Vector Error Correction Model Model Research Articles

The Impact of Cryptocurrency and Electronic Money Use On The Circulating Money Supply and Monetary Stability in Indonesia

The purpose of this research is to analyze the long-term and short-term impacts of the use of cryptocurrency and electronic money on the money supply (M2) in Indonesia, as well as to analyze the long-term and short-term impacts of the use of cryptocurrency and electronic money on monetary stability (exchange rates) in Indonesia. The research method used is quantitative descriptive analysis with the Vector Error Correction Model (VECM) using the Eviews application and secondary data in the form of monthly data from 2011 to 2023 obtained from the official websites of Bank Indonesia and Finance. This study utilizes data on cryptocurrency transaction values and electronic money transaction values in Indonesia by analyzing the VECM model, which can observe the long-term and short-term impacts of the use of digital money on the money supply and monetary stability, in this case, viewed through the Indonesian exchange rate. The research results indicate that there is a one-way causality between electronic money and cryptocurrency, but not the other way around, and there is a one-way causality between the money supply and cryptocurrency, with the money supply as the dependent variable influencing cryptocurrency. The results of the VECM estimation indicate that in the long term, electronic money has a negative and sifnificant impact on the money supply. Meanwhile, the short-term estimation shows that both cryptocurrency and electronic money significantly influence the money supply, but their effects are dynamic and vary based on different lags. The long-term estimation with the exchange rate as the dependent variable shows that cryptocurrency does not have a significant impact on the exchange rate, while in the short term, both cryptocurrency and electronic money have a negative and significant effect on the exchange rate.

The Impact of Cryptocurrency and Electronic Money Use On The Circulating Money Supply and Monetary Stability in Indonesia

The purpose of this research is to analyze the long-term and short-term impacts of the use of cryptocurrency and electronic money on the money supply (M2) in Indonesia, as well as to analyze the long-term and short-term impacts of the use of cryptocurrency and electronic money on monetary stability (exchange rates) in Indonesia. The research method used is quantitative descriptive analysis with the Vector Error Correction Model (VECM) using the Eviews application and secondary data in the form of monthly data from 2011 to 2023 obtained from the official websites of Bank Indonesia and Finance. This study utilizes data on cryptocurrency transaction values and electronic money transaction values in Indonesia by analyzing the VECM model, which can observe the long-term and short-term impacts of the use of digital money on the money supply and monetary stability, in this case, viewed through the Indonesian exchange rate. The research results indicate that there is a one-way causality between electronic money and cryptocurrency, but not the other way around, and there is a one-way causality between the money supply and cryptocurrency, with the money supply as the dependent variable influencing cryptocurrency. The results of the VECM estimation indicate that in the long term, electronic money has a negative and sifnificant impact on the money supply. Meanwhile, the short-term estimation shows that both cryptocurrency and electronic money significantly influence the money supply, but their effects are dynamic and vary based on different lags. The long-term estimation with the exchange rate as the dependent variable shows that cryptocurrency does not have a significant impact on the exchange rate, while in the short term, both cryptocurrency and electronic money have a negative and significant effect on the exchange rate.

FOREIGN EXCHANGE RATE PREDICTION OF INDONESIA'S LARGEST TRADING PARTNER BASED ON VECTOR ERROR CORRECTION MODEL

Foreign exchange rates from the currencies of trading partners are a critical element in the development of Indonesia's economic landscape. As an active country in international trade, Indonesia's economic health is highly dependent on trade partnerships, movements, and interactions of foreign exchange rates from Indonesia's main trading partners. To achieve economic stability, Bank Indonesia intervenes in the foreign exchange market to keep the Rupiah exchange rate within a reasonable range. Indonesia is committed to achieving several points in the Sustainable Development Goals (SDGs), such as point 17, which emphasizes partnerships, and point 8, which underlines inclusive and sustainable economic growth. This commitment is an important factor in Indonesia's economic development. Therefore, it is necessary to predict the exchange rate value of Indonesia's largest trading partners considering these SDG aspects. In this study, the Vector Error Correction Model (VECM) was used to predict the foreign exchange rate of Indonesia's largest trading partners. The data used in this study is secondary data obtained from the investing.com webpage, comprising weekly data from January 2021 to November 2023. The foreign exchange rates of Indonesia's largest trading partners have a cointegration relationship, indicating long-term relationships and similarities in movements. The best model identified is VECM (1), with a very accurate MAPE value of 3.29%. The Impulse Response Function (IRF) analysis shows that the Chinese Yuan responds variably to different currencies, stabilizing over time. Variance Decomposition reveals that short-term fluctuations in the Chinese Yuan are primarily influenced by itself (87.89%) and significantly by the Singapore Dollar, South Korean Won, and Taiwan Dollar. The Granger Causality Test indicates that the Philippine Peso influences 11 other exchange rates, refining the VECM model and improving prediction accuracy. Indonesia is expected to build economic collaborations that can help achieve economic stability.

