自從2007年發生金融海嘯後，歐盟貨幣整合問題持續被討論，其中，歐盟成員國各國經濟狀況差距過大被認為是這次經濟危機爆發出種種問題的主要原因。而近幾年，在國際其他地區區域性整合刺激之下，亞洲各國開始重視簽屬自由貿易協定的重要性，甚至未來朝區域貨幣整合作發展。 此論文主要是探討東亞國家是否具備成立亞元區的條件。我們探討東亞各國2004-2015年間對美元匯率以及消費者物價指數(CPI)變化是否有一致的趨勢，主要使用MST及HC、DCG tree機器學習分群演算法作為分析工具，對於東亞十個國家，包含台灣、日本、中國、南韓、香港、印尼、泰國、馬來西亞、菲律賓、新加坡以及用於對照的世界上其他國家作分析，以2008年金融海嘯以及2013年日本提出新經濟政策作為分界點，分為三個時期做討論。 研究結果顯示，三個時期，東亞國家匯率變動相較於世界其他國家皆較有一致的趨勢，而更進一步檢視，我們發現在日本提出新經濟政策前後，東亞國家匯率結構發生明顯的變化。物價指數方面同樣也是東亞國家有較集中的趨勢，但物價指數不向匯率，不同時期並無明顯變化。我們進而推論東亞國家以符合成立亞元區最基本的條件，匯率和物價指數的一致性，但是有幾個結果不能忽略，日本對於東亞國家匯率結構的影響力以及人民幣、日圓等強勢貨幣匯率變動趨勢在分析結果中和其餘東協國家還是有些差距，這些可能是在未來貨幣整合過程中市所需要注意的部分。 / After the global financial crisis, European Union (EU) faced a range of economic and political problems, including the Greek debt crisis and the upcoming UK referendum on EU membership. People start to think what factors cause these problems. A large literature has emerged discussing this issue and examining the future directions of monetary and exchange rate agreements of many countries in the region. One of the popular questions has raised researchers’ interests: Are East Asian countries possible to be the next monetary integrated area? This paper investigates the similarity of exchange rates and consumer price index (CPI) in the East Asian region, Taiwan, Japan, China, Hong Kong, South Korea and five ASEAN member countries by cluster analysis. We use HC tree and DCG tree to discuss the structures of these two macro economic factors and whether major economic events between 2004~2015 such as global financial crisis and Japan’s new economic policy influenced the structure. The exchange rates and CPI are both found to be more consistent among East Asian countries, including that these countries already have the basic condition to be the monetary integrated area. However, it is important to pay attention to Japan’s new economic policy, which influenced the structure a lot, and that Japanese yen and Chinese yuan still have gaps with ASEAN member countries.

This thesis explores whether scanner data can be used to inform Consumer Price Index (CPI) construction, with particular reference to the issues of substitution bias and choice of aggregation dimensions. The potential costs and benefits of using scanner data are reviewed. Existing estimates of substitution bias are found to show considerable variation. An Australian scanner data set is used to estimate substitution bias for six different aggregation methods and for fixed base and superlative indexes. Direct and chained indexes are also calculated. Estimates of substitution bias are found to be highly sensitive to both the method of aggregation used and whether direct or chained indexes were used. The ILO (2004) recommends the use of dissimilarity indexes to determine the issue of when to chain. This thesis provides the first empirical study of dissimilarity indexes in this context. The results indicate that dissimilarity indexes may not be sufficient to resolve the issue. A Constant Elasticity of Substitution (CES) index provides an approximate estimate of substitution-bias-free price change, without the need for current period expenditure weights. However, an elasticity parameter is needed. Two methods, referred to as the algebraic and econometric methods, were used to estimate the elasticity parameter. The econometric approach involved the estimation of a system of equations proposed by Diewert (2002a). This system has not been estimated previously. The results show a relatively high level of substitution at the elementary aggregate level, which supports the use a Jevons index, rather than Carli or Dutot indexes, at this level. Elasticity parameter estimates were found to vary considerably across time, and statistical testing showed that elasticity parameter estimates were significantly different across estimation methods. Aggregation is an extremely important issue in the compilation of the CPI. However, little information exists about 'appropriate' aggregation methods. Aggregation is typically recommended over 'homogenous' units. An hedonic framework is used to test for item homogeneity across four supermarket chains and across all stores within each chain. This is a novel approach. The results show that treating the same good as homogenous across stores which belong to the same chain may be recommended.

