scholarly journals FINANCIAL CYCLES IN THE ECONOMY AND IN ECONOMIC RESEARCH: A CASE STUDY IN CHINA

2021 ◽  
Vol 27 (5) ◽  
pp. 1250-1279
Author(s):  
Yong Qin ◽  
Zeshui Xu ◽  
Xinxin Wang ◽  
Marinko Škare ◽  
Małgorzata Porada-Rochoń
Keyword(s):  
Time Series Data ◽  
Singular Spectrum Analysis ◽  
Real Life ◽  
Economic Research ◽  
Series Data ◽  
Time Varying ◽  
Causality Test ◽  
Vector Autoregression Model ◽  
Time Varying Parameter ◽  
Financial Cycles

This work explores the relationship between financial cycles in the economy and in economic research. To this aim, we take China as an empirical example, and an intuitive bibliometric analysis of selected terms concerning financial cycles in economic research is performed first. Both in the economy and in economic research, we then conduct singular spectrum analysis to further isolate and describe the specific length and amplitude of financial cycles for China based on quarterly time-series data. Finally, according to the estimated cycles that detrended by Hodrick-Prescott filter for financial and bibliometric variables, the Granger causality test scrutinizes the results of the first two steps. Moreover, a time-varying parameter vector autoregression model is estimated to quantitatively investigate the time-varying interaction between financial and bibliometric variables. Our study shows that financial cycles have a strong effect on the developments in the financial-related literature. In particular, the 2008 global financial crisis’s impulse intensity is significantly higher than in other periods. Surprisingly, discussions on financial cycles in the literature also have an impact on financial activities in real life. These findings contribute to nascent work on the patterns in financial cycles, thus providing a new and effective insight on the interpretation of financial activities.

scholarly journals Does Equity Market Integration Exist Between Turkey and the Eurozone?

JEJAK ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 29-48
Author(s):  
Berto Usman ◽  
Nega Muhabaw Kassie ◽  
Fitra Wahyudi
Keyword(s):  
Market Integration ◽  
Time Series Data ◽  
Stock Exchange ◽  
Equity Market ◽  
Series Data ◽  
Causality Test ◽  
Stock Market Integration ◽  
Vector Autoregression Model ◽  
Dynamic Relationship ◽  
Residual Matrix

This research investigates the existence of stock market integration between Turkey and the Eurozone. In this study, the performance of Turkey’s stock exchange is proxied by the BIST100, and the EURO STOXX50 is employed as a proxy for the Eurozone index. We hypothesize that there is a dynamic relationship between Turkey and the Eurozone. Methodologically, our research was conducted by employing monthly time series data obtained from EIKON datastream International. In order to demonstrate the extent of equity market integration between Turkey and Eurozone, a vector autoregression model (VAR) was utilized. According to the results, there is no co-integration between these two equity markets. This is in line with the output of residual matrix test, where the correlation between these two market indices was found to be low. However, a Granger causality test indicated that there was a low one-way contribution from Turkey to the Eurozone index during the observation period.

scholarly journals STRUCTURAL BREAKS DAN KETIDAKSTABILAN PERMINTAAN UANG DI INDONESIA

2018 ◽  
Vol 6 (2) ◽  
pp. 47-60
Author(s):  
Deviyantini Deviyantini ◽  
Iman Sugema ◽  
Tony Irawan
Keyword(s):  
Money Demand ◽  
Structural Breaks ◽  
Structural Changes ◽  
Demand Function ◽  
Time Series Data ◽  
Series Data ◽  
Time Varying ◽  
Economic Shocks ◽  
Money Demand Function ◽  
Time Varying Parameter

This research aims to identify the sources of instability of the money demand function (M1 and M2) due to structural changes that occur as a result of economic shocks. These shocks are technically shown by the presence of structural breaks in the data and can lead the parameters non-constancy. The instability of the money demand function was analyzed using the Gregory and Hansen test. The source of instability of the money demand was identified using time varying parameter model. This research used quarterly time series data from 1993Q1 to 2013Q4. The results show that the money demand function (M1 dan M2) is not cointegrated (unstable) and the source of the instability is exchange rate variable. Keywords: Stability money demand, Structural breaks, Time varying parameter model

scholarly journals STRUCTURAL BREAKS DAN KETIDAKSTABILAN PERMINTAAN UANG DI INDONESIA

2018 ◽  
Vol 6 (2) ◽  
pp. 47-60
Author(s):  
Deviyantini Deviyantini ◽  
Iman Sugema ◽  
Tony Irawan
Keyword(s):  
Money Demand ◽  
Structural Breaks ◽  
Structural Changes ◽  
Demand Function ◽  
Time Series Data ◽  
Series Data ◽  
Time Varying ◽  
Economic Shocks ◽  
Money Demand Function ◽  
Time Varying Parameter

