology, which determines the periodic autoregressive model (PAR (p)), as well as its order p. A lognormal distribution with three parameters is applied on the residues that are created by the PAR (p) model, as a way to generate synthetic hydrologic series similar to the original series. However, this lognormal transformation brings nonlinearities which can disturb the stability and convergence of Stochastic Dual Dynamic Programming (SDDP). This thesis incorporates the bootstrap technique to create synthetic scenarios which will be taken into account as a basis for the SDDP implementation. This statistical technique, called bootstrap, is an alternative method used to determine both the order (p) of the model PAR (p), and, after that, to produce synthetic hydrological series. Thus, the objective of this thesis is to analyze the impact of the Bootstrap technique compared to the current methodology. The results showed that the bootstrap technique is suitable to obtain adherent hydrological series. So, it was created reliable scenarios regarding the planning of the operation of hydrothermal systems. Finally, this new methodology can be used as an alternative technique to long term hydrothermal planning problems.

This article compares the stock exchanges in Vienna, Budapest, Frankfurt and Milan. It settles basic information about their development, the subject of exchange and the classification of market segments. This work also characterizes the trade system of each of the stock exchanges and the liquidity of spot market, with particular emphasis on stock market. A part of this analysis is the comparison of markets based on aspects such as market capitalization, trade volume or the quantity of trade. Last chapter is devoted to the interconnection of stock markets, which is explored by the means of correlation coefficient among different indexes. These indexes show a strong link of the markets and through splitting the timeframe into periods of (2000-2004 - before Hungary's EU entry and after - 2004-2009), a stronger correlation was discovered during the period of index growth (after the Hungary's EU entry). By creating a more autoregressive model VAR, which describes individual processes among stock indexes and the direction of dependency, the hypothesis of strong interconnection of stock markets was proven. VAR model verified one-sided reliance among indexes and the rising level of integration of world markets.

Cities have adopted evolving urban digitization strategies, and most of those increasingly focus on data, especially in the field of public transportation. Transportation data have intuitively spatial and temporal characteristics, for they are often described with when and where the trips occur. Since a trip is often described with many attributes, the transportation data can be presented with a tensor, a container which can house data in $N$-dimensions. Unlike a traditional data frame, which only has column variables, tensor is intuitively more straightforward to explore spatio-temporal data-sets, which makes those attributes more easily interpreted. However, it requires unique techniques to extract useful and relatively correct information in attributes highly correlated with each other. This work presents a mixed model consisting of tensor decomposition combined with seasonal vector autoregression in time to find latent patterns within historical taxi data classified by types of taxis, pick-up and drop-off times of services in NYC, so that it can help predict the place and time where taxis are demanded. We validated the proposed approach using the experiment evaluation with real NYC tax data. The proposed method shows the best prediction among alternative models without geographical inference, and captures the daily patterns of taxi demands for business and entertainment needs.

No description available.

The objective of the thesis is to examine interdependencies among the stock markets of the Czech Republic, Hungary, Poland and Germany in the period 2008-2010. Two main methods are applied in the analysis. The first method is based on the use of high-frequency data and consists in the computation of realized correlations, which are then modeled using the heterogeneous autoregressive (HAR) model. In addition, we employ realized bipower correlations, which should be robust to the presence of jumps in prices. The second method involves modeling of correlations by means of the Dynamic Conditional Correlation GARCH (DCC-GARCH) model, which is applied to daily data. The results indicate that when high-frequency data are used, the correlations are biased towards zero (the so-called "Epps effect"). We also find quite significant differences between the dynamics of the correlations from the DCC-GARCH models and those of the realized correlations. Finally, we show that accuracy of the forecasts of correlations can be improved by combining results obtained from different models (HAR models for realized correlations, HAR models for realized bipower correlations, DCC-GARCH models).

Submitted by Sara Ribeiro (sara.ribeiro@ucb.br) on 2018-06-18T18:53:22Z No. of bitstreams: 1 RafaeldeMoraisVargasDissertacao2018.pdf: 2179808 bytes, checksum: e2993cd35f13b4bd6411d626aefa0043 (MD5) / Approved for entry into archive by Sara Ribeiro (sara.ribeiro@ucb.br) on 2018-06-18T18:54:14Z (GMT) No. of bitstreams: 1 RafaeldeMoraisVargasDissertacao2018.pdf: 2179808 bytes, checksum: e2993cd35f13b4bd6411d626aefa0043 (MD5) / Made available in DSpace on 2018-06-18T18:54:14Z (GMT). No. of bitstreams: 1 RafaeldeMoraisVargasDissertacao2018.pdf: 2179808 bytes, checksum: e2993cd35f13b4bd6411d626aefa0043 (MD5) Previous issue date: 2018-02-27 / Given the importance of market risk measures, such as value at risk (VaR), in this paper, we compare traditionally accepted volatility forecast models, in particular, the GARCH family models, with more recent models such as HAR-RV and GAS in terms of the accuracy of their VaR forecasts. For this purpose, we use intraday prices, at the 5-minute frequency, of the S&P 500 index and the General Electric stocks, for the period from January 4, 2010 to December 30, 2013. Based on the tick loss function and the Diebold-Mariano test, we did not find difference in the predictive performance of the HAR-RV and GAS models in comparison with the Exponential GARCH (EGARCH) model, considering daily VaR forecasts at the 1% and 5% significance levels for the return series of the S&P 500 index. Regarding the return series of General Electric, the 1% VaR forecasts obtained from the HAR-RV models, assuming a t-Student distribution for the daily returns, are more accurate than the forecasts of the EGARCH model. In the case of the 5% VaR forecasts, all variations of the HAR-RV model perform better than the EGARCH. Our empirical study provides evidence of the good performance of HAR-RV models in forecasting value at risk. / Dada a importância de medidas de risco de mercado, como o valor em risco (VaR), nesse trabalho, comparamos modelos de previsão de volatilidade tradicionalmente mais aceitos, em particular, os modelos da família GARCH, com modelos mais recentes, como o HAR-RV e o GAS, em termos da acurácia de suas previsões de VaR. Para isso, usamos preços intradiários, na frequência de 5 minutos, do índice S&P 500 e das ações da General Electric, para o período de 4 de janeiro de 2010 a 30 de dezembro de 2013. Com base na função perda tick e no teste de Diebold-Mariano, não encontramos diferença no desempenho preditivo dos modelos HAR-RV e GAS em relação ao modelo Exponential GARCH (EGARCH), considerando as previsões de VaR diário a 1% e 5% de significância para a série de retornos do índice S&P 500. Já com relação à série de retornos da General Electric, as previsões de VaR a 1% obtidas a partir dos modelos HAR-RV, assumindo uma distribuição t-Student para os retornos diários, mostram-se mais acuradas do que as previsões do modelo EGARCH. No caso das previsões de VaR a 5%, todas as variações do modelo HAR-RV apresentam desempenho superior ao EGARCH. Nosso estudo empírico traz evidências do bom desempenho dos modelos HAR-RV na previsão de valor em risco.

