scholarly journals Output-inflation Trade-off in the Presence of Foreign Capital: Evidence for Vietnam

2021 ◽  
pp. 227797872097989
Author(s):  
Ly Dai Hung
Keyword(s):  
Foreign Capital ◽  
Time Series Data ◽  
Output Growth ◽  
Capital Inflows ◽  
Series Data ◽  
Var Model ◽  
Data Set ◽  
Trade Off ◽  
Rate Fluctuation ◽  
Foreign Capital Inflows

On one monthly time-series data set of Vietnam economy over 02/2008–09/2018, the Time-Varying-Coefficient VAR model records that the trade-off between inflation and output growth is mitigated by the foreign capital inflows. The inflation is mostly determined by credit supply growth, while output growth is largely driven by foreign direct investment (FDI) capital inflows. A monthly increase of FDI by USD 1 billion can raise 1.77% of monthly output growth rate. The result also holds on accounting for exchange rate fluctuation. JEL Classifications: E31, F15, F36, F43

scholarly journals Dynamic Modelling of Sharia-Based Corporate, Islamic Index and Exchange Rate: VAR Model Application

2020 ◽  
Vol 6 (2) ◽  
pp. 195
Author(s):  
Hasrun Afandi Umpusinga ◽  
Atika Riasari ◽  
Fajrin Satria Dwi Kesumah
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Dynamic Modelling ◽  
Series Data ◽  
Var Model ◽  
Suitable Model ◽  
Data Set ◽  
Dynamic Relationship

Indonesia is one of largest users of sharia-based compliant recently which bring into many concerns how the sharia stocks listing in the most valuable sharia stocks index in Indonesia perform and correlate with other variables, particularly exchange rates. The study aims to analysis the causal relationship and to forecast the performances of sharia-based stocks and its Islamic index in Indonesia along with the volatility of exchange rate. Vector Autoregressive (VAR) model is applied as the method to analyse the multivariate time series as it is believed as the suitable model in predicting such time-series data in the scope of multivariate variables. The finding suggests VAR(1) model is the fitted model as such to both analyse its dynamic relationship and forecast the data set for the next 24 weeks. While the prediction shows the JII has an increasing data, both ANTM and EXR are predicted to have a stable volatility. In addition, granger causality defines variables to have effect in its respective variables, and IRF describes the shocks in one variable cause another variable is relatively difficult in reaching its zero condition in short-term period.

scholarly journals Stock market development, foreign capital inflows and economic growth in Zimbabwe: A multivariate causality test

2012 ◽  
Vol 9 (2) ◽  
pp. 313-322
Author(s):  
Kunofiwa Tsaurai ◽  
Nicholas M. Odhiambo
Keyword(s):  
Economic Growth ◽  
Stock Market ◽  
Time Series Data ◽  
Capital Inflows ◽  
Market Development ◽  
Series Data ◽  
Causality Test ◽  
Stock Market Development ◽  
Foreign Capital Inflows ◽  
Variable Bias

In this study we examine the dynamic nexus between stock market development and economic growth – using time-series data from Zimbabwe. The causal relationship between stock market development and economic growth has been a subject of extensive debate in recent years. In an attempt to address the omission-of-variable bias, which has not been addressed by many previous studies, we have incorporated savings as a third variable in the bivariate setting between stock market development and economic growth – thereby creating a multivariate simulation. The study uses the Johansen–Juselius (Johansen and Juselius, 1990) (maximum likelihood) and a dynamic specification model to examine this linkage. The empirical results reveal that there is a distinct causal flow from stock market development to economic growth – without any feedback in Zimbabwe. The results also show that there is a unidirectional causal flow from savings to economic growth, and from stock market development to savings.

Is stock market sensitive to foreign capital inflows and economic growth?

2015 ◽  
Vol 8 (3) ◽  
pp. 142-164 ◽  
Author(s):  
Syed Ali Raza ◽  
Syed Tehseen Jawaid ◽  
Sahar Afshan ◽  
Mohd Zaini Abd Karim
Keyword(s):  
Economic Growth ◽  
Stock Market ◽  
Foreign Capital ◽  
Capital Inflows ◽  
Series Data ◽  
Content Type ◽  
Market Capitalization ◽  
Long Run ◽  
Foreign Capital Inflows ◽  
Stock Market Capitalization

Purpose – The purpose of this study is to investigate the impact of foreign capital inflows and economic growth on stock market capitalization in Pakistan by using the annual time series data from the period of 1976 to 2011. Design/methodology/approach – The autoregressive distributed lag bound testing cointegration approach, the error correction model and the rolling window estimation procedures have been performed to analyze the long run, short run and behavior of coefficients, respectively. Findings – Results indicate that foreign direct investment (FDI), workers’ remittances and economic growth have significant positive relationship with the stock market capitalization in long run as well as in short run. Results of the dynamic ordinary least square and the fully modified ordinary least square suggest that the initial results of long-run coefficients are robust. Results of variance decomposition test show the bidirectional causal relationship of FDI and economic growth with stock market capitalization. However, unidirectional causal relationship is found in between workers’ remittances and stock market capitalization. Practical implications – It is suggested that in Pakistan, investors can make their investment decisions through keeping an eye on the direction of the considered foreign capital inflows and economic growth. Originality/value – This paper makes a unique contribution to the literature with reference to Pakistan, being a pioneering attempt to investigate the effects of foreign capital inflows and economic growth on stock market by using long time series data and applying more rigorous techniques.

