Structural Vector Error Correction Model Research Articles

Pass-through of oil prices to Brazilian inflation: An analysis between 2014 and 2023

ABSTRACT The price of fuels, and consequently, the price of oil, is one of the elements of historical relevance for the general price level that the Brazilian population faces, given the country’s dependency on the road transportation mode for product distribution and the potential ripple effects that the oil price naturally exerts on other sectors of the economy. Therefore, the aim of the present study is to quantify the influence of oil prices on the overall price index of the Brazilian economy for the period between January 2014 and May 2023. To achieve this objective, a model based on a hybrid New Keynesian Phillips curve was constructed, and the estimation was performed using Structural Vector Error Correction (SVEC) models. As a result, we find that, although oil prices are not the most significant drivers of inflation, their influence is present, positive, and persists for a considerable number of periods.

Permanent and temporary monetary policy shocks and the dynamics of exchange rates

We show the distinction between permanent and temporary monetary policy shocks is helpful to understand the impacts of monetary policy on exchange rates in the short as well as over the long run. Drawing on monthly data for several advanced economies from 1971 to 2019 and resorting to a simple structural vector error correction (SVEC) model, we find that a shock leading to a temporary increase in U.S. nominal interest rates leads to a temporary appreciation of the USD against the other currencies. In turn, a monetary policy shock leading to a permanent rise in nominal interest rates – e.g., one associated with a normalisation of monetary policy after a long period at the zero lower bound – results in a depreciation of the USD, in the short as well as over the long run that may contribute to higher (not lower) inflation also in the short run.

COMPARATIVE ANALYSIS OF THE IMPACT OF OIL PRICE SHOCKS ON SELECTED MACROECONOMIC VARIABLES IN NIGERIA AND EGYPT

This paper empirically analyzed the impact of oil price shock on selected macroeconomic variables in the African oil-producing countries using Nigeria and Egypt from 1983 to 2020 and 1984-2020 for Egypt, respectively, due to data availability. The study employed the structural vector error correction model (SVECM) due to co-integration among the variables. It was revealed that the response to and significance of the oil price shock differs between the two countries; oil price shock caused an economic boom in Nigeria while it shrunk the Egyptian economy; the broad money supply only responded to the oil price shock in Egypt; the general price level responds positively to the oil price shock in Egypt but respond negatively in Nigeria; oil price shock caused exchange rate depreciation in Nigeria but appreciation in Egypt, an oil price shock caused interest rate reduction in Nigeria but an increment in Egypt. Therefore, policymakers must consider the sensitive nature of their macroeconomic variables to oil price shocks and continue to make proactive decisions that will reduce, if not eliminate, its negative impact. Again, the government of oil exporting countries should ensure diversification of their economy during the oil boom in sectors that would help to caution against the impact of adverse oil price shocks in the economy when such is experienced.

Dynamic responses of energy prices to oil price shocks

PurposeThis study examines the dynamic responses of five different daily energy prices to a pulse shock affecting the daily price of oil.Design/methodology/approachDaily data for energy prices from the Federal Reserve Economic Data (FRED) database for January 7, 1997, through February 8, 2021, are analyzed. A bivariate structural vector error correction model and generalized autoregressive conditionally heteroscedastic model are combined and extended by adding the volatility of the growth rate of daily oil prices as an explanatory variable for the growth rates of energy prices. This model is estimated and used to generate impulse responses for energy prices.FindingsThe empirical results show that the levels of the daily energy prices examined have unit roots, are integrated of order one, are cointegrated, and generally revert slowly to their long-term equilibrium relationships with the price of oil. The growth rates for the daily energy prices have autoregressive conditional heteroscedasticity, generally are positively related to the volatility of daily oil prices, respond quickly to a pulse shock to daily oil prices, and have cumulative responses that last at least one month.Originality/valueThis paper allows for simultaneous estimation of extended bivariate structural vector error correction and generalized autoregressive conditionally heteroscedastic models that include the volatility of oil as an explanatory variable and uses these models to generated cumulative impulse responses for the growth rates of daily energy prices to oil price shocks.