動態資料往往隨著時間區間取法或測量工具的不同而有差異,此種不確定的特質我們稱為模糊性。但是傳統的時間數列仍是以確定的觀察值來記錄具有模糊性的動態資料。為了更完整的表示一個動態過程,我們考慮模糊時間數列(fuzzy time series)以具有不確定性的模糊集合來取代明確的數值,保持原來的模糊性。 本文探討模糊時間數列中模糊自我迴歸模式(fuzzy autoregressive model簡寫為 FAR 模式)的建構過程,並分別利用此模式來預測中央政府總預算和匯率。FAR 模式乃根據Box-Jenkins(1970)所提出的 ARMA 三階段模式建立的流程並推廣Zadeh(1965)所提出的模糊集合理論而來。在這過程中 ,我們考慮人類思維方法,使FAR 模式更具有彈性且適合未來預測時的需要。而對於所討論的動態過程,也不需要任何模式上的假設(例如:線性或穩定 ),因此 FAR 模式的適用範圍極為廣泛,更不會因為模式的誤判而導致預測時的嚴重錯誤。最後,我們將 FAR 模式的預測結果與傳統 ARMA 模式做比較。 文中關於模糊時間數列的一些性質,例如:模糊趨勢(fuzzy trend)和模糊穩定(fuzzy stationary),由於傳統文獻中沒有加以討論,本文亦提出定義和新的看法。 / Representations of dynamic data are always different as the time interval or measuring tool change. We call these characteristics of uncertainty fuzziness. But traditional time series use crisp observations to record a fuzzy dynamic process. To completely represent, we consider fuzzy time series replacing the crisp numbers with fuzzy sets and preserve original fuzziness. In this paper, the fuzzy autoregressive model (FAR model) of fuzzy time series is studied and used to forecast the Central government expenditure and exchange rates, respectively. The modeling process is according to Box- Jenkins' (1970) method of ARMA model and merged with the fuzzy set theory proposed by Zadeh (1965). Reasonable human judgements and ways of thinking are taken into consideration throughout the modeling process to make the FAR model more elastic and appropriate for forecasting. Unlike certain incorrectly identified models which lead to inaccurate forecasts, the FAR model can be widely applied due to its not having any assumptions on the original time series (e.g., linearity and stationarity). Finally, the performances of the FAR model to Central government expenditure and exchange rates are compared with that of the traditional ARMA model. Additionally, some properties about fuzzy time series, e.g., fuzzy trend and fuzzy stationary, have not been studied in the literature, and we propose definitions and new opinions.

台灣幅員狹小，天然資源不足，唯有藉著大量出口才能換取外匯，情況使得台灣逐漸發展成一小型開放經濟。長久以來，美國一直是台灣最大的貿易夥伴，使得台灣產品對美輸出的多寡往往直接影響台灣總體經濟的表現。隨著政府外匯政策的逐漸自由化，匯率在總體經濟中所扮演的角色也越顯重要。近幾年來，台幣匯價在外匯市場上時有波動，不但影響政府政策的擬定、經貿活動的往來，外匯市場上的投炒作更造成熱錢的流動。是故，新台幣對美元匯率的決定及波動因素是值得我們深入探討的課題。基於此點，本文擬建立一個可供實証的小型開放經濟模型，試圖探討新台幣對美元匯率的決定因素。首先，參照Frankel(1979)所提出的實質利率差價模型(Real Interest Rate Differential Model)，作為實証研究的基礎。其次，利用Johansen(1988,1991)、Johansen & Juselius(1990)的共整合(cointegration)分析方法，以台灣地區1981年至1993年間的月資料，驗証縮減式的長期關係是否成立。最後，採用誤差修正模型(error correction model)，估計匯率的動態調整途徑，並對匯率變動率進行樣本後預測。 　　實証結果發現：(1)實質匯率差價模型所刻畫的匯率與其他經濟變數的長期關係在台灣是可以成立的；(2)傳統貨幣學派對兩國結構喜數相同的假設過於嚴苛，對於台灣及美國並不適用；(3)除了名目利率外，台灣及美國的貨幣供給、產出水準及通貨膨脹率具有一對一的關係；(4)以誤差修正模型預測台幣／美元匯率變動率，其效果優於隨機漫步模型。