scholarly journals Impact of Foreign Capital Inflows and Money Supply on Exchange Rate: A Case Study of Pakistan

2017 ◽  
Vol 3 (1) ◽  
pp. 83-90 ◽  
Author(s):  
Sajid Ali ◽  
Raima Nazar
Keyword(s):  
Foreign Direct Investment ◽  
Exchange Rate ◽  
Foreign Aid ◽  
Money Supply ◽  
Direct Investment ◽  
Foreign Capital ◽  
Capital Inflows ◽  
Series Data ◽  
Foreign Capital Inflows ◽  
The Impact

The study attempts to examine the impact of foreign capital inflows and money supply on exchange rate of Pakistan. For this purpose we have undertaken time series data for the period of 1973-2016. Annual data for the period 1973-2016 is used, taken from Economic Survey of Pakistan (various issues) and International Financial Statistics (IFS). The main variables used in our analysis are exchange rate, openness, workers' remittances, foreign direct investment, foreign aid and money supply. Simple Linear Regression model with ordinary least method (OLS) is used to analyse the results. Money supply is positively and significantly related to exchange rate. Worker's remittances (WREM), foreign aid (FAID), foreign direct investment. (FDI) and openness (OPP) are negatively and significantly related to exchange rate.  The study shows that foreign capital inflows and workers' remittances significantly appreciate the exchange rate in the case of Pakistan.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

The Effects of Oil Prices Changes on Output Growth and Inflation: Evidence from Turkey

2013 ◽  
Vol 5 (11) ◽  
pp. 730-739 ◽  
Author(s):  
Pelin ÖGE GÜNEY
Keyword(s):  
Time Series Data ◽  
Output Growth ◽  
Oil Price ◽  
Series Data ◽  
Negative Effects ◽  
Price Changes ◽  
Asymmetric Effects ◽  
Price Increases ◽  
Causality Tests ◽  
The Impact

This paper investigates the effects of oil price changes on output and inflation for the case of Turkey using monthly time series data for the period 1990:1–2012:3. Recent studies suggest that oil price changes may have asymmetric effects on the macroeconomic variables. To account for asymmetric effects, we decompose oil price changes into positive and negative parts following Hamilton (1996). Our results show that while oil price increases have clear negative effects on output growth, the impact of oil price decline is insignificant. Similarly, oil price increases have positive and significant effects on inflation. However, oil price declines have not a significant effect on inflation. The Granger causality tests also support these results.

Does Tax Increment Financing Pass the “But-for” Test in Missouri?

2019 ◽  
Vol 33 (3) ◽  
pp. 187-202
Author(s):  
Ahmed Rachid El-Khattabi ◽  
T. William Lester
Keyword(s):  
Economic Development ◽  
Kansas City ◽  
Time Series Data ◽  
Local Level ◽  
Conclusive Evidence ◽  
Series Data ◽  
Tax Increment Financing ◽  
Data Set ◽  
Development Indicators ◽  
The Impact

The use of tax increment financing (TIF) remains a popular, yet highly controversial, tool among policy makers in their efforts to promote economic development. This study conducts a comprehensive assessment of the effectiveness of Missouri’s TIF program, specifically in Kansas City and St. Louis, in creating economic opportunities. We build a time-series data set starting 1990 through 2012 of detailed employment levels, establishment counts, and sales at the census block-group level to run a set of difference-in-differences with matching estimates for the impact of TIF at the local level. Although we analyze the impact of TIF on a wide set of indicators and across various industry sectors, we find no conclusive evidence that the TIF program in either city has a causal impact on key economic development indicators.

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

Foreign capital inflows and economic growth: the experience of low-income countries in Sub Saharan Africa

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Atif Awad
Keyword(s):  
Low Income ◽  
Foreign Capital ◽  
Ordinary Least Squares ◽  
Capital Inflows ◽  
Sub Saharan Africa ◽  
Low Income Countries ◽  
Content Type ◽  
Long Run ◽  
Foreign Capital Inflows ◽  
The Impact

Purpose This paper aims to investigate the long-run impact of selected foreign capital inflows, including aid, remittances, foreign direct investment (FDI), trade and debt, on the economic growth of 21 low-income countries in the Sub Saharan Africa (SSA) region, during the period 1990–2018. Design/methodology/approach To obtain this objective and for robust analysis, a parametric approach, which was dynamic ordinary least squares, and a non-parametric technique, which was fully modified ordinary least squares, were used. Findings The results of both models confirmed that, in the long run, trade and aid affected the growth rate of the per capita income in these countries in a positive way. However, external debt seemed to have an adverse influence on such growth. Originality/value First, this is the initial study that has addressed this matter across a homogenous group of countries in the SSA region. Second, while most of the previous studies regarding capital inflows into the SSA region have focused on the impact of only one or two aspects of such foreign capital inflows on growth, the present study, instead, examined the impact of five types of foreign capital inflows (aid, remittances, FDI, trade and debt).

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