Spillover effect among independent carbon markets: evidence from China’s carbon markets

Carbon pricing is one of the key policy tools in the green recovery of the post-COVID-19 era. As linkages among ETSs worldwide are future trend, the carbon price spillover effects among markets are needed to be explored. This study examines the spillover effects and dynamic linkages of carbon prices using the example of China’s pilot carbon markets during 2015–2019, which are seemingly independent carbon markets. A structural vector error correction model and an improved directed acyclic graph approach are applied. The main results are as follows. First, the linkages among the five pilots demonstrate features of “two small-world networks.” Specifically, these are the Guangdong and Hubei network and the Beijing, Shenzhen and Shanghai network. Second, Shenzhen, Beijing and Hubei ranked as the top three pilots in terms of external spillover effect, accounting for 36.25%, 29.76%, and 25.59%, respectively. Second, Guangdong pilot has increasing influence on the Hubei, Shenzhen and Beijing pilots. Third, trading activities are positive contributors to the spillover, while the allowance illiquidity ratio and volatility are negative factors. The findings imply that to retain an expectable abatement costs in achieving the climate goals in green recovery, carbon prices in other potentially related markets should be considered by the policy maker in addition to its own policy design.

The Dynamics of Structural Transformation in Australia, 1960–2020*

A dynamic long‐run general equilibrium framework based on a structural vector error correction model is used to study the factors determining the structural transformation of the Australian economy from 1960 to 2020. Three sets of shocks are identified: demand shocks through aggregate consumption, supply shocks caused by changes in relative prices across industries, and shocks arising from deviations between observed and long‐run industry employment shares. The empirical results highlight the importance of all shocks in causing the structural transformation of the Australian economy across industries and over time. The results are found to be robust to a range of sensitivity experiments.

Directed acyclic graph based information shares for price discovery

The possibility to measure the contribution of agents and exchanges to the price formation process in financial markets acquired increasing importance in the literature. In this paper I propose to exploit a data-driven approach to identify structural vector error correction models (SVECM) typically used for price discovery. Exploiting the non-Normal distributions of the variables under consideration, I propose a variant of the widespread Information Share measure, which I will refer to as the Directed Acyclic Graph based-Information Shares(DAG-IS), which can identify the leaders and the followers in the price formation process through the exploitation of a causal discovery algorithm well established in the area of machine learning. The approach will be illustrated from a semi-parametric perspective, solving the identification problem with no need to increase the computational complexity which usually arises when working at incredibly short time scales. Finally, an empirical application on IBM intraday data will be provided.

An index of cryptocurrency environmental attention (ICEA)

