scholarly journals The determinants of electricity consumption for ASEAN countries

2017 ◽  
Vol 13 (4-1) ◽  
pp. 331-339
Author(s):  
Mohd Tahir Ismail ◽  
Nadhilah Mahmud ◽  
Rosmanjawati Abdul Rahman
Keyword(s):  
Forecast Error ◽  
Carbon Dioxide Emission ◽  
Electricity Consumption ◽  
Error Variance ◽  
Sample Period ◽  
Causality Test ◽  
Vector Autoregressive ◽  
Vec Model ◽  
Forecast Error Variance ◽  
Asean Countries

The present study investigates the causal relationship between ASEAN seven member countries electricity consumption (EC) and some determinants such as gross domestic product (GDP), exports (EXP) and carbon dioxide emission (CO2) using vector autoregressive (VAR) framework via vector error correction (VEC) model for the period from 1980-2015. The findings show that the effect of the chosen determinants is different among the seven countries. Within the sample period, by utilizing Granger causality test, out of the seven countries, only four revealed either unidirectional or bidirectional causality running from EC to the three determinants, GDP, EXP and CO2. Whereas, thru forecast error variance decomposition (FEVD), forecasting beyond the sample period uncovered a shock to EC will also spread to GDP, EXP and CO2. The present study suggests that ASEAN should take note in designing their electricity policy, since electricity affect and be affected by other factors. In addition, ASEAN also should find solutions on how to control CO2 emission through EC.

scholarly journals Alternative Model Selection Using Forecast Error Variance Decompositions in Wholesale Chicken Markets

2009 ◽  
Vol 41 (1) ◽  
pp. 227-240 ◽  
Author(s):  
Andrew M. McKenzie ◽  
Harold L. Goodwin ◽  
Rita I. Carreira
Keyword(s):  
Forecast Error ◽  
Error Variance ◽  
Autoregressive Models ◽  
Model Specification ◽  
Decomposition Approach ◽  
Vector Autoregressive ◽  
Forecasting Performance ◽  
Causality Tests ◽  
Forecast Error Variance ◽  
Variance Decompositions

Although Vector Autoregressive models are commonly used to forecast prices, specification of these models remains an issue. Questions that arise include choice of variables and lag length. This article examines the use of Forecast Error Variance Decompositions to guide the econometrician's model specification. Forecasting performance of Variance Autoregressive models, generated from Forecast Error Variance Decompositions, is analyzed within wholesale chicken markets. Results show that the Forecast Error Variance Decomposition approach has the potential to provide superior model selections to traditional Granger Causality tests.

scholarly journals Public Expenditure and Economic Growth in India: An Empirical Analysis Using Vector Autoregression (VAR) Model

2020 ◽  
Vol 5 (2) ◽  
pp. 45-58
Author(s):  
Rashmi Gupta ◽  
Swati Shastri
Keyword(s):  
Economic Growth ◽  
Public Expenditure ◽  
Forecast Error ◽  
Granger Causality Test ◽  
Error Variance ◽  
Var Model ◽  
Impulse Responses ◽  
Causality Test ◽  
Long Run ◽  
Forecast Error Variance

Objective – The objective of this study is to test direction of causality between components of public expenditure and economic growth in India. Methodology/Technique – The paper uses annual data for the period 1980-2015. To measure public expenditure, plan expenditure and non-plan expenditure are used. The econometric methodology employed is Vector Auto regression (VAR) model. Findings – First, the stationary properties of the data were tested using Augmented Dickey-Fuller (ADF) test, Dickey-Fuller (DF) test, and the Phillip-Perron (PP) test and found that variables were non-stationary in level, but stationary in first differences. Then, Johansen- Jueslius cointegration test was employed to test the long-run association among the variables and results suggest an absence of any long-run association between plan expenditure and non-plan expenditure and economic growth in India. The Granger Causality test suggests there is unidirectional causality running from economic growth and non-plan expenditure and plan expenditure and non-plan expenditure and absence of causality public expenditure and economic growth. Novelty – The results of the Forecast Error Variance Decompositions test indicated that innovations in the variables are mostly explained by their own shocks. The impulse responses of the economic growth, plan expenditure and non-plan expenditure with respect to identified shocks are consistent with the results of Variance Decomposition Analysis. Type of Paper: Empirical. JEL Classification: O4, O49, O53. Keywords: Plan Expenditure; Non-plan Expenditure; Economic Growth; Unit Root; Cointegration Test; Granger Causality Test; Forecast Error Variance Decomposition; Impulse Responses. Reference to this paper should be made as follows: Gupta, R; Shastri, S. 2020. Public Expenditure and Economic Growth in India: An Empirical Analysis Using Vector Autoregression (VAR) Model, J. Bus. Econ. Review 5(2) 45– 58 https://doi.org/10.35609/jber.2020.5.2(1)

scholarly journals Eurozone or national inflation projections: Which has greater impact on consumer expectations?

