Generalized Impulse Response Functions Research Articles

Leadership shift in the global soybean market: Dynamic connectedness approach (TVP-VAR)

The price transmission in international soybean market has been extensively examined. However, recent econometric advancements have enabled the application of dynamic connectedness methodology as outlined by Antonakakis and Gabauer (2017) [1], which is based on a Time-varying Parameter Vector Autoregressive (TVP-VAR) model. This approach captures the time-varying connectedness of time series, considering potential risk shock emitters and receivers. The connectedness index (Diebold and Yilmaz, Jan. 2012) [2] was developed using Generalized Forecast Error Variance Decomposition (GFEVD) and the Generalized Impulse Response Function (GIRF) (Koop et al., Sep. 1996; Pesaran and Shin, Jan. 1998) [3,4]. This study aims to understand the dynamic connectedness and price leadership. The research examined markets including the US Soybean futures market (Chicago Board of Trade), Rotterdam Port spot market representing the European soybean market, Paranaguá port representing Brazil, Argentina represented by Rosario Futures and Spot, and the Chinese domestic spot market and the Dalian futures on behalf of China. The research spanned approximately ten years, from September 2009 to May 2019. The findings suggest that the soybean market has reached a high level of maturity, able to withstand exogenous shocks for at least the past seven years. The net pairwise directional connectedness revealed dynamic and bidirectional causality. The dynamic connectedness index showed a highly connected and developed market in the West (Chicago Futures, Rotterdam, Paranaguá, and Rosario Futures and Spot). However, the connectedness between Western and Eastern markets was relatively low, indicating some level of market isolation. Furthermore, the pairwise connectedness index between Eastern market (China spot and Dalian Futures) was also considerable low. Lastly, Paranaguá and Rosario overtook Rotterdam as price-leading markets and were identified as the primary net transmitters of shocks, indirectly implying causality. Chicago, Rosario, and Paranaguá formed a triumvirate leading international prices, while Rotterdam adopted a secondary leading position, deviating from its historical role as the price leader.

Alternative monetary policies and renewable energy stock returns

The aim of this study is to determine how monetary policy interacts with the financial sector specialising in renewable energy, especially since the implementation of Quantitative Easing (QE). Using EU data and the VAR approach incorporating the interest rate, representing monetary policy, an index of renewable energy stock prices, oil prices, technology and the VIX, this paper applies Granger causality, generalised impulse response functions and historical variance decompositions to explain this interaction. To account for the changes in monetary policy such as QE, structural break tests have been used to determine their effects on the variables, as the breaks correspond to the main QE events, the data has been divided into subsamples to reflect the possible differing effects of QE. The results suggest that monetary policy has only a limited effect overall on renewable energy stocks, as the long recent period of alternative monetary policies has been found not to influence renewable energy stock prices. More time-disaggregated analysis undertaken by incorporating structural breaks identifies a significant influence during some time periods depending on the type of monetary policy being conducted.

Does household debt affect the size of the fiscal multiplier?

Abstract Does household debt affect the size of the fiscal multiplier? We investigate the effects of household debt on government spending multipliers using a smooth transition vector autoregression model. Through generalized impulse response functions, we measure whether the effect of government spending on GDP is conditioned by different levels of household debt in Australia, Sweden, and Norway, three countries with high levels of household indebtedness, and in the world’s seven largest economies. Our results indicate that the short-term effects of government spending tend to be higher if fiscal expansion takes place during periods of low household debt. On average, the fiscal multiplier (on impact) is 0.70, 0.61, and 0.79 (percent of GDP) larger when the increase in government spending takes place during periods of low household debt for Australia, Norway, and the United States.

Housing market, oil prices, and macroeconomic volatility in the G7

AbstractIn this paper, we investigate house price shocks on the macroeconomic variables (financial market, inflation, and real sector) of the G7 economies. We use the GVAR to capture the spillover effects from the U.S. housing market and oil prices on these economies from 1991M3–2022M10. We identify the U.S. house price shock using the Structural Generalized Impulse Response Function, house supply and demand variables, and regional divergence. We find that the domestic stock markets and industrial production are the most sensitive to house price shocks. We further compare the importance of house and oil prices on domestic fluctuations. The estimates reinforce the previous findings: U.S. house prices are responsible for a quarter of the domestic volatility of the stock markets and industrial production. In the other macroeconomic segments, the effects of house prices are present, but in lower values. Our results show that house prices provoke more domestic fluctuations than oil prices. Finally, we also found that short and long‐term credit markets, as well as stock markets, transmit the house price shock to industrial production. Consequently, we provide potential channels to comprehend the spillover effect of U.S. house prices on international markets.

