Structural Vector Autoregressive Model Research Articles

Assessing the extent and persistence of major crisis events in the crude oil market and economy: evidence from the past 30 years

This study investigates the extent and persistence of major crisis events in the crude oil market and economy and searches for general rules of event impact. Although the short-term effects of such crises may quickly become evident, their long-term implications can be challenging to uncover. To this end, we analyzed 50 major crisis events across four categories using a Proxy structural vector autoregressive (Proxy-SVAR) model. The results show that these events substantially impacted crude oil production, world crude oil prices, and inflation in China and the United States. Generally speaking, major crisis events have a more severe impact on the crude oil market, but their impact on the economy lasts longer. Notably, geopolitical conflicts have led to a sharp decline in S&P500, resulting in a simultaneous reduction in the industrial production indices of the world, China, and the US. Among the different types of crises, the financial crisis had the most prolonged impact, persisting for approximately four years. The public health emergency represented by COVID-19 resulted in a decline in actual oil prices, with its impact on the oil market continuing for over three years. Considering our findings, we suggest four policy measures to enhance economic resilience.

Terrorism and Economic Growth in Nigeria

Terrorism has emerged as a significant threat to global security and economic stability, with Nigeria experiencing its detrimental effects. This empirical study investigated the relationship between terrorism and economic growth in Nigeria. The study focused on the impact of terrorism on productivity in Nigeria, examining various aspects including economic growth, trade, foreign direct investment (FDI), manufacturing output, and other key indicators but with specific focus on economic growth. It highlighted the pervasive nature of terrorism in Nigeria, with various groups such as Boko Haram, ISIS, Fulani herdsmen, and Niger Delta militants contributing to violence and insecurity. The study reviews both empirical and theoretical literature on terrorism and its economic consequences, emphasizing the negative relationship between terrorism and economic growth. Using a positivist research paradigm, the study employed a Structural Vector Autoregression (SVAR) approach to analyze the impact of terrorism on economic variables in Nigeria. The SVAR model incorporated endogenous variables such as economic growth, capital, terrorism, and trade. Data covering a 42-year period from 1980 to 2021 were utilized, and they were sourced from the World Bank and World Development Indicators. The estimation and results section includes discussions on lag length selection criteria and impulse response analyses to understand the dynamic effects of terrorism on economic variables over time. In conclusion, the study underscored the detrimental impact of terrorism on productivity and economic growth in Nigeria, highlighting the need for effective counter-terrorism measures to mitigate these effects. It provides valuable insights for policymakers and researchers aiming to address the complex challenges posed by terrorism in the country.

US trade policy and the US dollar

We show that the US dollar response to trade policy uncertainty (TPU) is key to assessing the impact of US trade policy. Employing structural vector autoregressive models, we find that TPU shocks supported a multilateral USD appreciation during the 2018–19 trade tensions between the US and some of its major trading partners. We rationalize this in a two-country New Keynesian model with financial frictions that links the increase in TPU to rising demand for safe USD assets. Our findings suggest that the TPU-induced appreciation of the USD in 2018–19 significantly counteracted US trade policy attempts to raise US competitiveness.

Effect of fuel prices on sailing speeds in short-sea shipping

ABSTRACT In this paper, we estimate fuel price elasticities of sailing speed for general cargo vessels using data covering the Baltic Sea and Norwegian coastal areas during the years 2007–2018. To disentangle the correlation between fuel prices and freight rates arising from changes in global demand, we instrument fuel prices on price changes triggered by supply shocks. The shocks are derived from the structural vector autoregressive (SVAR) model proposed by Kilian (2009). In a sensitivity analysis, we use unanticipated oil supply shocks derived from oil supply outages as an alternative instrument for fuel prices. We find that disregarding the correlation between fuel prices and freight rates yields biased parameter estimates, which can partly explain the mixed results in the previous literature. Our preferred estimate for the fuel price elasticity of speed is −0.08.