2020 ◽  
pp. 14-14
Author(s):  
Magdalena Szyszko ◽  
Karolina Tura-Gawron
Keyword(s):  
Central Bank ◽  
Forecast Error ◽  
Probabilistic Method ◽  
Error Variance ◽  
Response Analysis ◽  
Inflation Expectations ◽  
Causality Test ◽  
Consumer Expectations ◽  
Forecast Error Variance ◽  
The Impact

We compare the dependence of consumer inflation expectations on European Central Bank (ECB) inflation projections with that on national central bank (NCB) projections in four economies: Austria, Belgium, Finland, and Germany. We aim to assess whether the information published by central banks affects consumers, and whether inflation projections published by NCBs are more relevant to consumers than those published for the entire Eurozone. Inflation expectations were obtained from the Business and Consumer Surveys conducted by the Directorate General for Economic and Financial Affairs of the European Commission and quantified using the probabilistic method. The methodology covers: (1) forecast encompassing tests, (2) the Granger causality test, and (3) impulse response analysis complemented by (4) forecast error variance decomposition. The results suggest that the ECB outlook constitutes a more important factor in expectation formation. This article adds to the existing literature by comparing the impact of common and national projections on consumer expectations.

Testing cointegration between workers' remittances and human capital formation in Sri Lanka

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ahamed Lebbe Mohamed Aslam ◽  
Selliah Sivarajasingham
Keyword(s):  
Human Capital ◽  
Sri Lanka ◽  
Function Analysis ◽  
Forecast Error ◽  
Error Variance ◽  
Capital Formation ◽  
Human Capital Formation ◽  
Causality Test ◽  
Content Type ◽  
Forecast Error Variance

PurposeThis study investigates the long-run relationship between workers' remittances and human capital formation in Sri Lanka by using the macro-level time series data during the period of 1975–2020.Design/methodology/approachIn this study, the augmented Dickey–Fuller (ADF) and Philips–Perron (PP) unit root tests, the autoregressive distributed lag (ARDL) bounds cointegration technique, the Granger causality test, the forecast error variance decomposition technique and impulse response function analysis were employed as the analytical techniques.FindingsIn accordance with the results of unit root tests, the variables used in this study are mixed order. Results of cointegration confirm that workers' remittances in Sri Lanka have both long-run and short-run beneficial relationship with human capital formation. The Granger causality test results indicate that there is a two-way causal relationship between workers' remittances and human capital formation. The results of forecast error variance decomposition expose that innovation of workers' remittances contributes to the forecast error variance in human capital in bell shape. Further, the empirical evidence of impulse response function analysis reveals that a positive standard deviation shock to workers' remittances has an immediate significant positive impact on human capital formation in Sri Lanka for a period of up to ten years.Practical implicationsThis research provides insights into the workers' remittances in human capital formation in Sri Lanka. The findings of this study provides evidence that workers' remittances help to produce human capital formation.Originality/valueBy using the ARDL Bounds cointegration and other techniques in Sri Lanka, this study fills an important gap in academic literature.

scholarly journals An Empirical Study of Dividend Payout and Future Earnings in Singapore

2010 ◽  
Vol 13 (02) ◽  
pp. 267-286 ◽  
Author(s):  
King Fuei Lee
Keyword(s):  
Forecast Error ◽  
Error Variance ◽  
Response Analysis ◽  
Causality Test ◽  
Dividend Payout ◽  
Vector Error Correction ◽  
Future Earnings ◽  
Vector Error ◽  
Long Run ◽  
Forecast Error Variance

The main purpose of this paper is to apply Johansen's vector error-correction model (VECM) to investigate the existence of the dividend signalling effect in the Singapore aggregate market through impulse response analysis, forecast error variance decomposition and Granger-causality test. Our findings show that a unit shock increase in dividend payout leads to a permanent increase in future earnings over time. These results imply that there exists informational/signalling content in dividend payout in the Singapore market over the long run. We further find that at least half of the forecast error variance in earnings can be accounted for by innovations in the dividend payout. In addition, the payout ratio is also shown to Granger-cause earnings in the Singapore market.

