本文是探討管理浮動匯率時期（1978年第三季至1993年第三季），台灣地區經常帳盈餘發生的原因，同時考慮匯率因素、貨幣市場及商品與勞務市場吸納的情況。利用兩個向量自迴歸模型，分別納入：(1)匯率、利率、經常帳、消費節約及貨幣供給，(2)匯率、利率、經常帳、財政盈餘及貨幣供給，藉由因果關係檢定、預測誤差分解、及衝擊反應，分析經常帳失衡的原因。 　　實証結果指出：台灣地區經常帳盈餘，深受匯率、財政盈餘及消費節約的影響，這個結論與我國低估幣值與出口拓展的政策一致。且經常帳盈餘並不能夠顯著的影響貨幣供給，這個結論與央行沖銷的措施一致，其目的無非是要隔離國外部門影響國內貨幣。足見自由化的匯率政策，不但讓匯率反應出合理的水準值，同時可追求獨立的貨幣政策，配合著獎勵投資、消費及增加公共支出，增加國內吸納，藉以減少鉅幅的經常帳盈餘。

本篇論文擴充Ang et al. (2007)之基本架構,分別建構台灣各式月資料與季資料的物價指數預測模型,並進行預測以及實證分析。我們用以衡量通貨膨脹率的指標為 CPI 年增率與核心CPI 年增率。我們比較貨幣模型、成本加成模型、6 種不同設定的菲力浦曲線模型、3 種期限結構模型、隨機漫步模型、 AO 模型、ARIMA 模型、VAR 模型、主計處(DGBAS)、中經院(CIER) 及台經院(TIER) 之預測。藉由此研究,我們可以完整評估出文獻上常用之各式月資料及季資料預測模型的優劣。 我們實證結果顯示,在月資料預測模型樣本外預測績效表現方面, ARIMA 模 型對 2 種通貨膨脹率指標的樣本外預測能力表現最好。至於季資料預測模型樣本外預測績效表現, ARIMA 模型對未來核心 CPI 年增率的樣本外預測能力表現最好; 然而,對於 CPI 年增率為預測目標的預測模型則不存在最佳的模型。此外,實證分析中我們也發現本研究所建構的模型預測表現仍遜於主計處的預測,但部份模型的樣本外預測能力表現則比中經院與台經院的預測為佳。 / This paper compares the forecasting performance of inflation in Taiwan. We conduct various inflation forecasting methods (models) for two inflation measures(CPI growth rate and core-CPI growth rate) by using monthly and quarterly data. Besides the models of Ang et al. (2007), we also consider some macroeconomic models for comparison. We compare some Monetary models, Mark-up models, six variants of Phillips curve models, three variants of term structure models, a Random walk model, an AO model, an ARIMA model, and a VAR model. We also compare the forecast ability of these model with three different survey forecasts (the DGBAS, CIER, and TIER surveys). We summarized our findings as follows. The best monthly forecasting model for both inflation measures is ARIMA model. For quarterly core-CPI inflation, ARIMA model is also the best model; however, when comparing the quarterly forecasts for CPI inflation, there does not exist the best one. Besides, we also found that the DGBAS survey outperforms all of our forecasting methods/models, but some of our forecasting models are better than the CIER and TIER surveys in terms of MAE.

After many decades, macroeconomic effects of fiscal policy have returned to the centre of the economic policy debate. Both automatic fiscal stabilizers and discretionary fiscal stimuli have been used to support aggregate demand during the recent global economic crisis with a subsequent need for large-scale fiscal consolidations. In this context, a proper assessment of the size of automatic fiscal stabilizers and fiscal multipliers represents a key input for fiscal policymaking. This dissertation provides a quantitative analysis of the interactions between fiscal policy and real economy in the Czech Republic. The impact of real economy developments on public finances is assessed based on the methods of the OECD, the European Commission and the ESCB for the identification of general government structural balances, i.e. balances adjusted for effects of the economic cycle and net of one-off and other temporary transactions. I find that the underlying fiscal position, as approximated by the government structural balance, was mostly below the level stabilising the debt-to-GDP ratio since mid-1990s. An indistinct improvement in the structural balance can be identified in the period 2004--2007, which was subsequently reversed by the adverse structural impact of the world economic crisis. At the same time, dynamics of unadjusted fiscal balance was largely determined by one-off transactions in the past. The effects of fiscal policy on real economy are analysed using the structural VAR approach. I find that an increase in government spending has a temporary positive effect on output that peaks after one to two years with a multiplier of around 0.6. Tax multiplier appears to be small and, in contrast to standard Keynesian assumptions, positive. Government spending is supportive to private consumption, contradicting the hypothesis of Ricardian equivalence, but it crowds out private investment in the short run. The results should be interpreted with caution, as the analysis is complicated by rapidly changing economic environment in the period of the economic transition, relatively short available time series and a large number of one-off fiscal transactions.

