FU, Diss.--Zugl.: Berlin, 2004. / Literaturverz. S.143-155.

Thesis (doctoral)--Hochschule für Wirtschaft und Politik, 1998. / Includes bibliographical references (p. 237-246).

Thesis (doctoral) - Freie Universität, Berlin, 2004. / "with 12 figures and tables". Includes bibliographical references ( p. [143]-155).

Doctorat en sciences sociales, politiques et économiques / info:eu-repo/semantics/nonPublished

MCOM / Department of Economics / The choice of a weak or strong currency has been at the center of the debate in most developing economies as exchange rates play a vital role in a country’s level of economic growth. This growth is critical to many developing economies. The study assessed the role of real exchange rate on economic growth in South Africa from 1994, first quarter, to 2015, fourth quarter. The study used time-series data in which Augmented Dicky Fuller and Philip Perron tests for stationarity, cointegration test, Vector Error Correction Model (VECM) approach for the long-run relationship were conducted. Impulse Response Function (IRF) and Variance Decomposition (VD) were also conducted to explain the response to shock amongst variables and how much of the forecasting error variance is explained by the exogenous shocks to other variables. VECM results showed a positive role exchange rates play on economic growth in South Africa. The study’s implication is that currency devaluation (exchange rates depreciation) can be effective in improving economic growth in the short-run. Nonetheless, a strong currency is good for economic growth in the long-run as it attracts foreign investments and a good instrument for controlling inflation. Thus, basing on the findings of the study, the floating exchange rate system adopted by South Africa in 2000 can be maintained.

published_or_final_version / Business Administration / Master / Master of Business Administration

建立模型來估計匯率早已行之有年。較早期的匯率模型，不論是在樣本內的配適或是樣本外的預測，其實表現的並不理想。之後的研究針對這樣的結果指出，這是因為匯率的表現是非線性的，並非傳統線性模型可描繪出來。而對於捕捉匯率非線性的特性，傾向使用無母數的估計方式。因此，本研究採用最近鄰域法進行美元兌新台幣的匯率預測。另外，許多早期的研究發現，隨機漫步模型與其他模型相比較之後，在匯率預測上的表現最好，因而引發了”打敗隨機漫步”的一連串熱潮。本研究欲延續這項議題，將隨機漫步模型做為與最近鄰域模型比較的基準。 / 本研究使用的資料為即期匯率，包含日資料、週資料和月資料三種。將每種資料皆切割為樣本內與樣本外兩個部分，其中最後三分之一的樣本數用於樣本外預測。平均絕對誤差與平均誤差平方根則是用來衡量比較模型預測的準確性。實證結果發現，使用局部加權估計的最近鄰域模型在樣本內的配適表現上優於隨機漫步模型；然而，在樣本外的預測能力上，隨機漫步模型仍舊略勝一籌。 / A wide variety of empirical exchange rate models have been estimated over the years. Earlier findings indicated that exchange rate equations do not fit particularly well, and forecast no better. Later researches then provided a potential reason for the poor performance that traditional exchange rate models, because they are nonlinear. To find a resolution for nonlinearity, nonparametric techniques tend to be useful tools. In this study, we use one of nonparametric techniques called nearest-neighbors method to predict NTD against USD. Besides, many earlier papers found that forecasts from popular models for the foreign exchange rate generally fail to improve upon the random walk out-of-sample. “Beat the random walk” became an emerging issue then. This has motivated this research, and thus we include the random walk as a linear benchmark. / The data set consists of the daily, weekly and monthly spot rates for NTD/USD. We divide each data set into a fitting set and a prediction set for in-sample analysis and out-of-sample forecast, respectively. The out-of-sample forecasts are calculated from the last one-third of each series. As a measure of performance the mean squared error (MAE) and root mean squared error (RMSE) are used. In our empirical results, we find that nearest-neighbors model using local weights easily tops the random walk in-sample. However, as we turn to the out-of-sample prediction, no models produce forecasts superior to the random walk. It seems difficult to beat the random walk out-of-sample in this study.

This thesis consists of three self-contained essays in International Trade, Exchange Rates and Prices. Although independent, these essays share some common themes. The first two papers can be related to the vast literature on exchange rate pass-through to prices. While the first paper uses firm-product level data from Sweden to study firms’ export price response to movements in exchange rate, the second paper employs aggregate level data from Ethiopia and looks at the issue from the importers’ perspective. The third paper, like the first paper, uses Swedish firm-level data and investigates firms’ exporting behavior. The third paper, however, specifically focuses on export margins of multi-product firms and studies their response when exporting to destinations of different size and distance from the home country. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Manuscript. Paper 2: Manuscript. Paper 3: Manuscript.</p>

In an effort to assess the predictive ability of exchange rate models when data on African countries is sampled, this paper studies nonlinear modelling and prediction of the nominal exchange rate series of the United States dollar to currencies of thirty-eight African states using the smooth transition autoregressive (STAR) model. A three step analysis is undertaken. One, it investigates nonlinearity in all nominal exchange rate series examined using a chain of credible statistical in-sample tests. Significantly, evidence of nonlinear exponential STAR (ESTAR) dynamics is detected across all series. Two, linear models are provided another chance to make it right by shuffling to data on African countries to investigate their predictive power against the tough random walk without drift model. Linear models again failed significantly. Lastly, the predictive ability of nonlinear models against both the random walk without drift and the corresponding linear models is investigated. Nonlinear models display useful forecasting gains over all contending models.

Potentially valuable information about the underlying data generating process of a dependent variable is often lost when an independent variable is transformed to fit into the same sampling frequency as a dependent variable. With the mixed data sampling (MIDAS) technique and increasingly available data at high frequencies, the issue of choosing an optimal sampling frequency becomes apparent. We use financial data and the MIDAS technique to estimate thousands of regressions and forecasts in the quarterly, monthly, weekly, and daily sampling frequencies. Model fit and forecast performance measurements are calculated from each estimation and used to generate summary statistics for each sampling frequency so that comparisons can be made between frequencies. Our regression models contain an autoregressive component and five additional independent variables and are estimated with varying lag length specifications that incrementally increase up to five years of lags. Each regression is used to forecast a rolling, one and two-step ahead, static forecast of the quarterly Yen and U.S Dollar spot exchange rate. Our results suggest that it may be favourable to include high frequency variables for closer modeling of the underlying data generating process but not necessarily for increased forecasting performance. / Graduate / 0501 / 0508 / 0511 / benjamincann@gmail.com