Vector Error Correction Model Model Research Articles

Dynamic Modeling and Forecasting Data Energy Used and Carbon Dioxide (CO2)

The model of Vector Autoregressive (VAR) with cointegration is able to be modified by Vector Error Correction Model (VECM). Because of its simpilicity and less restrictions the VECM is applied in many studies. The correlation among variables of multivariate time series also can be explained by VECM model, which can explain the effect of a variable or set of variables on others using Granger Causality, Impulse Response Function (IRF), and Forecasting. In this study, the relationship of Energy Used and CO2 will be discussed. The data used here were collected over the year 1971 to 2018. Based on the comparison of some criteria: Akaike Information Criterion Corrected (AICC), Hannan-Quin Information Criterion (HQC), Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC) for some VAR(p) model with p= 1,2,3,4,5, the best model with smallest values of AICC, HQC, AIC and SBC is at lag 2 (p= 2). Then the best model found is VECM (2) and further analysis such as Granger Causality, IRF, and Forecasting will be based on this model.

Analysis of Some Energy and Economics Variables by Using VECMX Model in Indonesia

Time series modeling analysis is one of the methods to forecast based on past data and conditions. The analytical tool that is commonly used to forecast multivariate time series data is the Vector Autoregressive (VAR) model. However, when the variables have cointegration and stationary at the first difference value, then the VAR model is modified into the Vector Error Correction Model (VECM). In VECM, all variables can be used as endogenous variables. If exogenous variables are involved in the VECM model, then the model is called as Vector Error Correction Model with Exogenous variables (VECMX). In the present study, a time series modeling analysis was used to analyze the price of gasoline, the money supply in a broad sense (M2), oil and gas exports, and consumption imports over the years from 2012 to 2020. By using information on the criteria of Akaike Information Criterion Corrected, Hannan–Quinn Criterion, Akaike Information Criterion, and Schwarz Bayesian Criterion, the best VAR(p) model is obtained with order 3, or lag 3. Based on the VAR(3) model, the cointegration test is conducted, and the result shows that there is a long-term relationship among variables, namely, there is a cointegration relationship between variables with rank = 1. Based on the cointegration rank = 1 and the smallest value of the information criteria and comparison of some candidate best models, namely, VECMX(2,1), VECMX(2,2), VECMX(3,1), VECMX(3,2), and VECMX(4,1), we found that the best model is VECMX(3,1) with lag 3 for endogenous variables and lag 1 for exogenous variables. Based on this best model, further analysis of Granger causality, Impulse Response Function (IRF), and forecasting is discussed.

Asymmetric Price Transmission: A Case of Wheat in India

In the present paper, horizontal and vertical integration was carried out on the wholesale and retail prices of wheat in the major markets of India. On confirming cointegration between the wholesale and retail prices of wheat in all needs, the vector error correction model (VECM) was applied to find the speed of adjustment in the corresponding price channel. The results revealed that price signals are transmitted across regions, indicating that price changes in one market are consistently related to price changes in markets and can influence the prices in other markets. In addition to studying cointegration, threshold autoregressive (TAR) and Momentum TAR (MTAR) models were applied to test for asymmetric cointegration. Hasen and Seo’s test was used to test for the presence of threshold cointegration. It revealed a significant presence of asymmetric and nonlinear cointegration in many markets. Accordingly, a threshold VECM (TVECM) model with two regimes was applied. The results indicate that the retail price significantly responds to the deviations from the long-run equilibrium compared to the wholesale price.

Empirical evidence of the monetary approach to the exchange rate determinants under a fully flexible regime: the case of Mexico

The purpose of this paper is to examine the empirical evidence on the evolution of the nominal peso-dollar exchange rate based on the monetary approach under a fully flexible exchange rate regime. We use a standardised framework where the uncovered interest rate (UIP) and the purchase power parities (PPP), flexible prices, and a typical demand for real money balances determine prices in the long run. Once we identify that time series of the nominal exchange rate and the fundamental macroeconomic variables are non-stationary, we estimate a vector error-correction model (VECM) and, for comparative purposes, an ARIMA-EGARCH model and an ARIMA-EGARCH model with monetary approach. Models' assessment based on the post estimation results shows that the model with the lowest HRMSE for all the steps-ahead forecast is the ARIMA-EGARCH model followed by the VECM model. Likewise, the lowest HMAE for the first three-steps ahead forecast is the VECM model, followed by the ARIMA-EGARCH model.