Fiscal Policy and Economic Growth Nexus: Evidence from Zambia

Purpose: This study examines the short-run and long-run impact of the expansionary fiscal policy on economic growth in Zambia from 1991 to 2021. Materials and Methods: The study employs the Vector Error Correction Model (VECM) and Auto Regressive Distributed Lag (ARDL) Models to examine the short-run and long-run impact of total public expenditure, total tax revenue and total public debt time series data on the gross domestic product (GDP) of Zambia from 1991 to 2021. The Keynesian theory informed our study, which presupposes that the unemployment problem might be solved by boosting government spending on consumption and that government spending was an exogenous factor that contributed to economic growth (Keynes, 1936; Singh & Sahni, 1984). Findings: Both the VECM and ARDL Models show the existence of statistically significant long-run cointegration among public expenditure, external debt, tax revenue and GDP. Specifically, VECM estimates show that for a 1% increase, Tax revenues have a positive long-run significant effect on Zambia’s economic growth of 3.36%, while external debt and public expenditure have significant negative effects on the economic growth of 1.17% and 0.003% in Zambia respectively. The ARDL model estimates indicate that, in the short run, an increase in tax revenue of 1% increases GDP growth by 1.92%, while a rise in government expenditure and external debt by 1% results in a decline in Zambia’s economic growth by 0.003% and 6.14% respectively. Implications to Theory, Practice and Policy: Given the foregoing findings, the therefore study recommends that the Government of Zambia should widen the tax base to mobilize more tax revenues and reduce the budget deficits emanating from high government expenditures. External public debt should also be reduced.

THE IMPACT OF FOREIGN DIRECT INVESTMENT ON THE CHANGE IN THAILAND'S INDUSTRIAL STRUCTURE

<p>Foreign direct investment (FDI) has played a significant role in Thailand's economic development. This study utilizes the Vector Error Correction Model (VECM) to analyse the impact of FDI on the change in Thailand's industrial structure. Based on the analysis results of the VECM model, it is evident that the impact of FDI on these changes is slightly smaller than that of the industrial structure itself; it is also positive. Both the industrial structure itself and FDI have long-term positive promoting effects on the change in Thailand's industrial structure. Furthermore, the impact of GDP per capita, imports and exports on changes in the industrial structure is subject to volatility. Initially, GDP per capita, imports, and exports had a short-term positive promoting effect on Thailand's industrial structure change. However, in the long term, the growth of these two economic factors constrains the change in Thailand's industrial structure.</p><p> </p><p><strong>JEL</strong>: F21; O14; O53; C32; L52</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0777/a.php" alt="Hit counter" /></p>

Hedge Fund Returns and Total Factor Productivity

This study explores whether hedge funds’ investment behavior can predict variations in productivity levels using a structural vector autoregressive model (SVAR) and a vector error correction model (VECM). As <i>informed</i> traders in the stock market with superior skills, hedge funds may quickly capture news shocks regarding future production growth in advance. With quarterly series of <i>TFP</i> provided by John Fernald (2014) and hedge fund index (<i>HFI</i>) returns obtained from the Credit Suisse/Tremont database, I find a contemporaneous correlation coefficient of 0.9791 between two endogenous variables over the sample period from 1Q:1994 to 2Q:2023, indicating a high degree of similarity in their movements. A Granger Causality Test rejects the hypothesis, “ ΔLn(<i>HFI</i>) does not Granger Cause Δ<i>TFP</i>”, suggesting that the information inferred from the hedge fund index are valuable in predicting future economic productivity. Finally, the forecast error variance decompositions using the VECM model indicate that over 65% of the variation in Δ<i>TFP</i> even after 20 quarters can be attributed to a shock to the Δ<i>Ln</i>(<i>HFI</i>) .