Analysis and forecasting of the dry bulk shipping market: structural VAR models using FFA-spot-time charter rates

ABSTRACT This study investigates dynamic interactive patterns of the dry bulk shipping markets through the lens of structural vector autoregression (VAR) models. There are two distinct hypotheses in the extant literature. One is that asymmetry between spot and forward freight agreement (FFA) markets causes FFA prices to respond more rapidly to new information than spot prices, termed the Physical-Paper Hypothesis in this paper. The other hypothesis is that there are some diverse shocks with different propagation mechanisms, termed the Shock-Diversity Hypothesis. This paper combines these two seemingly separate hypotheses at one time in two illustrative structural VAR models, based on a dataset of weekly spot, FFA, and time charter (TC) rates in the dry bulk shipping markets. The two structural VAR models show that spot, FFA, and TC rates have different, particularly dynamic responses to identified permanent shocks. Furthermore, they show that the reduced-form tri-variate FFA-Spot-TC VAR model produces more accurate forecasts of future spot rates than the bi-variate FFA-Spot and TC-Spot VAR models. Therefore, market participants can use VAR models as an information-processing tool for understanding of (e.g. data descriptions and structural inferences), and forecasting in, dry bulk shipping markets.

Efficacy of Monetary Policy Transmission During the Flexible Inflation Targeting Regime in India

This article builds a Structural Vector Autoregressive model and employs non-recursive identification restrictions to examine the effectiveness of the Monetary policy transmission mechanism in India during the Flexible Inflation Targeting regime (2016–2023). The results indicate that policy rate shocks have a significant negative impact on domestic output and prices during this regime. The findings further, reveal evidence of an exchange rate puzzle during the Flexible Inflation Targeting regime. Our results give credence to the RBI’s move towards a Flexible indicator targeting approach as all the macroeconomic variables of interest, that is, Domestic output and inflation produce plausible estimates in response to a monetary policy shock. JEL Classification: C32, E52, E5, E52, F41

The Impact of Quantitative Easing on Stock Prices in the U.S. Stock Market - Structural VAR Model Analysis

In an effort for future investment implications, the current study examined the relationship between historical Quantitative Easing (QE) programs and the stock market. This study analyzed weekly basis data in the United States between 2007 and 2023. Through a Vector auto-regression (VAR) model, a significant rise following a quick drop in stock prices to a positive shock in QE was discovered, indicating lagged response. Further robustness check proved the validation of relationship, by sectioning the whole data set into 4 periods corresponding to four QE programs, except relatively insignificant effects in QE2 and QE3 periods. These lagged responses were pointed out firstly in this paper and connected with policy motivations and contemporaneous economy environment across programs. The findings may enable investors to anticipate market movements and generate respectable returns in the event of such unconventional money shocks.

Geopolitical risk, supply chains, and global inflation

AbstractThis paper investigates the effects of global geopolitical risks and global supply chain pressures on global inflation for the monthly period of 1999M1–2022M12. The investigation is based on a structural vector autoregression model, where the effects of global oil prices and global monetary policy are controlled for. Four alternative measures of inflation are used, including headline, core, food, and energy inflation. The empirical results show that disruptions in global supply chains are the main drivers of global inflation in the long run as the corresponding shocks explain the lion's share of volatilities in headline inflation (by 32%), core inflation (by 30%), and food inflation (by 22%), followed by oil price shocks and policy rate shocks. In comparison, energy inflation is explained the most by oil price shocks (by 55%) followed by supply chain shocks and policy rate shocks. Positive supply chain pressure and oil price shocks have positive and statistically significant effects on headline inflation even after five years, whereas positive policy rate shocks have negative and statistically significant effects on headline inflation in the long run. In contrast, positive shocks to geopolitical risk result in higher headline inflation only up to one year, with insignificant effects in the long run. Several policy implications follow.

The effect of structural oil shocks on stock returns of Indian renewable energy companies across market conditions

Purpose This study aims to evaluate the structural oil shocks effect on stock returns of Indian renewable energy companies across market conditions. Design/methodology/approach This study applies the structural vector autoregression model to estimate sources of oil shocks such as oil supply shock, aggregate demand shock and oil price-specific demand shock. In the next step, the panel quantile regression model estimates the effect of these oil shocks on stock return across market conditions. Monthly data are collected from January 2009 to December 2019. All renewable energy companies listed on the National Stock Exchange of India are considered for the analysis. Findings In the whole sample analysis, this study finds that oil shocks negatively affect stock returns in most of the market conditions except oil price-specific demand shock. In sub-groups, oil shocks driven by supply and aggregate demand also negatively affect stock return in most market conditions. This study finds the positive interaction of oil price-specific demand shock. A majority of these positive interactions happen in bearish market conditions. In the whole sample, the asymmetric effects of shocks driven from oil supply and oil price-specific demand are seen in most quantiles or market conditions. At the same time, aggregate demand shock does not affect asymmetrically. In the sub-group analysis, standalone renewable energy companies stock returns are least asymmetrically affected by these oil shocks. The asymmetries of oil supply-driven shock on stock returns of the renewable energy sub-group companies are found in most quantiles. Originality/value First, this is a company-level study of the stock returns response to the structural oil shocks in the renewable energy sector. Second, to the best of the authors’ knowledge, this type of study is the first in the Indian context. Third using panel quantile regression model along with capital asset pricing model framework, the authors investigate these effects across market conditions.

