Structural Vector Autoregression Analysis Research Articles

The Impact of Fiscal Policy on Macroeconomic Conditions in Pakistan: Insights from a Structural Vector Autoregression Analysis

The Impact of Fiscal Policy on Macroeconomic Conditions in Pakistan: Insights from a Structural Vector Autoregression Analysis

Spillover effects of energy transition metals in Chile

This paper examines the impact of spillover effects of energy transition metals in Chile. The study aims at identifying the causal relationships among these energy transition metals and other major assets like oil, gold, and bitcoin, and how they shape Chile’s economy, both pre-COVID-19 and during the first months of the pandemic. Our Structural Vector Autoregressive analysis is carried out with daily frequency data for two separate periods, the pre-COVID-19 analysis (the whole of 2018) and the COVID-19 analysis (Feb 2020 to end of 2020). Our results suggest that Chile has been highly responsive to renewable energy shocks during COVID-19, in concert with an increased responsiveness to traditional oil-driven shocks. This lends weight to the idea that traditional energies are still relevant and concomitant with energy transition, especially in a small open resource-oriented economy like Chile. In addition to that, bitcoin shocks seem to have contributed to Chile’s transition, likely as a result of bitcoin’s extensive use of energy and its potential to increase renewable energy capacity. Policywise, our findings call for a watchful eye on regulatory constraints, excessively high royalties, and the maintenance of stabilization funds to mitigate uncertainty, all of which can affect Chile’s competitive edge at an untimely hour.

Cyclical dynamics and co-movement of business, credit, and investment cycles: empirical evidence from India

The paper aimed to investigate the cyclical dynamics of the business, credit, and investment sectors in India. This was achieved by utilizing annual data from 1980 to 2021 and investigating the impact of domestic and global financial cycles on the business cycle. The cycles were derived using the Hodrick–Prescott filter, and structural vector autoregression (SVAR) and Granger causality tests were employed to establish the dynamic interactions among these cycles. The results of the study revealed a clear divergence between domestic and global financial cycles. Additionally, the SVAR analysis confirmed the presence of a long-run relationship between business, investment, and credit cycles. Notably, the findings suggest that credit cycles can provide valuable insights to manage business cycles in India. Finally, robustness checks were conducted to confirm the reliability of SVAR findings.

Uncertainty and international fund flows: A cross-country analysis

This paper investigates the impacts of uncertainty on international portfolio allocation decisions and returns across 27 countries. Using panel structural vector autoregressive analysis, a positive link between both country-level and global uncertainty with equity fund flows is uncovered, highlighting the role of investor sentiment in capital allocation decisions. Findings also support the portfolio rebalancing theory, revealing the negative influence of past returns on fund flows. Results show that the VIX demonstrates a positive association with fund flows, particularly during periods of elevated global uncertainty. Moreover, this research analyzes differences between developed and emerging markets in their sensitivity to uncertainty shocks.

Statistical identification in panel structural vector autoregressive models based on independence criteria

SummaryThis paper introduces a novel panel approach to structural vector autoregressive analysis. For identification, we impose independence of structural innovations at the pooled level. We demonstrate robustness of the method under cross‐sectional correlation and heterogeneity through simulation experiments. In an empirical application on monetary policy transmission in the Euro area, we find that bond spreads rise significantly after an unexpected monetary tightening. Furthermore, the central bank responds to offset effects of adverse financial shocks. Additionally, we document sizable heterogeneity in country‐specific output responses.

Sequencing the COVID‐19 Recession in the USA: What Were the Macroeconomic Drivers?

AbstractWe apply a structural vectorautoregressive analysis to decompose fluctuations in the growth rate of industrial production and inflation precipitated by the COVID‐19 pandemic in the USA into aggregate demand, aggregate supply, and uncertainty shocks. While all three types of shocks contributed to output and inflation dynamics, the surge in economic uncertainty contributed to the decline in output more strongly than aggregate demand or aggregate supply disruptions. In 2021, the decline in uncertainty and adverse aggregate supply shocks emerged to be similarly important in spurring inflation.

