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Article
Publication date: 1 February 1994

L.L. Leachman, Christie H. Paksoy and J.B. Wilkinson

This research applies vector autoregression to estimate a system composed of market share and relative advertising expenditures of the seven major competitors in the U. S…

Abstract

This research applies vector autoregression to estimate a system composed of market share and relative advertising expenditures of the seven major competitors in the U. S. replacement passenger tire market between 1972 and 1983. The results of the study suggest that a company's market share in this market cannot be predicted from its relative advertising expenditures.

Details

Studies in Economics and Finance, vol. 15 no. 2
Type: Research Article
ISSN: 1086-7376

Abstract

Identification of shocks of interest is a central problem in structural vector autoregressive (SVAR) modeling. Identification is often achieved by imposing restrictions on the impact or long-run effects of shocks or by considering sign restrictions for the impulse responses. In a number of articles changes in the volatility of the shocks have also been used for identification. The present study focuses on the latter device. Some possible setups for identification via heteroskedasticity are reviewed and their potential and limitations are discussed. Two detailed examples are considered to illustrate the approach.

Details

VAR Models in Macroeconomics – New Developments and Applications: Essays in Honor of Christopher A. Sims
Type: Book
ISBN: 978-1-78190-752-8

Keywords

Article
Publication date: 13 November 2017

Amanjot Singh and Manjit Singh

The authors aim to report empirical linkages between the US and Brazil, Russia, India and China (BRIC) financial stress indices catalyzing catalyzing dependent economic…

Abstract

Purpose

The authors aim to report empirical linkages between the US and Brazil, Russia, India and China (BRIC) financial stress indices catalyzing catalyzing dependent economic policy initiatives (an extended version of Singh and Singh, 2017a).

Design/methodology/approach

Initially, the study develops financial stress indices for the respective BRIC financial markets. Later, it captures linkages among the said US-BRIC indices by using Johansen cointegration, vector autoregression/vector error correction models (VECM), generalized impulse response functions, Toda–Yamamoto Granger causality, variance decomposition analyses and bivariate generalized autoregressive conditional heteroskedasticity (GARCH) model under constant conditional correlation framework, in general. Markov regime switching and efficient causality tests proposed by Hill (2007) are also used.

Findings

Overall, there are both short-run and long-run dynamic interactions observed between the US and Indian financial stress indices. For rest of the markets, only short-run interactions are found to be in existence. The time-varying co-movement coefficients report financial contagion impact of the US financial crisis on Russian and Indian financial systems only. Contrary to this, Brazilian and Chinese financial systems are largely exhibiting interdependence with the US financial system. Efficient causality tests report indirect impact of the Russian financial system on Brazilian via auxiliary Indian financial system.

Originality/value

The present study is the first of its kind capturing linkages among the US-BRIC financial stress indices by using diverse econometric models. The results support different market participants and policymakers in understanding effectiveness and implementation of economic policies while considering their cross-market interactions as well.

Details

International Journal of Law and Management, vol. 59 no. 6
Type: Research Article
ISSN: 1754-243X

Keywords

Book part
Publication date: 13 December 2013

Refet S. Gürkaynak, Burçin Kısacıkoğlu and Barbara Rossi

Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample…

Abstract

Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.

Details

VAR Models in Macroeconomics – New Developments and Applications: Essays in Honor of Christopher A. Sims
Type: Book
ISBN: 978-1-78190-752-8

Keywords

Article
Publication date: 8 January 2020

Chaiyuth Padungsaksawasdi

Considering the unique data of the gold investor sentiment index in Thailand, the purpose of this paper is to investigate the bivariate dynamic relationship between the…

Abstract

Purpose

Considering the unique data of the gold investor sentiment index in Thailand, the purpose of this paper is to investigate the bivariate dynamic relationship between the gold investor sentiment index and stock market return, as well as that between the gold investor sentiment index and stock market volatility, using the panel vector autoregression (PVAR) methodology. The author presents and discusses the findings both for the full sample and at the industry level. The results support prior literature that stocks in different industries do not react similarly to investor sentiment.