The relationship between the inflation rate and the unemployment rate in Poland and their long-term associations with selected macroeconomic variables

The purpose of this paper is to examine the relationship between the inflation and unemployment rates in Poland and to analyze their long-term relationships with selected macroeconomic variables: the WIG20 index, consisting of the 20 largest Polish companies noted on Warsaw Stock Exchange, the USD/PLN exchange rate, the Brent crude oil index, and the interest rate on 10-year government bond yields. The main objective of the study is to determine the relationship between inflation and unemployment. In this study, a vector error correction model (VECM) was used to study long-run dependence. The impulse response function and forecast error variance decomposition were also used to examine the interactions between variables. There is one long-run relationship between the factors studied. Both the values of the VECM model parameters and the results of the impulse response function indicate that there is a negative relationship between inflation and unemployment in the short term. In the long term, there is a positive relationship, resulting in the stagflation phenomenon.

Modeling the Farmer Exchange Rate in Indonesia Using the Vector Error Correction Model Method

The agricultural sector plays a crucial role in the Indonesian economy. However, the farm sector still has serious problems, including agricultural product prices, which often fall when the harvest supply is abundant. So often, the income obtained is not proportional to the price spent by farmers, which has an impact on decreasing the welfare of farmers. An indicator to observe changes in the interest of Indonesian farmers is the Farmer Exchange Rate Index (NTP). This study aims to form a model and project the welfare level of farmers in Indonesia, focusing on NTP indicators, which are caused by the influence of variables such as inflation, Gross Domestic Product (GDP), interest rates, and the rupiah exchange rate. The method used is the Vector Error Correction Model (VECM), used when there are indications that the research variables do not show stability at the initial level and there is a cointegration relationship. The results of this study show that in the long run, significant factors affecting NTP are inflation, interest rates, and the rupiah exchange rate. Meanwhile, in the short term, the variables that have an impact are GDP and the rupiah exchange rate. The resulting VECM model shows a MAPE error rate of 1.79%, indicating excellent performance, as the MAPE error rate is below 10%. The implication of this research is provides information related to NTP projection that can be used to formulate strategies to strengthen Indonesia's agricultural sector.

The Relationship Between Financial Stability and Credit Supply: Evidence from Iraq

Objective: The study aims to investigate the impact of bank credit on financial stability in the Iraqi economy and to reveal trends in bank credit in the Iraqi economy.
 
 Method: We use the Vector Error Correction Model (VECM) and the Granger causality to assess the role of bank credit in strengthening financial stability in the Iraqi economy for the period 2006 to 2021.
 
 Result: We provide evidence on the presence of a long-term relationship between bank supply and all indicators of financial stability. In the short term, a positive association is documented between bank credit and capital adequacy ratio.
 
 Conclusion: Several conclusions could be drawn from our analysis of the evolution of bank credit and financial stability indicators, as well as from our empirical investigation of the relationship between them. The bank credit has been steadily increasing from 2006 to 2021. The capital adequacy ratio (CAR) was very high over the period 2006-2021, exceeding the standard ratio set by the Central Bank of Iraq, which is 12%. This reflects the soundness of the banking system in Iraq. However, the return on equity declined from 27.1% in 2010 to 5.15% at the end of the study period in 2021. Using the VECM model and Granger causality, we provide evidence that bank credit affects financial stability through multiple channels. Thus, the Iraqi central bank should use the tool of credit supply to enhance financial stability.

Modeling and Analysis Data Production of Oil, and Oil and Gas in Indonesia by Using Threshold Vector Error Correction Model

Data in the fields of finance, business, economics, agriculture, the environment and weather are commonly in the form of time series data. To analyze time series data that involves more than one variable (multivariate), vector autoregressive (VAR) models, vector autoregressive moving average (VARMA) models are generally used. If the variables discussed have cointegration, then the VAR model is modified into a vector error correction model (VECM). The relationship between short-term dynamics and deviation in the VECM model is assumed to be linear. If there is a nonlinear relationship between short-term dynamics and deviation, then a threshold vector error correction model (TVECM) can be used. The variables used in this research consist of oil production and Indonesian oil and gas production from January 2019 to March 2021. The research results show that the best model for data on oil production and oil and gas production is the TVECM 2 Regime model. Based on the TVECM 2 Regime model, further analysis, namely Granger causality and Impulse Response Function are discussed.