A macroeconomic viewpoint using a structural VAR analysis of silver price behaviour

This article investigates silver price as a fluctuating commodity price since the financial crisis of 2007–2009. In this regard, a structural vector autoregression (VAR) was applied to observe the sensitivity of the silver price and future pricing due to changes in macroeconomic variables and to review changes in macroeconomic variables due to changes in the silver price. The main results show that the silver price is susceptible to changes in the gold price, increasing sideways. A shock to OECD GDP caused the silver price to increase which makes logical sense, thus showing a positive correlation between output and the silver price. A shock to the oil price caused the silver price to spike over the short term, then move sideways over the long term. A shock to the US Federal funds rate caused the silver price to dip over the short term, then increase slightly over the medium and move sideways over the long term, while a shock to the real effective exchange rate of the USA caused the silver price to increase sideways. The article sheds some light on the reactive status of the silver price to macroeconomic variables and its influence as a safe haven commodity.

Does exporting cause productivity growth? Evidence from Chilean firms

Does exporting increase firm productivity, or are increased export sales caused by the firm’s ability to increase its productivity? This paper provides new empirical evidence on the causal relation between trade and productivity, adopting a structural vector autoregressive analysis combined with identification algorithms from the machine learning literature. We focus on a well-studied country (Chile) and on already-exporting firms (intensive margin). We identify the contemporaneous and lagged causal structure between firm productivity and export growth using two different machine learning algorithms based on independent components analysis (ICA), which exploit the non-Gaussian distribution of the data to recover the independent structural shocks that drive the observed variables. Our findings show that, for Chilean firms, productivity growth causes export growth in the same year, but that the reverse does not apply. Export growth also has no causal effect on TFP growth in subsequent years. To increase sales in the foreign market, firms should first also increase productivity. The increased presence in the foreign market does not contribute to such productivity growth.

The global impact of financial development on renewable energy in a panel structural vector autoregression analysis

AbstractFinancial development is likely to benefit the use of renewable energies, particularly those lacking environmental conditions and significant external capital. However, the impact of financial development on alternative renewable energy sources has yet to be documented in different income‐level countries. Using a unique panel data set of 62 countries with three income levels for the 2000–2019 period, we investigate how financial development impacts renewable energy on a global scale by using a panel structural vector autoregression analysis (PSVAR). Our empirical findings indicate that financial development significantly affects renewable energy installment and consumption, while FDI takes a negative impact on SOL sources. We further find that wind energy source to financial development shock exhibits a higher impact in high‐income countries. Our findings provide new insights concerning renewable energy in different countries using the PSVAR model, which may inspire scholars interested in this research field.

Identification of Structural VAR Models via Independent Component Analysis: A Performance Evaluation Study

Independent Component Analysis (ICA) is a statistical method that linearly transforms a random vector. Under the assumption that the observed data are mixtures of non-Gaussian and independent processes, ICA is able to recover the underlying components, but with a scale and order indeterminacy. Its application to structural vector autoregressive (SVAR) models allows the researcher to recover the impact of independent structural shocks on the observed series from estimated residuals. We analyze different ICA estimators, recently proposed within the field of SVAR analysis, and compare their performance in recovering structural coefficients. Moreover, we assess the size distortions of the estimators in hypothesis testing. We conduct our analysis by focusing on non-Gaussian distributional scenarios that get gradually close to the Gaussian case. The latter is the case where ICA methods fail to recover the independent components. Although the ICA estimators that we analyze show similar pattern of performance, two of them — the fastICA algorithm and the pseudo-maximum likelihood estimator — tend to perform relatively better in terms of variability, stability across sub- and super-Gaussian settings, and size distortion. We finally present an empirical illustration using US data to identify the effects of government spending and tax cuts on economic activity, thus providing an example where ICA techniques can be used for hypothesis testing.

Policy Intervention and Financial Sustainability in an Emerging Economy: A Structural Vector Auto Regression Analysis.

The purpose of the study is to observe the impact of policy intervention on financial sustainability using the structural vector autoregression (SVAR) analysis. The population of the study is the manufacturing sector of Pakistan, which is an emerging economy. Data for 249 firms operating in the manufacturing sector are taken, collected from Datastream from 2005 to 2019, with total observations of 2,400. To conduct the analysis, R software is used for its better visualization. Results show that firm performance, corporate governance, and sectoral policies have a positive and long-term impact on financial sustainability, whereas earning management and financialization not only have a negative impact, but this impact affects the operations of the corporate for a longer period. This study would be helpful for policymakers as it gives a framework for financial sustainability based on the policies and strategies developed by the sector.