Design/methodology/approach

The PVAR methodology with the GMM estimation is found to be superior to other static panel methodologies due to considering both unobservable time-invariant and time-variant factors, as well as being suitable for relatively short time periods. The panel data approach improves the statistical power of the tests and ensures more reliable results.

Findings

In general, a negative and unidirectional association from gold investor sentiment to stock returns is observed. However, the gold sentiment-stock realized volatility relationship is negative and bidirectional, and there exists a greater impact of a stock’s realized volatility on gold investor sentiment. Importantly, evidence at the industry level is stronger than that at the aggregate level in both return and volatility cases, confirming the role of gold investor sentiment in the Thai stock market. The capital flow effect and the contagion effect explain the gold sentiment-stock return relationship and the gold sentiment-stock volatility relationship, respectively.

Research limitations/implications

The gold price sentiment index can be used as a factor for stock return predictability and stock realized volatility predictability in the Thai equity market.

Practical implications

Practitioners and traders can employ the gold price sentiment index to make a profit in the stock market in Thailand.

Originality/value

This is the first paper to use panel data to investigate the relationships between the gold investor sentiment and stock returns and between the gold investor sentiment and stocks’ realized volatility, respectively.

Details

International Journal of Managerial Finance, vol. 16 no. 3
Type: Research Article
ISSN: 1743-9132

Keywords

Open Access
Article
Publication date: 5 August 2021

Zhonglu Liu, Haibo Sun and Songlin Tang

Climate change not only causes serious economic losses but also influences financial stability. The related research is still at the initial stage. This paper aims to…

1390

Abstract

Purpose

Climate change not only causes serious economic losses but also influences financial stability. The related research is still at the initial stage. This paper aims to examine and explore the impact of climate change on financial stability in China.

Design/methodology/approach

This paper first uses vector autoregression model to study the impact of climate change to financial stability and applies NARDL model to assess the nonlinear asymmetric effect of climate change on China’s financial stability using monthly data from 2002 to 2018.

Findings

The results show that both positive and negative climate shocks do harm to financial stability. In the short term, the effect of positive climate shocks on financial stability is greater than the negative climate shocks in the current period, but less in the lag period. In the long term, negative climate shocks bring larger adjustments to financial stability relative to positive climate shocks. Moreover, compared with the short-term effect, climate change is more destructive to financial stability in the long run.

Originality/value

The paper provides a quantitative reference for assessing the nexus between climate change and financial stability from a nonlinear and asymmetric perspective, which is beneficial for understanding climate-related financial risks.

Details

International Journal of Climate Change Strategies and Management, vol. 13 no. 3
Type: Research Article
ISSN: 1756-8692

Keywords

Content available
Article
Publication date: 2 June 2020

Muhammad Najib Razali, Ain Farhana Jamaluddin, Rohaya Abdul Jalil and Thi Kim Nguyen

This research attempts to highlight the concept of big data analytics in predictive maintenance for maintenance management of government buildings in Malaysia.

Abstract

Purpose

This research attempts to highlight the concept of big data analytics in predictive maintenance for maintenance management of government buildings in Malaysia.

Design/methodology/approach

This study uses several empirical analyses such as vector autoregression (VAR), vector error correction model (VECM), ARMA model and Granger causality to analyse predictive maintenance by using big data analytics concept.

Findings

The results indicate that there are strong correlations among these variables, which indicate reciprocal predictive maintenance of maintenance management job function. The findings also showed that there are significant needs of application of big data analytics for maintenance management in Putrajaya, Malaysia, to ensure the efficient maintenance of government buildings.

