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Relevant bibliographies by topics / Vector error correction model

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Journal articles

Academic literature on the topic 'Vector error correction model'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 June 2025

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Journal articles on the topic "Vector error correction model"

1

Gonzalo, Jesùs, and Jean-Yves Pitarakis. "Specification via model selection in vector error correction models." Economics Letters 60, no. 3 (1998): 321–28. http://dx.doi.org/10.1016/s0165-1765(98)00129-3.

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2

Abusharbeh, Mohammed. "Determinants of Islamic bank financing in the Middle East: Vector Error Correction Model (VECM)." Investment Management and Financial Innovations 17, no. 4 (2020): 285–98. http://dx.doi.org/10.21511/imfi.17(4).2020.25.

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As the world has been struck with a global financial crisis, Middle Eastern countries have been affected as well. Thus, Islamic banks have expanded, and the competitive advantage has become intensive with the increased number of conventional banks in the global banking system. This manuscript is aimed to examine the impact of macroeconomic and bank-specific factors on Islamic bank financing in the Middle Eastern countries. Therefore, the Vector Error Correction Model and the Granger causality test were run from 2009 to 2018 to detect the long- and short-run relationship between the explanatory variables and Islamic bank financing. The results suggest that both inflation and profitability negatively impact Islamic bank financing in the long run. The paper also revealed bidirectional causality between the variables GDP and bank size and Islamic bank financing. It shows that GDP and bank size are highly dominant factors of Islamic bank financing in the short run. Thus, this paper provides evidence that any short-run shock in the variables of GDP, inflation, and bank size will cause a long-term relationship with Islamic bank financing. This article’s novelty is to ensure resilience within the Islamic banking system during and after the financial crisis. It provides evidence that Islamic banks can cushion their financial activities from economic volatility during the crisis. The results found can be used to predict the growth of Islamic bank financing in upcoming years in the Middle East and all emerging countries.

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3

Liao, Zhipeng, and Peter C. B. Phillips. "AUTOMATED ESTIMATION OF VECTOR ERROR CORRECTION MODELS." Econometric Theory 31, no. 3 (2015): 581–646. http://dx.doi.org/10.1017/s026646661500002x.

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Model selection and associated issues of post-model selection inference present well known challenges in empirical econometric research. These modeling issues are manifest in all applied work but they are particularly acute in multivariate time series settings such as cointegrated systems where multiple interconnected decisions can materially affect the form of the model and its interpretation. In cointegrated system modeling, empirical estimation typically proceeds in a stepwise manner that involves the determination of cointegrating rank and autoregressive lag order in a reduced rank vector autoregression followed by estimation and inference. This paper proposes an automated approach to cointegrated system modeling that uses adaptive shrinkage techniques to estimate vector error correction models with unknown cointegrating rank structure and unknown transient lag dynamic order. These methods enable simultaneous order estimation of the cointegrating rank and autoregressive order in conjunction with oracle-like efficient estimation of the cointegrating matrix and transient dynamics. As such they offer considerable advantages to the practitioner as an automated approach to the estimation of cointegrated systems. The paper develops the new methods, derives their limit theory, discusses implementation, reports simulations, and presents an empirical illustration with macroeconomic aggregates.

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4

Obeng, Cosmos, and Cleophas Attor. "INTERCONNECTION AMONG CRYPTOCURRENCIES: USING VECTOR ERROR CORRECTION MODEL." International Journal of Entrepreneurial Knowledge 10, no. 2 (2022): 24–41. http://dx.doi.org/10.37335/ijek.v10i2.157.

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The research paper aimed to investigate the relationship between the major popular cryptocurrencies in terms of market dominance and identify any pattern and/or causality between the short-run and long-run series. Cryptocurrency has received much attention because of media publicity and the financial returns it generates within a short time, with its associated risk level. This innovative financial research investigates for the first time by thoroughly analyzing nine top cryptocurrencies, excluding stablecoins. The study used the Vector Error Correction model to analyse how the various cryptocurrency under investigation are interconnected. The results demonstrated how concentrated the causality effect is on some specific cryptocurrencies. The study uses the top nine cryptocurrencies on the crypto markets, excluding stablecoins that have existed since October 2017. The frequency of the data is 1523 daily closing prices. The choice of the data stemmed from its availability and has existed since October 2017. The primary outcome is clear and possibly explains the dominance of Bitcoin and Ethereum as the main drivers of the prices of related or altcoins. Any movement in the price level of the two dominant cryptos affects all the altcoins on the crypto market. The research further unearths the interconnection or correlation between the major cryptocurrencies. It will assist institutional and retail investors, fund managers, and managers, with the possible mix of assets in their portfolio based on their risk appetite level in making investment decisions.

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5

Jiang, Yixiao, George K. Zestos, and Zachary Timmerman. "A Vector Error Correction Model for Japanese Real Exports." Atlantic Economic Journal 48, no. 3 (2020): 297–311. http://dx.doi.org/10.1007/s11293-020-09675-1.

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6

Faghih, Sayed Amir Mohsen, and Hamed Kashani. "Forecasting Construction Material Prices Using Vector Error Correction Model." Journal of Construction Engineering and Management 144, no. 8 (2018): 04018075. http://dx.doi.org/10.1061/(asce)co.1943-7862.0001528.

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7

Van Gestel, Tony, Marcelo Espinoza, Bart Baesens, Johan A. K. Suykens, Carine Brasseur, and Bart De Moor. "A Bayesian nonlinear support vector machine error correction model." Journal of Forecasting 25, no. 2 (2006): 77–100. http://dx.doi.org/10.1002/for.975.

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8

Wong, James M. W., Albert P. C. Chan, and Y. H. Chiang. "Forecasting construction manpower demand: A vector error correction model." Building and Environment 42, no. 8 (2007): 3030–41. http://dx.doi.org/10.1016/j.buildenv.2006.07.024.

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9

Sung, Joo-han. "A Study on the Apartment Sale Price Decision Model Using Vector Error Correction Model (VECM): Focusing on the Housing Market in Changwon City." Housing Finance Research 5, no. 1 (2021): 27–49. http://dx.doi.org/10.52344/hfr.2021.5.1.27.

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10

Barigozzi, Matteo, Marco Lippi, and Matteo Luciani. "Cointegration and Error Correction Mechanisms for Singular Stochastic Vectors." Econometrics 8, no. 1 (2020): 3. http://dx.doi.org/10.3390/econometrics8010003.

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Large-dimensional dynamic factor models and dynamic stochastic general equilibrium models, both widely used in empirical macroeconomics, deal with singular stochastic vectors, i.e., vectors of dimension r which are driven by a q-dimensional white noise, with q < r . The present paper studies cointegration and error correction representations for an I ( 1 ) singular stochastic vector y t . It is easily seen that y t is necessarily cointegrated with cointegrating rank c ≥ r − q . Our contributions are: (i) we generalize Johansen’s proof of the Granger representation theorem to I ( 1 ) singular vectors under the assumption that y t has rational spectral density; (ii) using recent results on singular vectors by Anderson and Deistler, we prove that for generic values of the parameters the autoregressive representation of y t has a finite-degree polynomial. The relationship between the cointegration of the factors and the cointegration of the observable variables in a large-dimensional factor model is also discussed.

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