Fiscal Policy, Government Ideology, and Economic Activity: Evidence from OECD Countries

AbstractThis study explores the role played by a country's political environment in the origin and effect of government spending. Using a rich data set that contains time‐varying ideological party positions, I show that both government ideology and the timing of elections are key determinants of government spending shocks and Granger‐cause government spending shocks estimated from traditional structural vector autoregressive (SVAR) models that ignore political considerations. I find that the magnitude of the spending multiplier depends on the political context: highly polarized countries have smaller multipliers relative to countries with a low degree of polarization, and established democracies have smaller multipliers than younger democracies.

Do higher global oil and wheat prices matter for the wheat flour price in Lebanon?

AbstractThis article investigates the effect of global oil and wheat prices, and local price shocks on the real price of wheat flour in Lebanon. We estimate a structural vector autoregressive model with exogenous variables (SVARX) using Bayesian methods. We then compute the impulse response functions and find that global commodity price shocks play a trivial role. Meanwhile, local gasoline price and wheat flour price‐specific shocks trigger large increases in the Lebanese wheat flour price on impact. Furthermore, since 2020, local gasoline price and wheat flour price‐specific shocks have contributed the most to the historical variation in the Lebanese wheat flour price.

The impact of carbon policy news on the national energy industry

This paper explores the impact of unexpected changes in European carbon policy on Portugal’s energy sector, focusing on effects on sales, output prices, and labor market dynamics. Using a structural vector autoregression (SVAR) model, the study finds that news of tighter carbon regulations leads to a significant short-term increase in domestic sales. Output prices rise in both home and foreign markets, with a larger increase observed in the latter. The labor market responds positively, as evidenced by higher wages and hours worked. The study also reveals that these carbon policy changes have played a significant role in historical fluctuations within the energy sector, especially during the Great Financial Crisis and key policy changes. The findings highlight the importance of judicious policymaking concerning carbon regulations, as the escalation in energy prices wields significant economic effects, though not all of these effects are bad from the energy industry standpoint.

Rising land values and the post-1978 growth-inequality relationship

ABSTRACT Since the late 1970s, the United States has experienced a sudden change from a positive relationship between growth and inequality to a negative one. This paper suggests that the rise in land values lies behind this. To demonstrate this hypothesis, I perform a set of panel structural vector autoregressive models with heterogeneous dynamics, using a comprehensive dataset of the United States at the state level. The estimation results highlight that the rapid growth in land values over the last four decades has rarely contributed to the productive capacity of the economy (resulting in falling growth) but instead has aided to the pursuit of personal enrichment by the wealthy (leading to increasing inequality).

The impact of US housing demand and supply shocks on the Australian economy: Analysis implementing a SVAR model

AbstractThis thesis develops an open economy structural vector autoregression model to determine how the Australian economy is affected by both a US housing demand shock and a US housing supply shock. Previous literature has either grouped Australia with other economies or has excluded Australia altogether. This leaves a significant literature gap in explaining how the Australian economy is solely impacted. The results of the model indicate both a US housing demand and a US housing supply shock significantly impact the Australian economy, with the most significant being the impact of a US house price shock upon Australian GDP which is large and persistent over time. The results contribute to the understanding of how Australian policymakers should incorporate the US housing market into policy decisions and central bank modelling.

Hedge Fund Returns and Total Factor Productivity

This study explores whether hedge funds’ investment behavior can predict variations in productivity levels using a structural vector autoregressive model (SVAR) and a vector error correction model (VECM). As <i>informed</i> traders in the stock market with superior skills, hedge funds may quickly capture news shocks regarding future production growth in advance. With quarterly series of <i>TFP</i> provided by John Fernald (2014) and hedge fund index (<i>HFI</i>) returns obtained from the Credit Suisse/Tremont database, I find a contemporaneous correlation coefficient of 0.9791 between two endogenous variables over the sample period from 1Q:1994 to 2Q:2023, indicating a high degree of similarity in their movements. A Granger Causality Test rejects the hypothesis, “ ΔLn(<i>HFI</i>) does not Granger Cause Δ<i>TFP</i>”, suggesting that the information inferred from the hedge fund index are valuable in predicting future economic productivity. Finally, the forecast error variance decompositions using the VECM model indicate that over 65% of the variation in Δ<i>TFP</i> even after 20 quarters can be attributed to a shock to the Δ<i>Ln</i>(<i>HFI</i>) .