Originality/value

The conducted case study has demonstrated the empirical perspective which streamlines with the big data analytics' concept in maintenance, especially for analytics' support with appropriate empirical methodology

Details

Property Management, vol. 38 no. 4
Type: Research Article
ISSN: 0263-7472

Keywords

Article
Publication date: 10 October 2016

Zekeriya Yildirim and Mehmet Ivrendi

Recent turbulence in global financial markets implies that emerging economies are likely to soon enter a new era with greater pressure for currency depreciation and…

2820

Abstract

Purpose

Recent turbulence in global financial markets implies that emerging economies are likely to soon enter a new era with greater pressure for currency depreciation and capital outflows. This will likely bring challenges, including macroeconomic instability and inflationary pressures due to potential rapid depreciation. In this context, certain key questions about emerging economies have become focal points of discussion in political and academic spheres: what are the effects of exchange rate depreciation on economic activity? Does exchange rate depreciation create inflationary pressure? Finding answers to these questions is critical for policymakers and financial market participants. As such, the purpose of this paper is to shed light on these questions and thus provides guidance on mitigating the negative impacts of shocks in four fast-growing emerging economies.

Design/methodology/approach

The authors use a vector autoregression model with sign restrictions to examine the dynamic effects of exchange rate movements on fundamental macroeconomic indicators for four fast-growing countries, namely, Brazil, Turkey, Russia, and South Africa. Following Berument et al. (2012a), Ncube and Ndou (2013), Bjørnland and Halvorsen (2013), and An et al. (2014), the authors adopt the sign restriction methodology to identify exchange rate shocks alongside other macroeconomic shocks (monetary policy and productivity shocks) leading to exchange rate fluctuations.

Findings

The results show that exchange rate depreciation typically generates a deep recession and high inflation while improving the trade balance in the four emerging economies. This indicates that depreciation has strong “stagflationary” effects, which are transmitted to the macroeconomy primarily via supply-side channels, especially through the cost of import. Furthermore, the authors find that monetary policy reacts immediately to a domestic currency depreciation in all four emerging countries.

Practical implications

The results imply that these countries’ monetary policies are not and cannot be neutral to exchange rate shocks. However, in these import-dependent countries, monetary tightening (i.e. rate hikes in response to an exchange rate shock) plays a limited role in mitigating the negative effects of depreciation on inflation and economic activity due to the presence of a dominant supply-side channel. In this framework, policymakers should pay greater attention to structural reforms that aim to reduce import dependency. These reforms may increase the effectiveness of domestic monetary policy in mitigating the negative effects of external shocks.

Originality/value

This paper provides a useful perspective for policymakers designing economic interventions to mitigate the adverse effects of exchange rate depreciation and to those who borrow or lend in domestic or international financial markets.

Details

Journal of Economic Studies, vol. 43 no. 5
Type: Research Article
ISSN: 0144-3585

Keywords

Abstract

Details

New Directions in Macromodelling
Type: Book
ISBN: 978-1-84950-830-8

Book part
Publication date: 7 September 2020

César Augusto Oliveros-Ocampo, Rosa María Chávez and Myrna Leticia Bravo

Tourism is sensitive to many factors related to security, such as terrorism and armed conflict. This scenario can be considered for Colombia and its National Natural…

Abstract

Tourism is sensitive to many factors related to security, such as terrorism and armed conflict. This scenario can be considered for Colombia and its National Natural Parks. The signing of the peace agreement with the revolutionary armed forces (FARC-EP) of Colombia that would end the armed conflict in 2016 offers economic possibilities for the country where tourism represents an important option, especially in natural areas where the government has regained control. This has had an impact on the sustained growth of tourism demand. The objective of this study is to determine the sensitivity of the tourism market in Colombia and natural parks and its relationship with the evolutionary dynamics of tourism, in a context of armed conflict. Variables related to visitors and the armed conflict were associated by autoregressive vectors, Pearson correlation test and Granger causality. A correlation of −0.5474 was found for the accumulated period from 1995 to 2018. It was also determined that the number of victims of the armed conflict is the Granger cause of the number of visitors to natural parks. The study concludes that the sensitivity of tourism in natural parks in Colombia is a consequence of State policy for the partial termination of the internal armed conflict.

Details

Tourism, Terrorism and Security
Type: Book
ISBN: 978-1-83867-905-7

Keywords

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