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

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。

銅價在近年來不斷的創下歷史新高，由於台灣蓬勃的電子、半導體、工具機產業皆需要銅，因此銅進口量位居全球第五(ICSG,2009)，使得台灣企業的生產成本受國際銅價的波動影響甚鉅，全球有70%的銅價是按照英國倫敦金屬交易所(London Metal Exchange, LME)的牌價進行貿易，因此本研究欲建置預測模式以預測銅價未來趨勢。 　　本研究之資料來源為2003年1月2日至2011年7月14日的LME三月期銅價，並依文獻探討選取LME的銅庫存、三月期鋁價、三月期鉛價、三月期鎳價、三月期鋅價、三月期錫價，以及金價、銀價、石油價格、美國生產者物價指數、美國消費者物價指數、聯邦資金利率作為影響因素的分析資料。時間序列分析、類神經網路已被廣泛的用於預測股市及期貨，本研究先藉由向量自我迴歸模型篩選出有影響力的變數，同時建置GARCH時間序列預測模型與具有遞迴的Elman類神經網路預測模型，再整合兩者建置GARCH-Elman類神經網路預測模型。 本研究之向量自我迴歸模型顯示銅價與金、鋁、銅庫存前第1期；自身前第2期；鎳、錫前第3期；鋅前第4期的變動有負向的影響；受到石油前第2期的變動有正向的影響，這其中以銅的自我解釋變異最高，銅庫存最低，推測其影響已有效率地反映到銅價上。也驗證預測模型必須考量總體經濟變數，且變數先經向量自我迴歸模型的篩選能因減少雜訊而提升類神經網路的預測能力。依此建置的GARCH模型有33.81%的累積報酬率、Elman類神經網路38.11%、整合兩者的GARCH-Elman類神經網路56.46%，皆優於實際銅價指數的累積報酬率。對銅有需求的企業者，能更為準確的預測漲跌趨勢，依此判斷如何跟原物料供應商簽訂合約的價格與期間，使其免於價格趨勢的誤判而提高生產成本，並提出五點建議供未來研究者參考。 / The recent copper price in London Metal Exchange (LME) has breaking the historical high. Taiwan’s booming electronics, semiconductor and machine tool industry causing copper import volume ranked fifth in the world (ICSG, 2009). Because of 70% of copper worldwide trade in accordance with the price of the London Metal Exchange, this study using time series and neural networks to build the LME copper price forecast model. 　　This study considering copper, copper stocks, aluminum, lead, nickel, zinc, tin, gold, silver, oil ,federal funds rate, CPI and PPI during the period of 2003/1/2 to 2011/7/14. Time series model and neural networks have been widely used for forecasting the stock market and futures. In this study, using Vector Autoregressive (VAR) model screened influential variables, building GARCH model and Elman neural network to forecast the LME copper price; and further, integrating this two models to build GARCH-Elman neural network prediction model. 　　This study’s VAR models show that the copper has negative effect with gold, aluminum, copper stocks, nickel, tin, zinc and itself. And has positive impact with oil prices. The highest of explained variance is copper. Copper stocks are lowest, speculating that its impact has been efficiently reflecting on the price of copper. Verifying the prediction model must consider the macroeconomics variables. Using VAR model screened influential variables can reduce noise to enhance the predictive ability of the neural network. This study’s GARCH model has 33.81% of the cumulative rate of return, Elman neural network has 38.11% and the GARCH-Elman neural network has 56.46%. All of them are better than the actual price of copper.

Government fiscal and monetary policy has long been based on the theory that was neither proven nor refuted since its origination. The original form of the Phillips curve has undergone significant modifications but its relevance remains questionable. This thesis examines the correlation between inflation and unemployment observed in the Czech Republic and Germany over the last twenty years. The validity of the theory is tested by advanced methods of time series analysis in the R environment. All the variables are gradually tested which results in the assessment of the correlation between the time series. The outcome of the testing is presented for both countries and a comparison at international level is drawn. Is is discovered that both of the countries have dependencies in their data. Czech republic has significant dependency in both ways, for Germany is the dependency significantly weaker and only in one way.