Purpose(1) A concern often expressed in relation to cryptocurrencies is the environmental impact associated with increasing energy consumption and mining pollution. Controversy remains regarding how environmental attention and public concerns adversely affect cryptocurrency prices. Therefore, the paper aims to introduce the index of cryptocurrency environmental attention (ICEA), which aims to capture the relative extent of media discussions surrounding the environmental impact of cryptocurrencies. (2) The impacts of cryptocurrency environmental attention on long-term macro-financial markets and economic development remain part of undeveloped research fields. Based on these factors, the paper will further examine the effects of the ICEA on financial markets or economic developments.Design/methodology/approach(1) The paper introduces a new index to capture cryptocurrency environmental attention in terms of the cryptocurrency response to major related events through gathering a large amount of news stories around cryptocurrency environmental concerns – i.e. >778.2 million news items from the LexisNexis News & Business database, which can be considered as Big Data – and analysing that rich dataset using variety of quantitative techniques. (2) The vector error correction model (VECM) and structural VECM (SVECM) [impulse response function (IRF), forecast error variance decomposition (FEVD) and historical decomposition (HD)] are useful for characterising the dynamic relationships between ICEA and aggregate economic activities.Findings(1) The paper has developed a new measure of attention to sustainability concerns of cryptocurrency markets' growth, ICEA. (2) ICEA has a significantly positive relationship with the UCRY indices, volatility index (VIX), Brent crude oil (BCO) and Bitcoin. (3) ICEA has a significantly negative relationship with the global economic policy uncertainty (GlobalEPU) and global temperature uncertainty (GTU). Moreover, ICEA has a significantly positive relationship with the industrial production (IP) in the short term, whilst having a significantly negative relationship in the long term. (4) The HD of the ICEA displays higher linkages between environmental attention, Bitcoin and UCRY indices around key events that significantly change the prices of digital assets.Research limitations/implicationsThe ICEA is significant in the analysis of whether cryptocurrency markets are sustainable regarding energy consumption requirements and negative contributions to climate change. Understanding of the broader impacts of cryptocurrency environmental concerns on cryptocurrency market volatility, uncertainty and environmental sustainability should be considered and developed. Moreover, the paper aims to point out future research and policy legislation directions. Notably, the paper poses the question of how cryptocurrency can be made more sustainable and environmentally friendly and how governments' cryptocurrency policies can address the cryptocurrency markets.Practical implications(1) The paper develops a cryptocurrency environmental attention index based on news coverage that captures the extent to which environmental sustainability concerns are discussed in conjunction with cryptocurrencies. (2) The paper empirically investigates the impacts of cryptocurrency environmental attention on other financial or economic variables [cryptocurrency uncertainty (UCRY) indices, Bitcoin, VIX, GlobalEPU, BCO, GTU index and the Organisation for Economic Co-operation and Development IP index]. (3) The paper provides insights into making the most effective use of online databases in the development of new indices for financial research.Social implicationsWhilst blockchain technology has a number of useful implications and has great potential to transform several industries, issues of high-energy consumption and CO2 pollution regarding cryptocurrency have become some of the main areas of criticism, raising questions about the sustainability of cryptocurrencies. These results are essential for both policy-makers and for academics, since the results highlight an urgent need for research addressing the key issues, such as the growth of carbon produced in the creation of this new digital currency. The results also are important for investors concerned with the ethical implications and environmental impacts of their investment choices.Originality/value(1) The paper provides an efficient new proxy for cryptocurrency and robust empirical evidence for future research concerning the impact of environmental issues on cryptocurrency markets. (2) The study successfully links cryptocurrency environmental attention to the financial markets, economic developments and other volatility and uncertainty measures, which has certain novel implications for the cryptocurrency literature. (3) The empirical findings of the paper offer useful and up-to-date insights for investors, guiding policy-makers, regulators and media, enabling the ICEA to evolve into a barometer in the cryptocurrency era and play a role in, for example, environmental policy development and investment portfolio optimisation.

Energy intensity determinants based on structure-oriented cointegration by embedding a knowledge box in a time series model: evidence from Iran.

Energy intensity reduction is an exigent issue for Iran, where energy consumption is so high. Therefore, finding effective policies to reduce energy intensity is essential. With this in mind, the impact of financial development, government investment, oil revenues, and trade openness on energy intensity is assessed in this study. We combined structural vector error correction model (SVECM) and directed acyclic graphs (DAG) technique to examine the relationships between study variables. The results of DAG prove that financial development, government investment, oil revenues, and trade openness influence the intensity of energy. Besides, the significant and long-run relationships among variables allowed us to apply SVECM. Impulse response functions and variance decomposition analysis indicate that government investment, oil revenues, and trade openness are negatively associated with the intensity of energy. Also, financial development positively influences energy intensity. Meanwhile, the impact of government investment is more significant than oil revenues, trade openness, and financial development impacts. So, government investment is the most effective policy regarding optimizing the consumption of energy and reducing energy intensity. We also advise policymakers to use oil revenues to increase government investment, enhance trade openness, and tax the private sector to improve the level of energy intensity.

The Dynamic Impact of High Oil Price Shock on the Algerian Economy: An Econometric Study during the Period (1980-2018) Using the SVECM Model

This study investigates the effect of high oil price shock on the Algerian economy, during the period (1980-2018) using the structural vector error correction model. The results of the Johansen test indicate the existing of a long-run equilibrium relationship between the study variables, The impulse response function (IRF) showed that the structural oil price shock is great and positively affects the GDP per capita and negatively effects the unemployment rate and the exchange rate in the short and long term, as for the inflation rate response it was negative in the short run, so it became positive in the long run. the results of the variance decomposition revealed that the oil price explains most of the changes that occur in unemployment rate and the GDP per capita, as well as the weak percentage of the changes that happen in both inflation rate and exchange rate in the short and long term.