Macroeconomic Impacts of Global Oil and Commodity Price Shocks on the Economy of Pakistan

The effects of oil and commodity price shocks on Pakistan's real exchange rate, inflation rate, government spending, money market rate, and industrial output are examined using monthly data from 2000-2017. Using the vector autoregressive (VAR) paradigm, we do an empirical investigation. Impulse response functions and generalized prediction variance decompositions are used to analyze the effect that fluctuations in oil and commodities prices have on Pakistan's economy. When the world oil price (LWOP) causes shock in the global oil market, the real exchange rate (LRER) shows a negative impulse response function. As a result of the rising cost of crude oil throughout the globe, inflation has been on the rise. Industrial output falls, the real exchange rate rises, and interest rates and inflation go up as a result of shocks. Despite increased industrial production, commodities prices have seen repercussions. Pakistan's interest rate follows shocks by rising for a prolonged period, much as the money market rate (MMR)or interest rate. After oil and commodity price shocks, governments tend to increase their spending. The real effective exchange rate is the primary source of economic volatility, as shown by generalized impulse response functions. Pakistan's economy has been hit hard by the shocks, especially the aftershocks in the currency rate. A depreciating currency rate is being seen in Pakistan. The real effective exchange rate is the main cause of fluctuations in the economy, as demonstrated by tools called generalized impulse response functions.

The influence of the Ukraine-Russia conflict on renewable and fossil energy price cycles

This study investigates the impact of the Ukraine-Russia conflict on energy cycles by extending a business cycle-based approach in the energy sector. We employ the Markov Switching Bayesian Vector Autoregressive (MS-BVAR) model to derive filtered probabilities that represent energy cycles. By utilizing the Russia Economic Policy Uncertainty Index and dummy variables derived from Google search data as conflict indicators, we conduct regression analysis and examine the generalized impulse response function to assess the effects of the conflict on energy cycles. Our research reveals notable differences in renewable and fossil energy price cycles. The renewable price cycle exhibits greater persistence, indicating a limited response to the conflict shock. In contrast, the fossil energy price cycle exhibits more significant and enduring adjustments in high- and low-volatility regimes. These findings highlight the heterogeneous impacts of the Ukraine-Russia conflict on the fossil energy market, indicating potential disruptions in supply chains and market sensitivities. Notably, fossil energy prices demonstrate higher responsiveness to the Ukraine-Russia conflict across market conditions.

Oil price uncertainty and unemployment dynamics: Nonlinearities matter

Using a logistic smooth transition autoregressive process for the monthly U.S. unemployment rate with oil price uncertainty as a transition variable, we investigate the impact of high and low oil price uncertainty states on the unemployment rate. We find that the unemployment rate rises and is more persistent in a high oil price uncertainty state than in a low oil price uncertainty state. Additionally, our analysis of generalized impulse response functions shows that an oil price uncertainty shock increases the unemployment rate in the high oil price uncertainty state but has no significant effect in the low oil price uncertainty state. The asymmetric response of the unemployment rate varies depending on the sign and size of the shocks and is contingent upon the initial history of the economy. Our findings are robust to different specifications and suggest that policymakers should consider these relationships and take a more nuanced approach to policies designed to mitigate the adverse impact of oil price uncertainties on the labor market.

New evidence on sources of macroeconomic fluctuations in sub‐Saharan African countries

AbstractFollowing a global vector autoregressive (GVAR) approach, this paper presents new evidence on the validity of international transmission of economic shocks from key trading partners as sources of macroeconomic fluctuations in sub‐Saharan African (SSA) countries. The GVAR model was estimated for 21 SSA countries grouped into three country classes—oil‐rich, other‐resources‐rich and non‐resource‐based economies, to account for output shocks from crucial trading partner countries—United States, United Kingdom, China and Europe. Furthermore, the generalized forecast error variance decompositions results reveal that output shocks from key trading partners constitute significant contributors to changes in key macroeconomic indicators—real gross domestic product, inflation, exchange rate and short‐term interest rate, in the SSA region. The generalized impulse response functions indicate that these economic shocks have more significant impacts on oil‐rich countries than on other country groups. A key recommendation from this study is that SSA countries, especially the resource‐rich economies, need to strengthen and diversify their economic structure, including the trade basket.

Why do households repay their debt in UK during the COVID-19 crisis?

