scholarly journals IMPACT OF EXCHANGE RATE FLUCTUATIONS ON DOMESTIC INVESTMENT IN NIGERIA (1986-2017)

2020 ◽  
pp. 40-46
Author(s):  
UDEH Samson Okey ◽  
EDEH Chukwudi Emmanuel
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Foreign Exchange Market ◽  
Time Series Data ◽  
Series Data ◽  
Annual Data ◽  
Financial Deepening ◽  
Domestic Investment ◽  
Exchange Rate Fluctuations ◽  
The Impact

The major objective of this study is to analyze the impact of exchange rate fluctuations on domestic investment in Nigeria, using annual data covering the period 1986–2017. Time series data on domestic investment, gross domestic product, interest rate, financial deepening and exchange rate were obtained from the Central Bank of Nigeria Statistical Bulletin 2017 edition. The technique of Autoregressive distributed lagged modeling (ARDL) is adopted to examine the time series data. Findings reveal that the relationship between exchange rate fluctuations and domestic investment in Nigeria is negative. However, exchange rate fluctuation has no significant impact on domestic investment in the long run p(t) value 0.6201> 0.05) and short run p(t) value 0.6244 > 0.05). The study suggests that monetary authorities in Nigeria should strive for currency swap agreements with other nations that trade heavily with Nigeria, for example, India, Turkey, United Kingdom. This would help in mitigating the effects of currency fluctuations in the foreign exchange market. KEYWORDS: Domestic investment, Exchange rate fluctuations, ARDL, GDP

Impact of Globalization on Economic Growth in Vietnam: An Empirical Analysis

2018 ◽  
pp. 32-47 ◽  
Author(s):  
Dat Tho Tran ◽  
Van Thi Cam Nguyen
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Foreign Direct Investment ◽  
Exchange Rate ◽  
Empirical Analysis ◽  
Direct Investment ◽  
Time Series Data ◽  
Series Data ◽  
Balance Of Trade ◽  
The Impact

This study aims at investigating the impact of globalization on economic growth in the case of Vietnam. Empirical analysis is done by using time series data for the period from 1995 to 2014. The paper tested the stationary cointegration of time series data and utilized the error correction modeling technique to determine the short run relationships among economic growth, globalization, foreign direct investment, balance of trade and exchange rate variables. Then, the long run relationship between economic growth and the variables representing economic integration were estimated by ordinary least square. The results show that globalization, measured by the KOF index, promotes economic growth and Vietnam has gained from integrating into the global economy. The overall index of globalization had positively and significantly impacted the economic growth in Vietnam. The results also indicated that economic globalization had a significantly positive effect on economic growth in the period examined. The study further revealed that foreign direct investment and the exchange rate affect economic growth positively whereas balance of trade affects economic growth negatively.

scholarly journals Determinants of private domestic investment in Palestine: time series analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ibrahim M. Awad ◽  
Ghada K. Al-Jerashi ◽  
Zaid Ahmad Alabaddi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Private Investment ◽  
Critical Issue ◽  
Background Information ◽  
Series Data ◽  
Domestic Investment ◽  
Causality Test ◽  
Content Type ◽  
The Impact

PurposeThis empirical paper aims to examine the impact of interest rate (IR) and political instability (POLINS) on Palestine's domestic private investment.Design/methodology/approachA set of econometric techniques of time series data are adopted to meet the study objectives. They include regression analysis, unit root tests, cointegration test, ARDL & Bound tests, VAR test and Granger causality test.FindingsThe study's primary results complement the neoclassical approach, which states that the IR is negatively associated with domestic private investment. The empirical results reveal that there is no long-run relationship. Also, there is no causality between domestic investment and lending rates. Accordingly, these findings alert policymakers to draw a series of steps to minimize the IR at a minimum to stimulate investment for improved economic growth and development.Practical implicationsThere is still no national currency in Palestine. The Palestinian Monetary Authority (PMA) is advised to set an appropriate ratio of the IR for the currencies-in-circulation in Palestine for boosting investment and economic development.Originality/valueThis paper provides new background information to both policymakers and researchers on the main determinants of investment in Palestine using econometric analysis. Accordingly, this critical issue is required to be examined in Palestine for stimulating investment.

scholarly journals New hybrid fuzzy time series model: Forecasting the foreign exchange market

2020 ◽  
Vol 66 (3) ◽  
pp. 263
Author(s):  
José Eduardo Medina Reyes ◽  
Salvador Cruz Aké ◽  
Agustín Ignacio Cabrera Llanos
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Foreign Currency ◽  
Time Series Data ◽  
Arima Model ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Exchange Market