Directed Acyclic Graph Based Information Shares for Price Discovery

The possibility to measure the contribution of agents and exchanges to the price formation process in financial markets acquired increasing importance in the literature. In this paper I propose to exploit a data-driven approach to identify structural vector error correction models (SVECM) typically used for price discovery. Exploiting the non-Normal distributions of the variables under consideration, I propose a variant of the widespread Information Share measure, which I will refer to as the Directed Acyclic Graph based-Information Shares(DAG-IS), which can identify the leaders and the followers in the price formation process through the exploitation of a causal discovery algorithm well established in the area of machine learning. The approach will be illustrated from a semi-parametric perspective, solving the identification problem with no need to increase the computational complexity which usually arises when working at incredibly short time scales. Finally, an empirical application on IBM intraday data will be provided.

U.S. Monetary Policy and Commodity Prices: A SVECM Approach

AbstractThis article discusses how U.S. monetary policy shocks can influence commodity prices. Historically, a lot of studies in the literature have investigated the impact of short‐term interest rates on different types of commodity prices. This article takes a more comprehensive approach and contributes to the literature by analysing the effect of both short‐ and long‐term interest rates as well as M2 money stock on commodity prices at aggregate and sub‐indices levels. The “B‐model” variant of structural vector error correction models (SVECMs) is used to estimate the restricted contemporaneous impact matrix (SR) and the restricted long‐term impact matrix (LR). Furthermore, SVECMs impulse response functions are used to evaluate the extent to which monetary policy shocks explain commodity prices. In contrast with the results of previous studies, we do not find evidence of a strong response pattern of commodity prices to monetary policy shocks in the short term. However, monetary policy shocks can explain commodity prices and their components in the long term. From a policy point of view, monetary authorities should exercise caution in using short‐term effects of monetary policy instruments on commodity prices, given their long‐term impact is inflationary while there are no beneficial short‐term effects.

Structural vector error correction modelling of Bitcoin price

Over the period from January 2011 to December 2019 we analyze the impact of shocks on the financial markets of emerging and developed countries on the price of Bitcoin using the structural vector error correction model. Results differ according to the duration and area selected. There is evidence of higher impact in short-term than in long-term. In the short term, bitcoin prices are positively influenced by Asian emerging countries and all countries areas, negatively by North America. In the long term, only all countries in Asia, the Pacific (negative) and Europe (positive) impact the price of bitcoin. Regardless of the duration, there is no influence of shocks in all other zones on the price of Bitcoin. The analysis of the health shock of COVID-19 and its impact on financial markets supports the results found. The results are potentially useful to academics, practitioners, and Bitcoin market participants to better facilitate risk-management-decisions.

Dynamic impacts of SME stock market development and innovation on macroeconomic indicators: A Post-Keynesian approach

This study is grounded on the propositions of Kaleckian–Post-Keynesian​ macroeconomic framework to explore the dynamic impacts of SME (Small and Medium Enterprise) stock market development and innovation on key macroeconomic variables in Hong Kong, Singapore, Thailand, and Malaysia. For the reported empirical analysis, a Structural Vector Error Correction (SVEC) model and an impulse response function (IRF) were adopted. The evidence shows that SME stock market development and/or innovation have small but positive impacts on short-run economic stimulation. The SME stock market development promotes economic growth through the combination of the following channels: private investment, savings and productivity in Hong Kong, Singapore, and Thailand. Innovation, on the other hand, fosters growth through the combination of these channels and the employment channel in all four countries.

The impact of fiscal shocks on real GDP and income inequality: What do Australian data say?

This paper examines the impact of three fiscal policy shocks on per capita real GDP and income inequality in Australia during the period 1965–2014. A small structural vector autoregressive (SVAR) model is constructed for an open economy for contemporaneous identification and estimation purposes. Based on the evidence of one cointegrating vector among the variables, a structural vector error correction (SVEC) model is specified for the long run. Direct taxation, indirect taxation receipts and government spending are identified as permanent fiscal policy shocks. The convergent use of two different models (SVAR & SVEC) strengthens the credibility of the results. The results have three key policy implications. First, a reduction in direct taxation receipts increases per capita real GDP without increasing income inequality. Second, a reduction in government expenditure significantly increases income inequality. Third, the adverse effect of indirect taxation receipts on income inequality is greater than the redistributive effect of government expenditure, which questions the widely held fiscal policy strategy of using indirect taxation to finance redistributive expenditure.