Johansen’s Cointegration Analysis of Some Factors of Economic Growth and Exports of Products from the Republic of Azerbaijan to Ukraine

In the present paper, a Johansen’s cointegration analysis is carried out considering the volume of exports from Azerbaijan to Ukraine, GDP per capita of Ukraine, the openness of the economy of Ukraine and the economically active population of Azerbaijan for the period 1996-2022, also a comparative analysis of the above indicators is carried out, the characteristics of joint short-term and long-term movements are determined. In the course of research the author used the methodology of modified gravitational modeling, the econometric methodology of time series analysis, including tests for checking stationarity, the extended Dickey-Fuller test, the Granger test for the detection of causality, determining the cointegration dependence using the Johansen’s test, a Vector Error Correction Model (VECM) has been built. First, a basic modified model of gravity has been built, and the statistical adequacy of this model has been checked. A comparative analysis of two regression models was carried out after the inclusion of the trend component in one of them. The dynamic structure of regression residuals was studied and the test for heteroscedasticity and autocorrelation was carried out. It is shown that the most suitable specification for cointegration is the quadratic trend in the initial levels with a linear trend in cointegration relationships, which has led to the emergence of two cointegration vectors. As a result of the completed analysis, two cointegration relationships are obtained. The results of the impulse response functions, decomposition of dispersions and the VECM model in the form of combinations of two cointegrating vectors with the expected signs of the adjustment integers showed that the economic indicators used in the analysis for the specified period maintain cointegration in the long term with the not stable equilibrium joint movements of the factors under study.

주택시장과 거시경제변수들 간의 장기적 균형관계 연구

This paper was empirically conducted to compare the explanatory power of the Vector Error Correction Model (VECM) and Vector Autoregressive Model (VAR Model) in predicting the long-term equilibrium relationship between housing prices and macroeconomic variables. The analysis period spans from January 1987 to July 2023. The analysis results are as follows. First, it is found that there exists one cointegration relationship among housing prices, stock prices, liquidity, 5-year yield of government housing bonds, and income. Therefore, the VECM model can be applied to analyze the long- and short-term equilibrium relationship. Secondly, the estimation results of the VECM model revealed the presence of the following long-term relationships: a negative (-) relationship between housing prices and stock prices, a positive (+) relationship with liquidity, a negative (-) relationship with the 5-year yield of government housing bonds, and a positive (+) relationship with income. This aligns with the expected theoretical signs. Thirdly, the Root Mean Square Error (RMSE) and Theil's Inequality Coefficient, which indicate the predictive and explanatory power of the VECM and VAR models, respectively, showed that the VECM model outperformed the VAR model with smaller values for both metrics. Therefore, when a cointegration relationship exists between housing prices and macroeconomic variables, it is necessary to apply the VECM model rather than the VAR model for analysis.

Forecasting PT Triputra Agro Persada Tbk (TAPG) Share Prices Using Multivariate Time Series Analysis

An increase in the price of crude palm oil (CPO) positively affects the share prices of companies engaged in the palm oil industry. PT Triputra Agro Persada Tbk (TAPG) 2021 was recorded as one of the companies with the CPO business that received the most significant capital gain. Prediction or forecasting of stock prices in the future is crucial for investors as a consideration before deciding to invest. Many kinds of research on stock price prediction have been carried out previously using univariate methods. Univariate modeling cannot represent the influence of other variables on stock prices. Forecasting with the influence of other variables can be done with multivariate time series analysis. This study aims to analyze the multivariate time series of TAPG stock prices and the factors that influence them. Based on the research results, data on TAPG stock prices and CPO prices are cointegrated, so the multivariate time series model used is the vector error correction model (VECM). In the VECM model, the optimum lag used is lag 11. In the long run, CPO prices significantly affect TAPG stock prices.