Effect of Foreign Aid on Economic Growth and Investment in Ethiopia

This study examined the effect of foreign aid on both economy and investment of Ethiopia from time 1974 to 2014. The empirical analysis has been done using multivariate co integration analysis of both vector autoregressive model (VAR) and (Vector error Correction model (VECM). Both models which enables to capture short run dynamics. VECM model is constructed by restricting long run behavior of endogenous variables for allowing for short run adjustment dynamics. The co integrating vector which is deviation from the long run equilibrium corrected through series of partial short run dynamics, which is known as error correction term The main findings of the study shown foreign aid has a significant positive effect on economic progress in both lengthy run and squat run. On other hands, aid has irrelevant and optimistic effect on gross domestic investment in both extensive time and short run. Further, the findings discovered that there is unidirectional causality among foreign aid to economic growth and foreign aid to gross domestic investment. Based on the findings the study recommends aid should be used to support the shortage of resource gap; also, aid should be focused on growth enhancing sectors as well as poverty reduction policies, then it will rise savings of societies’.

The Impulse Response of Domestic and Foreign Interest Rate in Output, Price, Exchange Rate Model, a Deconstructed Derivation and Economic Calibration of Vector Error Correction Model

How to assess external shocks, whether they are inflationary shocks or interest rate shocks without warning, is crucial to construct smooth and predictable financial market. The purpose of this paper is to analyze the short-run and long-run effects of shocks on an economy under chaotic conditions of uncertainty using the Vector Error Correction Model (VECM). A comprehensive and detailed derivation process is presented for VECM and reveals the dynamics of interest rates and inflation in the face of external shocks, through Cholesky ordering and impulse response decomposition. With a constraint, the VECM model is also used to derive its two applications in well-known theoretical fields, the Fisher model and the uncovered interest rate parity (UIP) theory.

Vector error correction model to analyze energy uses, environmental quality and economic growth during Covid-19 Pandemic

Time series data commonly show are interconnected behaviour and non-stationer interrelated variables, so a model that able to obtain a good forecasting result from a non-stationary multivariate variables time series data are needed. Vector Error Correction Model (VECM) is one of multivariate time series model which is a vector form of Vector Autoregressive Boundary (VAR) for non-stationary time series data and has a cointegration relationship. The purpose of this study is to identify the VECM model in analyzing the relationship between energy use, environmental quality (CO2), and economic growth (GDP) during the Covid-19 pandemic that plagued Indonesia. The results of this study explained energy uses and and environmental quality (CO2) and economic growth (GDP) are interrelated and have a long-term cointegration relationship due to the influence of the Covid-19 pandemic.

Analisis Integrasi Pasar Spasial Harga Daging Sapi di Provinsi Jambi

This study aims to analyze the spatial market integration of beef prices in supporting the stabilization of prices for basic livestock products in Jambi Province. Spatial Market Integration Analysis Beef prices in Jambi Province use secondary data which is weekly time series data for the period 2018 – 2021 (August). The data analyzed in this study is the price of beef in the markets of Jambi City and Bungo Regency. The analytical method used to see the level of spatial integration of the beef market in supporting the stabilization of livestock staples both in the short and long term in Jambi Province is the VAR (Vector Autoregression) / VECM (Vector Error Correction Model) model. The study concludes that the weekly average beef price in Jambi City and Bungo Regency during the period 2018 to 2021 (August) is cointegrated. There is a long-term relationship between weekly average beef prices in Jambi City and Bungo Regency. The VECM model is more accurate in forecasting the weekly average beef price in Jambi City and Bungo Regency in the future.

Faktor Yang Mempengaruhi Tingkat Suku Bunga Deposito Bank Umum Di Indonesia

BBanks are financial institutions involved in the state development process that provide services to the public and have functions, such as raising funds. Banks cannot carry out their tasks properly if there are no funds; one of the ways the banks attract the public to raise funds is the time deposit interest rate. This study aims to determine what factors can affect commercial banks' deposit rates in Indonesia from 2010 to 2019. This research method uses multiple regression analysis techniques using the Vector Error Correction Model (VECM) method using secondary data. The results of this study indicate that the BI rate significantly affects the deposit rate. Inflation significantly affects deposit rates. Meanwhile, economic growth does not affect substantially deposit rates. The estimation results of the VECM model show that there is a significant effect in the long run but not substantial in the short term.

Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence.

BackgroundCOVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment.ObjectiveThe goal of this study was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census.MethodsThe study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts.ResultsThe cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave.ConclusionsWhen used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes.

Green growth of cereal food production under the constraints of agricultural carbon emissions: A new insights from ARDL and VECM models

The aim of this study is to investigate the green growth of cereal food production under the constraints of agricultural carbon emissions and area sown. A qualitative time-series data from 1985 to 2018 was used to analyze the Autoregressive distributed lag (ARDL), vector error correction model (VECM), and Novel dynamic ARDL models. The empirical results of the long-run and short-run nexus show that the increasing in area sown and food production index resulting to increase cereal food production. While agricultural carbon emission only confirms short-run significant negative effect and predicts that the decreasing agricultural carbon emissions will support to the increase of cereal food production. The novel dynamic ARDL model revealed that in long-run 10% positive change in agricultural carbon emission decreases cereal food production and a 10% decrease in agricultural carbon emission will steadily increase cereal food production. Besides, the VECM model results predict that bidirectional Granger causality runs from area sown and food production index to the cereal food production while unidirectional Granger causality exists from cereal food production to agricultural carbon emissions. Overall, conclusion the agricultural carbon emissions and area sown are accountable for reducing cereal food productivity. By switching from chemical fertilizers to organic fertilizers, farmers can enhance farm productivity in a healthy and sustainable environment.

EFFECT OF EXCHANGE RATE VOLATILITY ON UGANDA'S TRADE BALANCE

This study examines the long-run and the short-run relationship between the real exchange rate, GDP, FDI, inflation (INF), gross capital formation (GCF), Net official's development assistance (NODA), GNI, and trade balance in Uganda for the period 1994-2018. We used an Augmented Dickey-Fuller (ADF) test for the stationarity test, and we use the Johannsen cointegration approach to prove the existence of cointegration. The ADF tests show that the series was non-stationary in level but became stationary after the first difference. The Johannsen cointegration test indicates the long and short-run relationship between all the explanatory and trade balance in Uganda. Under such circumstances, a Vector Error Correction Model (VECM) is employed since the results offer more information than other data generation processes. Our findings are as follows: Real exchange rates, FDI, GCF and have a positive relationship with Trade balance. It means that Uganda can depreciate the Exchange rate to improve its Trade balance. The results proved the J-Curve effect's existence (i.e., the long-term impact of exchange rate on trade balance). The recommendations from this study are - Uganda's monetary policy management should emphasize more efforts on the stability and minimization of the volatility of exchange rates of the shillings since its movements affect international prices both negatively and positively, leading to either a decline or trade boost.
 Keywords: Trade balance; Real exchange rate; Net official's development assistances; GNI; VECM model; Uganda

ANALISIS INTEGRASI SPASIAL PASAR CABAI MERAH KERITING DI JAWA TENGAH DENGAN METODE VECTOR ERROR CORRECTION MODEL

Curly red chili (Capsicum annuum L.) is one of commodity which has a big influence to the national economy. To maintain the price stability of curly red chili, an integrated market is needed. Spatial market integration is the level of closeness of relations between regional markets and other regional markets. Spatial market integration will be modeled by the Vector Error Correction Model (VECM) method to see the closeness of both short and long term relationships. The object of this study is the price of curly red chili for several regions in Central Java, such as Kota Semarang, Kab. Demak, Kab. Pati, and Kab. Pekalongan in the period January 2016 to December 2019 where the data has met the stationarity test at first level of difference. In Johansen's cointegration test, it was obtained 3 cointegrations, which means that in each short-term period all variables tend to adjust to each other to achieve long-term balance. Granger causality test shows that there is a two-way relationship and the relationship affects one variable to another for all variables. The VECM model obtained has the MAPE accuracy value for HCMK Semarang 15.93%, Kab. Demak 17.61%, Kab. Pati 15.88%, and Kab. Pekalongan 14.49% which can be interpreted that the performance of the model is good. Keywords: Curly Red Chili, Spatial Market Integration, VECM, Johansen's Cointegration, Granger Causality