Asymmetric Effects of Oil Price Volatility on Government Expenditure in Oil-Importing African Countries: Evidence from Zambia

This paper examines the impact of disaggregated oil price shocks the government expenditure in oil-importing African countries using the case of Zambia. The analysis utilizes a“Structural Vector Autoregressive (SVAR) Model” to evaluate the short-run effects, while a “Vector Error Correction Model (VECM) is employed to analyze the relationship over the long-run. Results of the short-run analysis reveal a notable positive influence of oil price shocks”on government expenditure that emanates from global aggregate and precautionary demand factors. The aforementioned results are clarified by the effects that increased global aggregate demand has on copper priсes, which in turn influences government revenue derived from taxes. Additionally, the transmission of “financial contagion and volatility spillovers” from the οil to the copper market clarifies the influence of precautionary demand on government expenditure. The findings obtained from the VECM indicate that global aggregate demand has positive effects on public sector spending in the long run. Additionally, the analysis demonstrates that the rate at which public sector spending recovers to its equilibrium level after a temporary deviation is both monotonic and statistically significant. Moreover, among the three categories of οil priсe hikes, Forecast Error Variance Decomposition analysis reveals that shocks related to precautionary demand have the most significant impact, accounting for 11.7% of the observed variation in public sector spending.

Conflict Inflation and the Role of Monetary Policy

ABSTRACT The aim of this study is twofold: on the one hand, to provide evidence of the positive association between the inflation rate and the labor share in the US; on the other, to show that monetary policy slows inflation by depressing the labor share. With the help of Bayesian econometrics, the paper shows evidence of a conflict Phillips curve. Furthermore, a structural vector autoregressive model reveals the detrimental effects of interest rate hikes on the labor share. Together, these results mean the monetary policy of administered interest rates resolves the class conflict in favor of capital to control inflation. Scrutiny of the US monetary policy development and a model of a controlled predator-prey system prepare the way for the empirical sections. Finally, the paper concludes that controlling the distributive conflict matters for impinging on inflation and that the Fed might follow alternative monetary rules to cease being the third party participating in the distributive conflict.

The Impact of Remittance Inflows on Inflation: The Case of Lebanon

Abstract This paper investigates the impact of workers’ remittance inflows on inflation in Lebanon. The data used is monthly data spanning the period from December 2007 to May 2019. I estimate a structural vector autoregressive model using 24 monthly lags and find that a one-time remittance growth shock leads to a statistically significant increase in the inflation rate in the short run in Lebanon. Also, the results show that a remittance growth shock makes a considerable contribution to the forecast error variance for the inflation rate in Lebanon.

Asymmetric effects of oil demand and oil supply shocks on New Zealand's trade

We analyze how different types of oil price shocks affect trade and whether these effects are asymmetric. Our focus is on New Zealand, which has historically been a net oil importer but recently transitioned to an import-only model to meet its needs for finished petroleum products. The analysis proceeds in two stages. First, we identify oil supply shocks, aggregate demand shocks, and oil-specific demand shocks using a recursive structural vector autoregression model. Then, we study whether the effects of these shocks on New Zealand's exports to and imports from its key trading partners—Australia, China, and the United States—are asymmetric using nonlinear autoregressive distributed lag models. The results show differences in the effects of different types of oil price shocks on exports and imports in the short and long run; however, there is limited evidence of asymmetries in these effects, especially in the case of exports. Exports to Australia and China, the most critical markets for New Zealand's exporters, are largely insensitive to oil price shocks.

Dynamic interaction between permanent and temporary employment across manufacturing labor markets in the Mexican states: A structural panel VAR approach

México’s implementation of several labor market reforms aimed to stimulate the flexibilization of labor relationships have brought long-standing concerns among scholars and governmental authorities about a displacement process of permanent by temporary jobs. In this regard, this manuscript aims to respond whether the dynamic interaction between permanent and temporary employment across Mexican states describe a substitution or complementary relationship. By means of implementing a structural panel vector autoregressive model, our estimation results demonstrate that a combination of both types of interaction, substitutive and complementary ones, prevail across manufacturing labor markets at state level. Moreover, a marked heterogeneity among estimated dynamic responses suggests that incentivizing permanent employment would induce stronger substitution effects on its temporary counterpart than, for example, substitution of permanent job positions arising when incentivizing temporary employment.