Prediksi Spot Price Komoditas Emas Berjangka dengan Pendekatan Vector Error Correction Model

Time series data usually exhibit non-stationary behavior and involve interrelated variables. Thus, we need a model that can obtain good forecasting results from non-stationary time series data with multivariate variables. The Vector Error Correction Model (VECM) is a multivariate time series model which is a vector form of Vector Autoregressive Regression (VAR) for time series data that are non-stationary and have a cointegration relationship. This research was conducted to model the cointegration relationship in providing clarity on the long-term relationship of the influence of future prices and the Covid-19 pandemic on price movements of gold futures commodities and to predict spot price prediction modeling for gold futures commodities. The results of the research using the VECM (2) model, which is the best model, show that the future price of the gold commodity is quite dominant in influencing the value of the spot price of gold. The Covid-19 variable does not have a significant effect on the spot gold price variable.

Türkiye'de Alabalık Üretimi, Fiyatı ve Sübvansiyon Arasındaki Nedensel İlişkinin Araştırılması

Although there have been trout subsidies in Türkiye for many years, the fact that trout production has been below expectations, especially in recent years, has led to the need to investigate the effects of the subsidies. Thus, the present study was conducted to estimate the long-run relationship among trout production, producer price, and subsidy in Türkiye. The time-series data covering 1984-2016 regarding trout production, sales prices, and subsidies were used. The Vector Error Correction Model (VECM) and Granger Causality Analysis were utilized to estimate the time-dependent causality relationship among the variables. The VECM results, which estimate the existence of a long-run relationship among the variables, revealed that 46.8% of the long-run deviations in the price and subsidy variables will be corrected in the next period. The analysis results indicated that sales prices affect trout production negatively in the long term. Granger causality analysis indicated that the prior period values of production and price variables were the reason for the changes in the subsidy variable. In the short-term VECM model, it was revealed that changes in production and prices positively affect the subsidy. A percent increase in production and price increased the subsidy amount by 1.79 and 3.14 percent, respectively. To increase trout production, the current subsidy policy should be revised to improve the infrastructure and capacity of aquaculture farms. Subsidies for the fishery sector should also be increased in real terms to achieve their objectives.

Natural Gas–Electricity Price Linkage Analysis Method Based on Benefit–Cost and Attention–VECM Model

In recent years, frequent linkage events of natural gas and electricity prices have seriously affected the stability of the energy supply market. In view of the increasingly close linkage relationship between gas and electricity prices, a gas–electricity price linkage analysis method considering the benefit–cost and price transmission of gas units is proposed. Firstly, a benefit–cost model of gas units based on risk premium theory is constructed to reflect the willingness of gas units to offer prices in different market environments. Secondly, a vector error correction model (VECM)-based gas–electricity price conduction analysis method was proposed to study the long-term co-integration relationship between gas–electricity prices and the short-term fluctuation conduction process. Then, aiming at the problem of delay in price transmission, a time-delay analysis method of gas–electricity price based on the attention mechanism is proposed. Finally, the gas price index and average gas power price data of China from August 2020 to November 2022 are used to verify the validity of the proposed model; the numerical results show that the proposed method can determine the long-term stable fitting relationship between gas and electricity prices, and can also analyze and judge the conduction direction of gas and electricity price fluctuations in different time periods.

The covid-19 pandemic caused vulnerability and survival strategies of star hotels in Nepal

Background: The concept of banking sector development is multi-dimensional, and it is difficult to establish a single description for it because it is an interconnected process that encompasses increases in the number and quality of financial services. Objectives: The objective of the study is to examine the impact of macroeconomic determinants on banking sector development in Nepal. Methods: The study applied the vector error correction model (VECM) approach technique with economic time series data ranging from 1995 to 2020. The study employed the VECM model to avoid the spurious regression problem in the construction of contemporary time series econometrics. The co-integration analysis is used in the study to determine the long-run equilibrium relationship between the macroeconomic variable and the banking sector development of the model. Banking sector development is measured by the arithmetic average of the normalized values of banking depth, banking efficiency, and banking stability. Result: This study reveals that per capita GDP and remittances have a positive and significant impact on the banking sector development. Similarly, government expenditure and stock market capitalization have positive and statistically significant roles to explain banking sector development in Nepal. In addition, it demonstrates that trade openness and inflation have a marginally negative but insignificant impact on banking sector development. Conclusion: There is a long-term equilibrium relationship between macroeconomic variables and banking sector development. Macroeconomic policies and institutional quality play an important role in the banking sector development. Implications: For policymakers since it clarifies the significance of sound macroeconomic policies in the development of the banking industry while taking into account the quality of the existing institutional infrastructure.