This research aims to identify the sources of instability of the money demand function (M1 and M2) due to structural changes that occur as a result of economic shocks. These shocks are technically shown by the presence of structural breaks in the data and can lead the parameters non-constancy. The instability of the money demand function was analyzed using the Gregory and Hansen test. The source of instability of the money demand was identified using time varying parameter model. This research used quarterly time series data from 1993Q1 to 2013Q4. The results show that the money demand function (M1 dan M2) is not cointegrated (unstable) and the source of the instability is exchange rate variable. Keywords: Stability money demand, Structural breaks, Time varying parameter model

EMPIRICAL ANALYSIS OF INDIA’S FOREIGN TRADE AND ECONOMIC GROWTH

2020 ◽  
Vol 8 (10) ◽  
pp. 105-111
Author(s):  
Khujan Singh ◽  
Anil Kumar
Keyword(s):  
Time Series Data ◽  
Unit Root Test ◽  
Series Data ◽  
Significant Implication ◽  
Causality Test ◽  
First Order ◽  
Long Run ◽  
Directional Relation ◽  
Augmented Dickey Fuller ◽  
Vecm Granger Causality

The present study is an attempt to examine long run relationship among India’s GDP, Exports and Imports for which yearly time series data from 1995 to 2018 has been collected. Data for India’s GDP has been collected from RBI website and India’s export and import data has been collected form Ministry of Commerce and Industry website. The Augmented Dickey-Fuller unit root test for stationarity found that studied variables become stationary at first order of difference. While, Johnson cointegration test revealed long run cointegration between India’s GDP, exports and imports. The results of VECM Granger causality test exhibited bi-directional relationship between India’s GDP and India’s exports, whereas uni-directional relation has been found between India’s GDP and India’s imports. These results have significant implication for India’s export import policy and to achieve a target of $5 trillion economy till 2024-2025.

scholarly journals Fusing Nature with Computational Science for Optimal Signal Extraction

Stats ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 71-85
Author(s):  
Hossein Hassani ◽  
Mohammad Reza Yeganegi ◽  
Xu Huang
Keyword(s):  
Data Science ◽  
Time Series Data ◽  
Singular Spectrum Analysis ◽  
Hybrid Approach ◽  
Computational Science ◽  
Series Data ◽  
Computationally Efficient ◽  
Forecasting Performance ◽  
Nature Inspired Algorithms ◽  
Optimal Signal

Fusing nature with computational science has been proved paramount importance and researchers have also shown growing enthusiasm on inventing and developing nature inspired algorithms for solving complex problems across subjects. Inevitably, these advancements have rapidly promoted the development of data science, where nature inspired algorithms are changing the traditional way of data processing. This paper proposes the hybrid approach, namely SSA-GA, which incorporates the optimization merits of genetic algorithm (GA) for the advancements of Singular Spectrum Analysis (SSA). This approach further boosts the performance of SSA forecasting via better and more efficient grouping. Given the performances of SSA-GA on 100 real time series data across various subjects, this newly proposed SSA-GA approach is proved to be computationally efficient and robust with improved forecasting performance.

scholarly journals ANALISIS KAUSALITAS KEBIJAKAN FISKAL EKSPANSIF, KEBIJAKAN MONETER EKSPANSIF DAN PERTUMBUHAN EKONOMI DI INDONESIA

2020 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Fadhliah Yuniwinsah ◽  
Ali Anis
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Monetary Policy ◽  
Fiscal Policy ◽  
Time Series Data ◽  
Vector Autoregression ◽  
Series Data ◽  
Vector Autoregression Model ◽  
Autoregression Model

This study examined the causality between expansionary fiscal policy, expansionary monetary policy and economic growth in Indonesia’s using a time series data with vector autoregression model (VAR) in the period of 1969-2018. The results of this study showed that are there is no causality between expansionary fiscal policy and expansionary monetary policy but there one-way relationship between them, it is the expansionary monetary policy gives influence to expansionary fiscal policy. There is no causality between expansionary fiscal policy and economic growth but there one-way relationship between them, It is economic growth gives influence to expansionary fiscal policy. And there is no causality between expansionary monetary policy and economic growth but there one-way relationship between them, it is economic growth gives influence to expansionary monetary policy.

scholarly journals PERAMALAN PRODUK DOMESTIK BRUTO (PDB) SEKTOR PERTANIAN, KEHUTANAN, DAN ‎PERIKANAN MENGGUNAKAN SINGULAR SPECTRUM ANALYSIS (SSA)

2019 ◽  
Vol 8 (1) ◽  
pp. 68-80
Author(s):  
Desy Tresnowati Hardi ◽  
Diah Safitri ◽  
Agus Rusgiyono
Keyword(s):  
Gross Domestic Product ◽  
Spectrum Analysis ◽  
Time Series Data ◽  
Seasonal Pattern ◽  
Singular Spectrum Analysis ◽  
Domestic Product ◽  
Series Data ◽  
Percentage Error ◽  
Singular Spectrum ◽  
Tracking Signal