The effects of social infrastructure and gender equality on output and employment: The case of South Korea

This paper examines the short-run and medium-run impact of spending in social infrastructure, defined as expenditure in education, childcare, health and social care, wages and gender pay gap on output and employment of men and women for the case of South Korea. Based on a gendered post-Kaleckian feminist macroeconomic theoretical model, we estimate the macroeconomic effects of social expenditure, wages and gender pay gap using a structural vector autoregression (SVAR) analysis for the period of 1970–2012. The results show that an increase in the public social infrastructure significantly increases the total non-agricultural output and employment in South Korea both in the short and medium run. Moreover, we find that higher social infrastructure expenditure increases female employment more than male employment in the short run and raises both male and female employment in the medium run due to increasing output. Finally, the results show that South Korean economy is gender equality-led in the medium run, although the effects are economically small in comparison to the strong effects of increases social infrastructure spending. The results indicate that sustainable equitable development and a substantial increase in employment requires a mix of both labour market and fiscal policies.

Instrumental Variable Identification of Dynamic Variance Decompositions

Macroeconomists increasingly use external sources of exogenous variation for causal inference. However, unless such external instruments (proxies) capture the underlying shock without measurement error, existing methods are silent on the importance of that shock for macroeconomic fluctuations. We show that, in a general moving-average model with external instruments, variance decompositions for the instrumented shock are interval-identified, with informative bounds. Various additional restrictions guarantee point identification of both variance and historical decompositions. Unlike structural vector autoregression analysis, our methods do not require invertibility. Applied to US data, they give a tight upper bound on the importance of monetary shocks for inflation dynamics.

Analysis of the impact of public health emergencies on the dry bulk shipping market

A structural vector autoregressive model and spillover index analysis based on generalized prediction error variance decomposition were used to explore the impact of public health emergencies on the dry bulk shipping market and provide suggestions for addressing the impact of public health emergencies. Moreover, the risk fluctuation and spillover of the dry bulk shipping market during public health emergencies were analyzed to understand the ways in which public health emergencies impact the dry bulk shipping market and to quantify the impact intensity. In related studies, the influence of the international crude oil price index and dry bulk ship port berthing volume were also considered. The results show that considering the immediate impact, the increase of newly confirmed cases of COVID-19 has a significant impact on the dry bulk shipping market, which lasts for more than 3 weeks and is always a negative shock. Different types of public health emergencies have different effects on the dry bulk shipping segmented shipping market. Dry bulk shipping companies should fully understand the development of public health emergencies, make full use of risk aversion forecasting tools in financial markets and make deployments for different situations.

Heterogeneous effects of oil structure and oil shocks on stock prices in different regimes: Evidence from oil-exporting and oil-importing countries

This paper examines whether or not the oil structure of economies, the source of oil shocks, and regime changes affect stock price responses. After dividing the entire period into two regimes (bull and bear market) and considering three different oil shocks: global oil price, global oil demand, and global oil supply, we attempted to select countries that are considered developed economies while demonstrating differences and similarities in their oil structures. To this end, we compare the effects of oil shocks on the stock prices of Norway and Canada, two oil-exporting countries, to China and Japan, two oil-importing countries, using the Threshold Structural Vector Autoregression (TSVAR) analysis for the period 1990:1–2020:4. The results indicate that the structure of the oil and the source of the oil shocks have a significant effect on the responses. In this regard, the responses of oil-exporting countries are strikingly similar to one another and dissimilar to those of the two oil-importing countries. All of the oil shocks considered thus far have had a short-term effect but have been neutral in the medium and long term (except for oil demand shocks in the bull market). Additionally, the stock market's responses to oil shocks have been regime-dependent, with most responses being more sharply in a bear market than in a bull market. According to the findings, there is a strong link between the stock market, the source and type of oil shocks (positive or negative), economics' oil structures, and regime changes, which may influence policymakers' response to various oil shocks in different stock market conditions.

Causality in structural vector autoregressions: Science or sorcery?