Estimating Orthogonal Impulse Responses via Vector Autoregressive Models

1991 ◽  
Vol 7 (4) ◽  
pp. 487-496 ◽  
Author(s):  
Helmut Lütkepohl ◽  
D.S. Poskitt
Keyword(s):  
Time Series ◽  
Forecast Error ◽  
Error Variance ◽  
Asymptotic Distributions ◽  
Impulse Response Functions ◽  
Impulse Responses ◽  
Vector Autoregressive ◽  
Forecast Error Variance ◽  
Variance Decompositions ◽  
The Relationship

Impulse response functions from time series models are standard tools for analyzing the relationship between economic variables. The asymptotic distribution of orthogonalized impulse responses is derived under the assumption that finite order vector autoregressive (VAR) models are fitted to time series generated by possibly infinite order processes. The resulting asymptotic distributions of forecast error variance decompositions are also given.

scholarly journals Assessing the progress of exports diversification in Saudi Arabia: growth-share matrix approach

2020 ◽  
Vol 18 (3) ◽  
pp. 118-128
Author(s):  
Mohammad Imdadul Haque
Keyword(s):  
Growth Rate ◽  
Saudi Arabia ◽  
Forecast Error ◽  
Impulse Response Function ◽  
Error Variance ◽  
Vector Autoregressive ◽  
Long Run ◽  
Market Shares ◽  
Forecast Error Variance ◽  
Revenue Volatility

High dependence on a particular category of exports results in fluctuations in income as the price of the export item fluctuates. In Saudi Arabia, a single category of mineral exports forms over 78% of the total exports, exposing the country to revenue volatility. The study aims to assess the magnitude of diversification of the export basket for the country. It uses data from 1984 to 2018 to study the importance of non-mineral exports in total exports. It applies Granger causality, variance decomposition, and impulse response function in the vector autoregressive framework. The study also uses the growth-share matrix to evaluate individual items of non-mineral exports. The results show a long-run relationship with a 1% increase in non-mineral exports, leading to a 0.30% increase in total exports. Non-mineral exports Granger-cause total exports. In the long run, non-mineral exports have a share of 64% of the forecast error variance in total exports. Moreover, a 1% shock in non-mineral exports creates a huge initial impact on total exports. Also, the growth rate of non-mineral products is higher than mineral products. The results indicate the importance of non-mineral exports for a predominantly oil-exporting country. Finally, the study attempts to classify its non-mineral export categories based on growth rates and market shares. Targeted emphasis on export category with a strong growth rate and low market share can be an effective strategy for further export diversification.

THE ROLE OF US-BASED FDI FLOWS FOR GLOBAL OUTPUT DYNAMICS

2017 ◽  
Vol 23 (3) ◽  
pp. 943-973 ◽  
Author(s):  
Florian Huber ◽  
Manfred M. Fischer ◽  
Philipp Piribauer
Keyword(s):  
Foreign Direct Investment ◽  
Latin American ◽  
Direct Investment ◽  
Forecast Error ◽  
Emerging Market ◽  
Error Variance ◽  
Vector Autoregressive ◽  
Long Term Effect ◽  
Forecast Error Variance ◽  
Global Vector

This paper uses a global vector autoregressive (GVAR) model to analyze the relationship between foreign direct investment (FDI) inflows and output dynamics in a multicountry context. The GVAR model enables us to make two important contributions: First, to model international linkages among a large number of countries, which is a key asset given the diversity of countries involved, and second, to model foreign direct investment and output dynamics jointly. The country-specific small-dimensional vector autoregressive submodels are estimated utilizing a Bayesian version of the model coupled with stochastic search variable selection priors to account for model uncertainty. Using a sample of 15 emerging and advanced economies over the period 1998:Q1–2012:Q4, we find that US outbound FDI exerts a positive long-term effect on output. Asian and Latin American economies tend to react faster and also stronger than Western European countries. Forecast error variance decompositions indicate that FDI plays a prominent role in explaining gross domestic product fluctuations, especially in emerging market economies.

scholarly journals Impact of the COVID-19 outbreak on the US equity sectors: Evidence from quantile return spillovers

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Syed Jawad Hussain Shahzad ◽  
Elie Bouri ◽  
Ladislav Kristoufek ◽  
Tareq Saeed
Keyword(s):  
Stock Market ◽  
Network Structure ◽  
Forecast Error ◽  
Common Factor ◽  
Error Variance ◽  
Stock Market Index ◽  
Vector Autoregressive ◽  
The Us ◽  
Market State ◽  
Forecast Error Variance

AbstractThe aim of this study is to examine the extreme return spillovers among the US stock market sectors in the light of the COVID-19 outbreak. To this end, we extend the now-traditional Diebold-Yilmaz spillover index to the quantiles domain by building networks of generalized forecast error variance decomposition of a quantile vector autoregressive model specifically for extreme returns. Notably, we control for common movements by using the overall stock market index as a common factor for all sectors and uncover the effect of the COVID-19 outbreak on the dynamics of the network. The results show that the network structure and spillovers differ considerably with respect to the market state. During stable times, the network shows a nice sectoral clustering structure which, however, changes dramatically for both adverse and beneficial market conditions constituting a highly connected network structure. The pandemic period itself shows an interesting restructuring of the network as the dominant clusters become more tightly connected while the rest of the network remains well separated. The sectoral topology thus has not collapsed into a unified market during the pandemic.

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