Diese Arbeit stellt Modelle und Methoden vor, die für robuste Statistiken und ihre Anwendungen in verschiedenen Bereichen entwickelt wurden. Kapitel 2 stellt einen neuartigen Partitionierungs-Clustering-Algorithmus vor, der auf Expectiles basiert. Der Algorithmus bildet Cluster, die sich an das Endverhalten der Clusterverteilungen anpassen und sie dadurch robuster machen. Das Kapitel stellt feste Tau-Clustering- und adaptive Tau-Clustering-Schemata und ihre Anwendungen im Kryptowährungsmarkt und in der Bildsegmentierung vor. In Kapitel 3 wird ein faktorerweitertes dynamisches Modell vorgeschlagen, um das Tail-Verhalten hochdimensionaler Zeitreihen zu analysieren. Dieses Modell extrahiert latente Faktoren, die durch Extremereignisse verursacht werden, und untersucht ihre Wechselwirkung mit makroökonomischen Variablen mithilfe des VAR-Modells. Diese Methodik ermöglicht Impuls-Antwort-Analysen, Out-of-Sample-Vorhersagen und die Untersuchung von Netzwerkeffekten. Die empirische Studie stellt den signifikanten Einfluss von durch finanzielle Extremereignisse bedingten Faktoren auf makroökonomische Variablen während verschiedener Wirtschaftsperioden dar. Kapitel 4 ist eine Pilotanalyse zu Non Fungible Tokens (NFTs), insbesondere CryptoPunks. Der Autor untersucht die Clusterbildung zwischen digitalen Assets mithilfe verschiedener Visualisierungstechniken. Die durch CNN- und UMAP-Regression identifizierten Cluster werden mit Preisen und Merkmalen von CryptoPunks in Verbindung gebracht. Kapitel 5 stellt die Konstruktion eines Preisindex namens Digital Art Index (DAI) für den NFT-Kunstmarkt vor. Der Index wird mithilfe hedonischer Regression in Kombination mit robusten Schätzern für die Top-10-Liquid-NFT-Kunstsammlungen erstellt. Es schlägt innovative Verfahren vor, nämlich Huberisierung und DCS-t-Filterung, um abweichende Preisbeobachtungen zu verarbeiten und einen robusten Index zu erstellen. Darüber hinaus werden Preisdeterminanten des NFT-Marktes analysiert. / This thesis provides models and methodologies developed on robust statistics and their applications in various domains. Chapter 2 presents a novel partitioning clustering algorithm based on expectiles. The algorithm forms clusters that adapt to the tail behavior of the cluster distributions, making them more robust. The chapter introduces fixed tau-clustering and adaptive tau-clustering schemes and their applications in crypto-currency market and image segmentation. In Chapter 3 a factor augmented dynamic model is proposed to analyse tail behavior of high-dimensional time series. This model extracts latent factors driven by tail events and examines their interaction with macroeconomic variables using VAR model. This methodology enables impulse-response analysis, out-of-sample predictions, and the study of network effects. The empirical study presents significant impact of financial tail event driven factors on macroeconomic variables during different economic periods. Chapter 4 is a pilot analysis on Non Fungible Tokens (NFTs) specifically CryptoPunks. The author investigates clustering among digital assets using various visualization techniques. The clusters identified through regression CNN and UMAP are associated with prices and traits of CryptoPunks. Chapter 5 introduces the construction of a price index called the Digital Art Index (DAI) for the NFT art market. The index is created using hedonic regression combined with robust estimators on the top 10 liquid NFT art collections. It proposes innovative procedures, namely Huberization and DCS-t filtering, to handle outlying price observations and create a robust index. Furthermore, it analyzes price determinants of the NFT market.