AbstractThis paper presents the structural vector autoregression (SVAR) as a method for estimating dynamic causal effects in agricultural and resource economics. We have a pedagogical purpose; we aim the presentation at economists trained primarily in microeconometrics. The SVAR is a model of a system, whereas a reduced‐form microeconometric study aims to estimate the causal effect of one variable on another. The system approach produces estimates of a complete set of causal relationships among the variables, but it requires strong assumptions to do so. We explain these assumptions and describe similarities and differences with the classical instrumental variables (IV) model. We demonstrate that the population analogue of the Wald IV estimator for a particular causal effect is identical to the ratio of two impulse responses from an SVAR. We further demonstrate that incorrect identification assumptions about some components of the SVAR do not necessarily invalidate the estimated causal effects of other components. We present an SVAR analysis of global supply and demand for agricultural commodities, which was previously examined using IV. We illustrate the additional economic insights that the SVAR reveals, and we articulate the additional assumptions upon which those insights rest.

The determinants of CO2 prices in the EU emission trading system

In 2005, the European Union launched its Emissions Trading System (ETS), the first and one of the largest international carbon markets aimed at reducing member states' CO2 emissions. Policymakers tend to use the carbon price as an indicator of the “health” and effectiveness of the ETS mechanism, although this measure is influenced by many other energy and climate policies, energy market fundamentals, and speculative shocks. This paper develops a model that links the energy sector (oil, natural gas, coal, electricity prices, and the share of fossil fuels in electricity generation), economic activity, and the carbon price. The model can be used as a monitoring tool for carbon price dynamics. We represent the model empirically through a Structural Vector Autoregression and use frequency-domain analysis to distinguish the effects of changes in fundamental factors from shocks to market microstructure. Our empirical results show that up to 90% (65% on average) of the fluctuations in the carbon price, adjusted for supply effects, are explained by fluctuations in fundamental market variables; however, the individual contributions are not stable. Overall, our results suggest that the ETS has started to work well.

Assessing the Financial Implications of COVID-19 Within the SVAR Framework for Some Asian Countries

This article examines the structural responses of foreign exchange and equity markets to the COVID-19 pandemic in seven Asian countries over its first 4 months (31 December 2019 to 1 May 2020). Marginal effects derived from a structural vector autoregression (SVAR) model suggest that a 1% increase in incidence of COVID-19 cases significantly diminished Indonesia’s equity market returns by 4.7%, depreciated the Indian rupee against the US dollar by 4.8%, but improved equity prospects in South Korea by 4.1%. For the other financial markets, the effect of COVID-19 was found to be insignificant. Further, the impulse response analyses imply that the influence of COVID-19 on foreign exchange and equity markets is only transitory in nature. Additional SVAR analysis for India and Indonesia over recent months (2 May 2020 to 22 January 2021) showed that their financial markets remained (or became) resistant to the escalating incidence of COVID-19 inflections and deaths.JEL Code: G15

Structural transmissions among investor attention, stock market volatility and trading volumes

AbstractWe employ data‐based approaches to identify the transmissions of structural shocks among investor attention measured by Google search queries, realised volatilities and trading volumes in the United States, the United Kingdom and the German stock market. The two identification approaches adopted for the structural vector autoregressive analysis are based on independent component analysis and the informational content of disproportional variance changes. Our results show robust evidence that investors' attention affects both volatilities and trading volumes contemporaneously, whereas the latter two variables lack immediate impacts on investors' attention. Some movements in investors' attention can be traced back to market sentiment.

Rara Avis: Latin American populism in the 21st century

Since the beginning of the 21st century, many Latin American countries have been ruled by governments characterized as populist (the so-called new Latin American Left). We focus on the macroeconomic implications of the policies adopted by these governments (instead of their leaders’ rhetoric) and we investigate to what extent this characterization holds. In particular, we focus on their wage policies by doing a Structural Vector Autoregressive analysis and assuming that populist shocks have no long-run effects on real wages. This identification implies that populist leaders prioritize redistribution through nominal wages disregarding the evolution of productivity. The results indicate that economic populism is not as widespread as previously thought. Instead, our approach leads to more nuanced results: while we find that there is populism in Argentina, the results for Brazil, Bolivia and Ecuador show only sporadic populist events. In the remaining countries, we do not find persistent economic populism.