Economic growth and energy consumption causal nexus viewed through a bootstrap rolling window

Balcılar, Mehmet; Özdemir, Zeynel Abidin; Arslantürk, Yalçın

doi:10.1016/j.eneco.2010.05.015

Cited by 444 publications

(374 citation statements)

References 50 publications

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“…As from the review of previous literature, the causality between the two series may suffer from the misleading result in the presence of structural changes in the full sample time series data. (Balcilar et al, 2010). The economic sanctions, the Asian financial crisis, weak internal economic condition, global financial crisis and the recent wave of terrorism in Pakistan cause structural changes in the economy.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The pre-test bias arises due to these methods. A rolling window sub-sample Granger causality is used to avoid the parameter constancy and pre-test bias (Balcilar et al, 2010). There are two significant factors for employing the rolling estimations first; the causal relationship between variables can change over time.…”

Section: Sub Sample Rolling Window Causality Testmentioning

confidence: 99%

“…Second, rolling estimation can detect instability across different sub-samples due to structural change, and the rolling-window estimation captures this process. According to Balcilar et al (2010), rolling window process is based on the fixed-size sub-sample. On the basis of this statement the fixed-size rolling window with given observation, the full sample is converted to a sequence of T-l sub-sample that is, τ-l+1, τ-l, T for τ= l, l+1, ..., T. Now this RB-based modified-LR causality test is alternative to single causality test for full sample to test each sub-sample.…”

Section: Sub Sample Rolling Window Causality Testmentioning

confidence: 99%

“…The impact of monetary policy on the stock market is defined as the average of the entire bootstrap estimates derived from the formula, with representing the number of bootstrap repetitions; similarly, the impact of the stock market on monetary policy is obtained from the formula. Both and are bootstrap estimates from the VAR models in Equation (1) The 90-percent confidence intervals are also computed, for which the lower and upper limits equal the 5th and 95th quantiles of each of the ø21,k and ø12,k , respectively (Balcilar et al, 2010).…”

Section: Sub Sample Rolling Window Causality Testmentioning

confidence: 99%

“…According to (Balcilar et al, 2010 there is no specific procedure to select the window size in rolling estimations. Pesaran and Timmerman (2005) they report that under the structural changes window size is depend on persistence and size of the breaks and select the window size according to the square root mean square error.…”

Section: Sub Sample Rolling Window Causality Testmentioning

confidence: 99%

See 4 more Smart Citations

Causal Relationship between Monetary Policy and the Stock Market: a Bootstrap Rolling Window Approach

Khan¹,

Qingyang²,

Khurshid³

2017

FMIR

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This study examines the causal relationship between the monetary policy and the stock price in Pakistan. We use bootstrap Granger full-sample causality test and sub-sample rolling window estimation test for the period of 1992:01 to 2015:06. The empirical result shows that there is a unidirectional causality from monetary policy to the stock price. However, considering the structural changes, we assess the parameter stability by using the full sample for short run relationship and find the short run instability for the full sample. This recommends that full-sample data cannot depend upon for the precise result. To address the structural changes issue, we propose rolling window approach is used to revisit the causal relationship between the two series and the result reports the bi-directional causal relationship between the monetary policy and the stock market. The relationship between the stock market and the money supply is not consistent, and the upward trend may cause serious policy implication on the stock market for future investment and profit.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Sub Sample Rolling Window Causality Testmentioning

confidence: 99%

Section: Sub Sample Rolling Window Causality Testmentioning

confidence: 99%

Section: Sub Sample Rolling Window Causality Testmentioning

confidence: 99%

Section: Sub Sample Rolling Window Causality Testmentioning

confidence: 99%

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Causal Relationship between Monetary Policy and the Stock Market: a Bootstrap Rolling Window Approach

Khan¹,

Qingyang²,

Khurshid³

2017

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Biofuel energy consumption‐economic growth relationship: an empirical investigation of Brazil

Al‐mulali

Solarin

Öztürk

2016

Biofuels Bioprod Bioref

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This study investigates the influence of biofuel energy consumption on Brazil's economic growth during the period 1980–2012 by employing the Autoregressive Distributed Lag (ARDL) approach and the vector error correction model (VECM) Granger causality. The results revealed two structural breaks during the early 1980s due to the Latin American debt crisis as well as the early 2000s due to the worries related to the increasing global spreads. Moreover, it was found that economic growth, biofuel energy consumption, capital, urbanization, and globalization are co‐integrated. Additionally, it was found that biofuel energy consumption, capital, urbanization, and globalization increase Brazil's economic growth in the short run and in the long run. However, the two structural breaks have a significant negative influence on economic growth. The vector error correction model Granger causality revealed a feedback causal relationship between all the variables (with the exception of capital). However, a unidirectional causality was concluded from capital to economic growth, biofuel energy consumption, urbanization, and globalization. From the results of this study, a number of policy implications were provided. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd

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Financial development, energy consumption, and economic growth: Some recent evidence for India

Raghutla

Chittedi

2020

Bus Strat Dev

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The objective of this study is to investigate the empirical relationship between financial development, energy consumption, and economic growth in India, spanning the time period 1970-2018. The study used ARDL-bound tests and the VECM method. Results confirm the presence of a long-term relationship among variables. Results from the Granger causality test show a unidirectional relationship, from economic growth to financial development, in the short-term. However, in the long-term, we identify a bidirectional causal relationship between economic growth and energy consumption and energy consumption and financial development. The study concludes that financial development and energy consumption are the key drivers of the economic growth of India.

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Economic growth and energy consumption causal nexus viewed through a bootstrap rolling window

Cited by 444 publications

References 50 publications

Causal Relationship between Monetary Policy and the Stock Market: a Bootstrap Rolling Window Approach

Causal Relationship between Monetary Policy and the Stock Market: a Bootstrap Rolling Window Approach

Biofuel energy consumption‐economic growth relationship: an empirical investigation of Brazil

Financial development, energy consumption, and economic growth: Some recent evidence for India

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