Exchange rate and pricing behavior: Comparison of Taiwan with Japan for manufacturing industries

Tsui, Hsiao-Chien

doi:10.1016/j.japwor.2006.12.001

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…Dhasmana (2013) investigated the determinants of the exchange rate in Indian manufacturing firms, and the result indicates exchange rate movements significantly affect industrial productivity. Tsui (2008) studied exchange rates and profit margins in terms of export rate, imported input rate, and external exposure index, which affect revenue and costs in manufacturing sectors, in order to find a relationship. He found that the appreciation of the home currency in Taiwan has a positive impact on the manufacturing sector when profit margins are high.…”

Section: Manufacturing Sector In Africamentioning

confidence: 99%

“…Swift (2007) examined the relationship and found that when the real exchange rate appreciates, the number of firms that stay in the market goes down, which also reduces the growth of firm productivity. On the other hand, the studies of Dhasmana (2013), Tsui (2008), andSwift (2007) show the impact of exchange rates is ambiguous. Dhasmana (2013) investigated the determinants of exchange rates in Indian manufacturing firms, and the result indicates exchange rate movements significantly affect industrial productivity.…”

Section: Manufacturing Sector In Africamentioning

confidence: 99%

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Impact of Macroeconomic Factors on Manufacturing Productivity in Ethiopia

2023

IKM

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This study examines the main impacts of macroeconomic factors on manufacturing productivity in Ethiopia using a time series analysis from 1980 to 2020. The study reveals short-term and long-term determinants of manufacturing industry development. The Ethiopian manufacturing sector was able to achieve a vast change in value-added, especially between 1999 and 2020. However, from 1980 to 1999, the growth rate was erratic and slow, and around 1995 it recorded its worst performance. In the short term, the vector error correction model (VECM) results show that the first lag of manufacturing value added and real interest rates greatly impact manufacturing productivity, but other variables have no short-term impact. In the long run, variables such as the balance of payments, external debt, and real interest rates contribute to the positive development of the manufacturing sector, while other variables such as the exchange rate, monetary sector credit, and broad money balance affect manufacturing sector productivity negatively. Therefore, the Ethiopian government should pay due attention to improving the sector. Although the government is making great efforts to establish various industrial parks in the country, it lacks policies and subsidies for specific manufacturing sectors that can produce necessities. As the level of inflation and cost of living in the country intensify, targeting and prioritizing the manufacturing sector's production and productivity will be the best and most recommended solution so far.

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Section: Manufacturing Sector In Africamentioning

confidence: 99%

Section: Manufacturing Sector In Africamentioning

confidence: 99%

Impact of Macroeconomic Factors on Manufacturing Productivity in Ethiopia

2023

IKM

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“…Lisi also has compared the neural networks and chaotic models for exchange rate prediction [26]. Tsui [27] has compared the exchange rate and pricing behaviour between Taiwan and Japan for manufacturing industries. Ghosh [28] has compared the effects of exchange rate regime choice in emerging markets with advanced and low-income nations for 1999-2011.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Four Interval ARIMA-base Time Series Methods for Exchange Rate Forecasting

Khashei

Montazeri²,

Bijari³

2015

IJMSC

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In today's world, using quantitative methods are very important for financial markets forecast, improvement of decisions and investments. In recent years, various time series forecasting methods have been proposed for financial markets forecasting. In each case, the accuracy of time series methods fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. In the literature, Many different time series methods have been frequency compared together in order to choose the most efficient once. In this paper, the performances of four different interval ARIMA-base time series methods are evaluated in financial markets forecasting. These methods are including Auto-Regressive Integrated Moving Average (ARIMA), Fuzzy Auto-Regressive Integrated Moving Average (FARIMA), Fuzzy Artificial Neural Network (FANN) and Hybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH). Empirical results of exchange rate forecasting indicate that the fuzzy artificial neural network model is more satisfactory than other models.

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Exchange rate and pricing behavior: Comparison of Taiwan with Japan for manufacturing industries

Cited by 2 publications

References 16 publications

Impact of Macroeconomic Factors on Manufacturing Productivity in Ethiopia

Impact of Macroeconomic Factors on Manufacturing Productivity in Ethiopia

Comparison of Four Interval ARIMA-base Time Series Methods for Exchange Rate Forecasting

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