An Analysis of the Causes of Fluctuations in the Futures Price of Fuel Oil in China Using the VECM Model

Purpose - This paper analyze the causes of fluctuations in the futures price of fuel oil in China by dividing them into domestic and foreign factors. In particular, this study aims to compare and analyze the impact level of the world’s three major crude oil futures markets on China’s fuel oil futures market. Design/Methodology/Approach - This paper first identifies the long-term equilibrium relationship between Chinese fuel oil futures price and each influencing factor through Johansen’s cointegration test. It then analyzes the short-term relationship through the impulse response function and variance decomposition of prediction errors by setting up a VECM (Vector Error Correction Model) model. The variables used were China’s fuel oil futures price, crude oil imports, crude oil production, light oil production, WTI (West Texas Intermediate) crude oil futures price, Brent crude oil futures price and Dubai crude oil futures price. Data consisted of monthly statistical data from January 2006 to December 2019. Findings - Based on cointegration test resuts, it was found that there is a long-term equilibrium relationship among the variables. As a result of variance decomposition analysis of the impact response function and prediction error, it was found that the light oil production in domestic factor has the greatest effect on China’s fuel oil futures price. In terms of foreign factors, Dubai crude oil futures price has the greatest effect on China’s fuel oil futures price. Research Implications - China is seeking a stable import of energy resources through the systematization of the oil futures market, and furthermore, there seems to be an intention to enhance China’s influence in the international oil market. Like China, Korea mainly imports and consumes Middle Eastern crude oil, so these results will have certain implications for Korea.

Study on the impacts of Shanghai crude oil futures on global oil market and oil industry based on VECM and DAG models

In the present study, the daily settlement data of Shanghai crude oil futures and world’s major crude oils are selected. The role of Shanghai crude oil futures is studied regarding its pricing power and hedging risk. The dynamic relation analysis between Shanghai crude oil futures and international oil market is conducted by using rolling window causality test. The vector error correction model (VECM) and directed acyclic graph (DAG) are used to explore the long-term relationship and identify the contemporaneous causality structure respectively. Then Shanghai crude oil futures’ impacts on other oil price fluctuations are analyzed by using variance decomposition method. The obtained analysis results show that the pricing power of Shanghai crude oil futures is limited compared with the international benchmark oil price, but it has begun to have a contemporaneous influence in the Asian oil market price transmission and better reflect oil supply and demand. Moreover, Shengli crude oil has stronger impact on the pricing mechanism after the listing of Shanghai crude oil futures. Furthermore, it also establishes an effective hedging tool for oil importers and refineries. Therefore, although the Shanghai crude oil futures is still in its initial development stage at present, it provides an important basis for becoming a regional benchmark in Asia and a useful instrument for energy market participants, influencing China’s oil industry in import price and consumption.

Application of Vector Error Correction Model (VECM) and Impulse Response Function for Daily Stock Prices

Vector Error Correction Model is a cointegrated VAR model. This idea of Vector Error Correction Model (VECM), which consists of a VAR model of the order p - 1 on the differences of the variables, and an error-correction term derived from the known (estimated) cointegrating relationship. Intuitively, and using the stock market example, a VECM model establishes a short-term relationship between the stock prices, while correcting with the deviation from the long-term comovement of prices. An Impulse Response Function traces the incremental effect of a 1 unit (or one standard deviation) shock in one of the variables on the future values of the other endogenous variables. Impulse Response Functions trace the incremental effect of the marketing action reflected in the shock. The data used in this analysis are 4 (four) daily plantation stocks prices in Indonesia with time period of January to July in three years which are 2018, 2019, and 2020. The objective of this study is to determine the relationship among 4 (four) stocks prices with VECM and to know the behaviour of each stocks prices with Impulse Response.