The role of globalization, real income, tourism in environmental sustainability target. Evidence from Turkey

In this study, we examine the role of real income, globalization and tourism on environmental sustainability target by applying Autoregressive Distributed Lag (ARDL) bounds testing approach that controls for structural breaks and Vector Error Correction Model (VECM) Granger causality approach that produces robust, efficient and reliable short-run and long-run estimates in the case of Turkey over the periods 1970–2014. To achieve our research objective, we examine stationarity properties of the series via unit root test after which we applied Bayer-Hanck combined cointegration technique to evaluate the presence of a long-run cointegration relationship among the series. The empirical results show that a 1% increase in real income level and international tourists' arrivals led to 0.625% and 0.129% increase in metric ton per capita CO2 emissions in the short-run and 0.345% and 0.071% increase in metric tons per capita CO2 emissions in the long-run, while globalization has non-significant negative impacts on CO2 emissions. The causality analysis suggest that tourism Granger causes CO2 emissions both in the short- and long-run, while real income and globalization only Granger cause CO2 emissions in the long-run. Findings also show that a one standard deviation shock to CO2 emissions has a noticeable positive and persistent impact on tourism, globalization and economic growth in the long-run.

MONEY SUPPLY VOLATILITY AND THE MACROECONOMY

This paper extends the ongoing literature on the macroeconomic effects of money supply volatility. We use monthly data for the USA and a bivariate, Markov switching, structural vector error correction model that is modified to accommodate generalized autoregressive conditional heteroscedasticity-in-mean errors to isolate the effects of money growth volatility on output growth. The model allows us to study how monetary uncertainty affects economic growth across different macroeconomic regimes.

Dynamic Analysis of Fiscal Policy in the United Kingdom

<p><em>This paper studies the effects of fiscal stimuli on the real GDP of the United Kingdom for the period of 1997 through the first quarter of 2017. Structural vector autoregressive and vector error correction models are estimated. Impulse responses from both models provide support for the Keynesian view that fiscal stimuli are associated with rises in the real GDP. Variance decomposition analysis shows that over time, depending which model is considered; tax cuts impart a positive effect on the real GDP in the range of 5 to 20 percent. Government expenditure shocks account for 8 to 15 percent of variations in the real GDP based on the two models. The multipliers of tax cuts and government expenditures initially rise reaching a peak in the ninth quarter and decline to 1.60 and 1.74 in three years, respectively. </em></p>

Crowding-out or crowding-in? Public and private investment in India

Abstract This paper contributes to the debate on the relationship between public and private investment in India along the following dimensions. First, acknowledging major structural changes that the Indian economy has undergone in the past three decades, we study whether public investment in recent years has become more or less complementary to private investment in comparison to the period before 1980. Second, we construct a novel data-set of quarterly aggregate public and private investment in India over the period 1996–2015 using investment-project data from the CapEx-CMIE database. Third, embedding a theory-driven long-run relationship on the model, we estimate a range of Structural Vector Error Correction Models (SVECMs) to re-examine the public and private investment relationship in India. Identification is achieved by decomposing shocks into those with transitory and permanent effects. Our results suggest that while public investment crowds out private investment in India over the period 1950–2012, the opposite is true when we restrict the sample to post 1980 or conduct a quarterly analysis since 1996. This change can likely be attributed to the policy reforms which started during the early 1980s and gained momentum after the 1991 crisis.

Money Supply Volatility and the Macroeconomy

This paper extends the ongoing literature on the macroeconomic effects of money supply volatility. We use monthly data for the United States and a bivariate, Markov switching, structural vector error correction (VEC) model that is modified to accommodate GARCH-in-Mean errors to isolate the effects of money growth volatility on output growth. The model allows us to study how monetary uncertainty affects economic growth across different macroeconomic regimes.