<span class="fontstyle0">This paper develops the comparison of the volatility prediction of the traditional<br />models (ARIMA, EGARCH, and PARCH), with respect to the Hybrid Fuzzy Time<br />Series and Fuzzy ARIMA Model of Tseng’s and Tanaka’s methodology (FTS-Fuzzy<br />ARIMA Tseng and FTS-Fuzzy ARIMA Tanaka). For this purpose, it applies to the<br />time series of the foreign exchange market to forecast the foreign currency exchange rate of Mexican Pesos against American Dollar, the growth rate of the time series data in a daily format from January 2008 to December 2017, to perform the sample test is used January 2018. The main result is that the models based on fuzzy theory generate a better estimate of the volatility of the foreign exchange rate.</span> <br /><br />

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

Impact of exchange rate and oil price on the yield of sovereign bond and sukuk: Evidence from Malaysian capital market

2017 ◽  
Vol 5 (4) ◽  
pp. 27
Author(s):  
Huda Arshad ◽  
Ruhaini Muda ◽  
Ismah Osman
Keyword(s):  
Exchange Rate ◽  
Capital Market ◽  
Time Series Data ◽  
Oil Prices ◽  
Oil Price ◽  
Series Data ◽  
Sovereign Bond ◽  
Long Run ◽  
Short Run ◽  
The Impact

This study analyses the impact of exchange rate and oil prices on the yield of sovereign bond and sukuk for Malaysian capital market. This study aims to ascertain the effect of weakening Malaysian Ringgit and declining of crude oil price on the fixed income investors in the emerging capital market. This study utilises daily time series data of Malaysian exchange rate, oil price and the yield of Malaysian sovereign bond and sukuk from year 2006 until 2015. The findings show that the weakening of exchange rate and oil prices contribute different impacts in the short and long run. In the short run, the exchange rate and oil prices does not have a direct relation with the yield of sovereign bond and sukuk. However, in the long run, the result reveals that there is a significant relationship between exchange rate and oil prices on the yield of sovereign bond and sukuk. It is evident that only a unidirectional causality relation is present between exchange rate and oil price towards selected yield of Malaysian sovereign bond and sukuk. This study provides numerical and empirical insights on issues relating to capital market that supports public authorities and private institutions on their decision and policymaking process.

EFFECTS OF TREND AND PERIODICITY ON THE CORRELATION DIMENSION AND THE LYAPUNOV EXPONENTS

2008 ◽  
Vol 18 (12) ◽  
pp. 3679-3687 ◽  
Author(s):  
AYDIN A. CECEN ◽  
CAHIT ERKAL
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Lyapunov Exponents ◽  
Correlation Dimension ◽  
Time Series Data ◽  
Logistic Map ◽  
Model Identification ◽  
Series Data ◽  
Largest Lyapunov Exponent ◽  
The Impact

We present a critical remark on the pitfalls of calculating the correlation dimension and the largest Lyapunov exponent from time series data when trend and periodicity exist. We consider a special case where a time series Zi can be expressed as the sum of two subsystems so that Zi = Xi + Yi and at least one of the subsystems is deterministic. We show that if the trend and periodicity are not properly removed, correlation dimension and Lyapunov exponent estimations yield misleading results, which can severely compromise the results of diagnostic tests and model identification. We also establish an analytic relationship between the largest Lyapunov exponents of the subsystems and that of the whole system. In addition, the impact of a periodic parameter perturbation on the Lyapunov exponent for the logistic map and the Lorenz system is discussed.

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

Welfare Use in Japan: Trends and Determinants

2007 ◽  
pp. 88
Author(s):  
Wataru Suzuki ◽  
Yanfei Zhou
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Time Series Data ◽  
Positive Impact ◽  
Series Data ◽  
Negative Effects ◽  
Long Time ◽  
Welfare Use ◽  
The Impact

This article represents the first step in filling a large gap in knowledge concerning why Public Assistance (PA) use recently rose so fast in Japan. Specifically, we try to address this problem not only by performing a Blanchard and Quah decomposition on long-term monthly time series data (1960:04-2006:10), but also by estimating prefecturelevel longitudinal data. Two interesting findings emerge from the time series analysis. The first is that permanent shock imposes a continuously positive impact on the PA rate and is the main driving factor behind the recent increase in welfare use. The second finding is that the impact of temporary shock will last for a long time. The rate of the use of welfare is quite rigid because even if the PA rate rises due to temporary shocks, it takes about 8 or 9 years for it to regain its normal level. On the other hand, estimations of prefecture-level longitudinal data indicate that the Financial Capability Index (FCI) of the local government2 and minimum wage both impose negative effects on the PA rate. We also find that the rapid aging of Japan's population presents a permanent shock in practice, which makes it the most prominent contribution to surging welfare use.

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