Institutional quality and CO2 emission–output relations: The case of Asian countries

Although Asian economies have registered strong economic growth over the last few decades, their growing pollution emissions raise concerns among the policymakers about the sustainability of this output growth. This paper tests the causal relationship between economic development, energy consumption, trade openness, financial development, FDI, government expenditures, institutional quality. and pollution emissions for 41 Asian economies from 1996 to 2015. Further, we separately test the impact of political and economic institutions on pollution emissions of the sample economies. Our estimated outcomes, based on the panel cointegration method and panel vector error correction models (VECM), substantiate the presence of a cointegration relationship among all the selected variables. While economic development, energy use, trade openness, and FDI augment environmental degradation, financial development and better economic institutions help the selected countries in reducing their pollution emissions. Moreover, better economic and political institutions also mediate the adverse impact of income, trade openness, and FDI on pollution emissions. The VECM model shows that per capita GDP is the only variable having a causal effect on pollution emissions in all the models. For all the other variables, the causal effect is significant only in a few cases. These outcomes have some important policy recommendations for the sample economies.

Estimation of VECM Parameter Using Bayesian Approach: An Application to Analysis of Macroeconomic Variables

This study uses Bayesian approach to estimate Vector Error Correction Model (VECM). The aims of this study is to analyze the relationship between macroeconomic variables in Indonesia. To analyze the best method to influence government targets or policies on economic growth by studying the relationships of many macroeconomic variables. Previous studies in analyzing the relationship between macroeconomic variables with VECM analysis, using the Maximum Likelihood Estimation. However this estimation method cannot solve the problem of overparameterization in VECM model. The variables used in this study are six macroeconomic variables in Indonesia in 2010 quarter 1 to 2019 quarter 4 are GDP, the money supply, exchange rate of rupiah to US dollar, exports, imports and interest rates. The number of data in this study is less than the number of estimated parameters causing overparameterization problems. Therefore, this study uses the Bayesian parameter estimation method to avoid overparameterization problems in economic data. The model obtained from this study is the BVECM(3) and it has been proven that the model is suitable (the model diagnostic test). Based on the parameter estimation results of BVECM(3), the significant variables affecting GDP are GDP itself, the money supply, exchange rate of rupiah to US Dollar, exports, imports and the interest rate for Bank Indonesia Certificates. In addition, there is a two-way relationship that affects each other, namely the relationship between GDP and the money supply, exports and imports, exports and interest rates, and between imports and interest rates.

A forecasting framework for predicting perceived fatigue: Using time series methods to forecast ratings of perceived exertion with features from wearable sensors

Advancements in sensing and network technologies have increased the amount of data being collected to monitor the worker conditions. In this study, we consider the use of time series methods to forecast physical fatigue using subjective ratings of perceived exertion (RPE) and gait data from wearable sensors captured during a simulated in-lab manual material handling task (Lab Study 1) and a fatiguing squatting with intermittent walking cycle (Lab Study 2). To determine whether time series models can accurately forecast individual response and for how many time periods ahead, five models were compared: naïve method, autoregression (AR), autoregressive integrated moving average (ARIMA), vector autoregression (VAR), and the vector error correction model (VECM). For forecasts of three or more time periods ahead, the VECM model that incorporates historical RPE and wearable sensor data outperformed the other models with median mean absolute error (MAE) <1.24 and median MAE <1.22 across all participants for Lab Study 1 and Lab Study 2, respectively. These results suggest that wearable sensor data can support forecasting a worker’s condition and the forecasts obtained are as good as current state-of-the-art models using multiple sensors for current time prediction.

MODEL PERMINTAAN UANG DI INDONESIA DENGAN PENDEKATAN VECTOR ERROR CORRECTION MODEL

This research aims to estimate the demand for money model in Indonesia for 2005.22015.12. The variables used in this research are demand for money, interest rate, inflation, and exchange rate (IDR/US$). The stationary test with ADF used to test unit root in the data. Cointegration test applied to estimate the long run relationship between variables. This research employed the Vector Error Correction Model (VECM) to estimate the money demand model in Indonesia. The results showed that all the data was stationer at the difference level (1%). There were long run relationship between interest rate, inflation and exchange rate to demand for money in Indonesia. The VECM model could not explain interaction between explanatory variables to independent variables. In the short run, there were not relationship between interest rate, inflation and exchange rate to demand for money in Indonesia for 2005.2-2015.12.