This thesis explores whether scanner data can be used to inform Consumer Price Index (CPI) construction, with particular reference to the issues of substitution bias and choice of aggregation dimensions. The potential costs and benefits of using scanner data are reviewed. Existing estimates of substitution bias are found to show considerable variation. An Australian scanner data set is used to estimate substitution bias for six different aggregation methods and for fixed base and superlative indexes. Direct and chained indexes are also calculated. Estimates of substitution bias are found to be highly sensitive to both the method of aggregation used and whether direct or chained indexes were used. The ILO (2004) recommends the use of dissimilarity indexes to determine the issue of when to chain. This thesis provides the first empirical study of dissimilarity indexes in this context. The results indicate that dissimilarity indexes may not be sufficient to resolve the issue. A Constant Elasticity of Substitution (CES) index provides an approximate estimate of substitution-bias-free price change, without the need for current period expenditure weights. However, an elasticity parameter is needed. Two methods, referred to as the algebraic and econometric methods, were used to estimate the elasticity parameter. The econometric approach involved the estimation of a system of equations proposed by Diewert (2002a). This system has not been estimated previously. The results show a relatively high level of substitution at the elementary aggregate level, which supports the use a Jevons index, rather than Carli or Dutot indexes, at this level. Elasticity parameter estimates were found to vary considerably across time, and statistical testing showed that elasticity parameter estimates were significantly different across estimation methods. Aggregation is an extremely important issue in the compilation of the CPI. However, little information exists about 'appropriate' aggregation methods. Aggregation is typically recommended over 'homogenous' units. An hedonic framework is used to test for item homogeneity across four supermarket chains and across all stores within each chain. This is a novel approach. The results show that treating the same good as homogenous across stores which belong to the same chain may be recommended.

Submitted by Simone Jager (si_jager@hotmail.com) on 2014-02-10T15:30:57Z No. of bitstreams: 1 Simone Jager 2014.pdf: 764649 bytes, checksum: 100e29a7572ff1d6c57a770ace28e1bf (MD5) / Approved for entry into archive by Vitor Souza (vitor.souza@fgv.br) on 2014-02-24T21:08:40Z (GMT) No. of bitstreams: 1 Simone Jager 2014.pdf: 764649 bytes, checksum: 100e29a7572ff1d6c57a770ace28e1bf (MD5) / Made available in DSpace on 2014-05-20T13:15:26Z (GMT). No. of bitstreams: 1 Simone Jager 2014.pdf: 764649 bytes, checksum: 100e29a7572ff1d6c57a770ace28e1bf (MD5) Previous issue date: 2014-01-23 / This paper compares time series models to forecast short-term Brazilian inflation measured by Consumer Price Index (IPCA). Were considered SARIMA Box-Jenkins models and structural models in state space, as estimated by the Kalman filter. For estimation of the models, the series of IPCA monthly basis from March 2003 to March 2012 was used. The SARIMA models were estimated in EVIEWS and structural models in STAMP. For the validation of the models out of sample forecasts were considered one step ahead for the period April 2012 to March 2013, based on the main criteria for assessing predictive ability proposed in the literature. The conclusion of the study is that, although the structural model allows, to decompose the series into components with direct interpretation and study them separately, while incorporating explanatory variables in a simple way, the performance of the SARIMA model to predict Brazilian inflation was higher in the period and horizon considered. Another important positive aspect is that the implementation of a SARIMA model is ready, and predictions from it are obtained in a simple and direct way. / Este trabalho compara modelos de séries temporais para a projeção de curto prazo da inflação brasileira, medida pelo Índice de Preços ao Consumidor Amplo (IPCA). Foram considerados modelos SARIMA de Box e Jenkins e modelos estruturais em espaço de estados, estimados pelo filtro de Kalman. Para a estimação dos modelos, foi utilizada a série do IPCA na base mensal, de março de 2003 a março de 2012. Os modelos SARIMA foram estimados no EVIEWS e os modelos estruturais no STAMP. Para a validação dos modelos para fora da amostra, foram consideradas as previsões 1 passo à frente para o período de abril de 2012 a março de 2013, tomando como base os principais critérios de avaliação de capacidade preditiva propostos na literatura. A conclusão do trabalho é que, embora o modelo estrutural permita, decompor a série em componentes com interpretação direta e estudá-las separadamente, além de incorporar variáveis explicativas de forma simples, o desempenho do modelo SARIMA para prever a inflação brasileira foi superior, no período e horizonte considerados. Outro importante aspecto positivo é que a implementação de um modelo SARIMA é imediata, e previsões a partir dele são obtidas de forma simples e direta.