Forecasting is the process of estimating conditions in the future by testing conditions from the past. One of the forecasting methods is Singular Spectrum Analysis (SSA) which aim of SSA is to make a decomposition of the original series into the sum of a small number of independent and interpretable components such as a slowly varying trend, oscillatory components and a structureless noise. Gross Domestic Product data in the agriculture, forestry, and fisheries sector are time series data with trend and seasonal pattern so that it can be processed using the SSA method. The forecasting process of SSA method uses the main parameter (L) of 21 obtained by the Blind Source Separation (BSS) method. From forecasting, acquired group of 3 groups. Forecasting resulted the value of Mean Absolute Percentage Error (MAPE) is 1.59% and the value of tracking signal is 2.50, which indicates that the results of forecasting is accurate. Keywords: Forecasting, Gross Domestic Product in the agriculture, forestry, and fisheries sector, Singular Spectrum Analysis (SSA)

scholarly journals Globalization Nexus Terrorism: Evidence from Pakistan

2021 ◽  
Vol 7 (1) ◽  
pp. 177-184
Author(s):  
Sabeeha Naseer ◽  
Muhammad Kamran Khan ◽  
Sami Ullah
Keyword(s):  
Foreign Direct Investment ◽  
Direct Investment ◽  
Time Series Data ◽  
Series Data ◽  
Var Model ◽  
Causality Test ◽  
Time Period ◽  
Controlling Variables ◽  
Adf Test ◽  
Auto Regression

The current study investigates nexuses between globalization and terrorism in context of Pakistan. Time series data utilized for time period 1981 to 2017. The data has been taken from the World Governance Indicator (WGI) and Swiss global index (KOF). Augmented Dicky fuller (ADF) test was applied to check out stationary of all variables such as terrorism, globalization, remittances, foreign direct investment and trade. The results of ADF test indicated that all variables were stationary at first difference. For empirical analysis Johnson co-integration and VAR model under causality were applied. The co-integration result shows all variables terrorism, globalization, FDI, remittances and trade are not co-integrated. Vector Auto Regression (VAR) Model under causality test shows that Globalization is causing factor of terrorism. While, other controlling variables such as remittances cause globalization, foreign direct investment and trade.

scholarly journals The Nexus between Export and Economic Growth in Tanzania

2019 ◽  
Vol 1 (2) ◽  
pp. p95
Author(s):  
Romanus L. Dimoso (PhD, Economics) ◽  
UTONGA, Dickson (MSc. Economics)
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Series Data ◽  
Test Results ◽  
Causality Test ◽  
Goods And Services ◽  
Long Run ◽  
Causality Tests ◽  
The Government ◽  
Econometrics Analysis

This study explored the causal relationship between exports and economic growth in Tanzania. It analyzed time series data for the period of 1980 to 2015. Economic growth is measured in terms of growth per cent while exports are measured in percentage change of goods and services sold abroad. Econometrics analysis was employed in the due course. Such procedures as testing for the presence of unit root, co-integration and causality were done. Furthermore, the Johansen co-integration and Granger causality tests were employed to examine the long-run relationship among variables. The results of co-integration indicate the existence of one co-integrating equation. The causality test results exhibited causality which runs from economic growth to exports. The results conclude that, in the long run, there is a relationship between exports and economic growth in Tanzania. This study recommends the Government to make efforts to improve exports and eventually, in the long-run, rejuvenating the economy.

scholarly journals Islamic Finance and Economic Growth Nexus: An Econometric Analysis

2019 ◽  
Vol 2 (1) ◽  
pp. 11-22
Author(s):  
Kashif Raza ◽  
Rashid Ahmad ◽  
Muhammad Abdul Rehman Shah ◽  
Muhammad Umar
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Trade Openness ◽  
Unit Root Test ◽  
Least Square ◽  
Series Data ◽  
Islamic Finance ◽  
Causality Test ◽  
Economic Agents ◽  
Fixed Capital Formation

Researchers have written chain of research papers about the dynamics of financial development and economic growth. The financial capital plays a productive role when it delivers to economic agents who are facing shortage or excess of funds.  This study explores the linkages among Islamic financing and economic growth for Pakistan, by using annual time series data from 2005-2018. Islamic banks’ financing funds used as a proxy of Islamic financing, Gross Domestic Product (GDP), Gross Fixed Capital Formation (GFCF), labor force (LF),Broad money(M) and Trade openness (TO) to presents real sector of an economy. For the exploration, the unit root test, Ordinary least square technique and Granger causality test are applied. The results validate a substantial causal relationship of Islamic financing and GDP, which supports the Schumpeter’s supply-leading view. The results indicate that Islamic finance contributed towards economic growth.  

Sign in / Sign up

Export Citation Format

Share Document