Multi-variate Time-series Analysis Using VECM Identifies the Best Set of Exogenous Predictors for Rainfall and Temperature in India: A Data Analytical Approach

The complex nature of climate change with multitude of underlying factors poses a major hindrance in data analysis and decision making by policy makers. Here, we utilize data analytic techniques to identify the best set of climate change indicator variables that could predict precipitation and temperature data for India. The observed values of important climatic parameters namely, rain, maximum temperature, minimum temperature, and mean temperature in India were analyzed along with the observed values of selected socio-environmental indicators featured by WHO for climate change as exogenous variables for a period of 61 years. Data were pre-processed to identify ten exogenous indicators which were then modelled using Vector Error Correction Model (VECM). 1024 VECM models were built and evaluated for the prediction of the four endogenous variables using all possible combinations of the selected indicators. Seven exogenous variables were determined as the best set of indicators based on the AIC of the different models. The model built using the identified variables was compared to others to illustrate the probable impact of this combination of variables. The study thus demonstrates a simple but rational data-driven approach for use in decision making.

The Effect of Public Debt on Economic Growth in Kenya

Using annual data from 1980 to 2019, this study examined the impact of public debt on Kenya's economic growth. Financing Gap theory served as a guide for the investigation. An explanatory research design was adopted in this study. The analysis of the long-run and short-run effects used a customized version of the Vector Error Correction Model (VECM) Model. The R-square value from the VECM model was 58.62, and the Chi-square was 26.913 (p > Chi2 = 0.0494), indicating that the VECM was suitable for parameter estimation. While external debt revealed a coefficient of 0.0003 with a p-value of 0.001 had a significant positive impact on economic growth, domestic debt reported a coefficient of -0.266 with a p-value of 0.019, supporting the debt overhang effect. If the marginal output of an available external debt is greater than or equal to the principal and interest payment, the study found that external debt had a positive impact on the economy of the borrowing country. On the other hand, domestic debt has a significant negative impact on economic growth. The government will be able to develop sound fiscal and monetary policies with the aid of the study's findings. The study demonstrates that increasing domestic debt levels in Kenya during the study period negatively and significantly impacted economic growth. As a result, this study suggests that the Kenyan government reduce domestic borrowing to avoid the crowding out effect, which has a detrimental long-term impact on economic growth. The government should use the borrowed domestic money to diversify the economy's productive base. This will boost long-term economic growth, widen the base of taxation, and increase the country's ability to pay off its obligations when they are due.

Spatio-Temporal Analysis of Agricultural Labour Wages Using Vector Error Correction Model: An Integrated Approach to Environment and Climate Change

This study attempted to explore the interactive relations among agricultural labour wage rates in five neighbouring Indian states viz., Andhra Pradesh, Karnataka, Tamilnadu Telangana and Chhattisgarh using monthly time series data of 2005-2020. The objective of this study was to examine the degree of integration among wage rates of agricultural labourers in neighbouring states. Integration with outside markets may partly mitigate the costs of climate change, as individuals respond to warming temperature by migrating to urban areas and internationally in search of employment. We built vector error correction model (VECM) by conducting stationarity test and cointegration test. The Granger Causality test was employed to check whether the wage rates among different states influence each other. For building the VEC Model, the complete data set (180 data points) was split into training (168 data points) and testing (12 data points) data sets. The nonstationarity of the data was established by the Augmented Dickey Fuller test. For the purpose of forecasting, VECM (1) was built and tested for goodness of fit using Mean Absolute Percentage Error (MAPE) which were found to be < 10% for all the states suggesting good fit of the VECM model. A growing body of literature suggests that the economic costs of climate change may be substantial and far‐reaching, impacting agriculture, mortality, labour productivity, economic growth, civil conflict and migration.