PurposeIn this paper, the authors investigate whether coronavirus disease 2019 (COVID-19) impacts household finances, like household debt repayments in the UK.Design/methodology/approachThis paper employs a vector autoregressive (VAR) model that nests neural networks and uses Mixed Data Sampling (MIDAS) techniques. The authors use data information related to COVID-19, financial markets and household finances.FindingsThe authors' results show that household debt repayments' response to the first principal component of COVID-19 shocks is negative, albeit of low magnitude. However, when the authors employ specific COVID-19-related data like vaccines and tests the responses are positive, insinuating the underlying dynamic complexities. Overall, confirmed deaths and hospitalisations negatively affect household debt repayments. The authors also report low persistence in household debt repayments. Generalised impulse response functions (IRFs) confirm the main results. As draconian measures, the lockdowns are eased and the COVID-19 shocks are diminishing, and household financial data converge to the levels prior to the pandemic albeit with some lags.Originality/valueTo the best of the authors' knowledge, this is the first study that examines the impact of the pandemic on household debt repayments. The authors' findings show that policy response in the future should prioritise innovation of new vaccines and testing.

A semi-parametric study on dynamic linkages among international real interest rates

Linkages between real interest rates among countries have been extensively investigated in the literature as it has important policy implications for the monetary authorities in the policy making process. Even in a perfectly efficient international interest rate market, equal real interest rates are not expected because of the existence of transaction costs and hence nonlinear approaches that take transactions costs into account are necessary to evaluate the relationship between real interest rates. A semi-parametric, generalized additive vector autoregressive models were applied to a consideration of real interest rate linkages among the US, the UK, Japan, Switzerland and the Eurozone. Through nonlinearity tests, we find strong evidence that nonlinearities substantially characterize real interest rate linkages. Generalized impulse response functions are utilized to investigate the dynamics of real interest rates after shocks of different sizes, directions, and histories. All marginal effects from four models backed by different economic theories and generalized impulse responses from vector error correction models support the real interest rate parity hypothesis which indicates a high degree of integration in capital markets. The results provide evidence that the conventional tests which rely on linearity assumptions may lead to misleading inferences and local monetary policy makers can only affect the real interest rates when the international financial markets are close to the equilibrium; the efforts of the policy makers may be offset by the arbitrage behavior when this condition is not satisfied.

Doing more with less: How to design a good subgroup governance model for the air pollution transport network in “2+26” cities of China?

Air pollution shares the attributes of significant spatial spillover effects and environmental public goods, leading to the territorial governance model that easily falls into a state of failure. Despite a growing number of studies on the local spatial spillover effect of air pollution, scant evidence currently exists on its global spatial association effect and a good subgroup governance model. Based on a panel data set of the daily prefecture-level city data on air quality measured by the air quality index (AQI) in “2 + 26” cities of China in 2015 and 2018, this study first builds an air pollution transport network (APTN), i.e., the cities as the nodes and the association relationships between the nodes as the edges. Furthermore, this paper reveals the spatial association effect and the temporal lagged attribute of the APTN using the Social network analysis (SNA) and the Generalized impulse response function (GIRF). The results are summarized as follows. (1) Every city has significant spatial association effects of air pollution with at least another city in the APTN, and northern APTN affects most to the air pollution of other cities, while southern APTN is obviously always affected by air pollution in other cities. (2) Transport strength peaks on the second day of an air pollution transport process, and the transport process lasts for 7–12 days. (3) The APTN is divided into four subgroups: Sycophants, Primary, Bidirectional, and Brokers, with Baoding, Zhengzhou, Heze, and Hengshui as the central cities of each group, respectively. Overall, our study provides a networked, modular, and early-warning governance model for policymakers.

TRADE OPENNESS, INVESTMENT FREEDOM- SELECTED COUNTRY RISK INDICATORS, IMPACT ON FOREIGN DIRECT INVESTMENTS, A PANEL VECTOR AUTOREGRESSION MODEL APPROACH

We employ a panel vector autoregression model (PVAR) selecting as endogenous variables Foreign Direct Investments percent of GDP, Trade Openness and Investment Freedom for a database comprised of European Countries. The data are collected from the Global Economy database. We compare different PVAR models changing the input of desired lags. We test the Hansen test for over-identifying restrictions and we generate the Generalised impulse response functions. The article uses a Hahn Kuehrsteiner Panel Var estimation estimating a stationarity PVAR with fixed effects. Econometric analysis shows a significant impact of trade openness and investment freedom indicators on foreign direct investment in Europe.

Auxiliary harmonic excitation generalized method for higher-order analysis of linear structure under multiple non-stationary non-Gaussian excitations

In the classical random vibration analysis, the frequency-domain analysis method, especially the time-frequency domain analysis method based on the frequency domain analysis method, is the most commonly analysis method. However, the time-frequency domain analysis method is challenging when dealing with multiple non-stationary non-Gaussian excitations. In this paper, an accurate and efficient auxiliary harmonic excitation generalized method for higher-order analysis of linear structures under multiple non-stationary non-Gaussian excitations is proposed. Firstly, the expression for the higher-order moment spectrum of response is theoretically deduced by the mode superposition method, in which the time-varying generalized impulse response function and the time-varying generalized frequency response function are introduced. Secondly, according to the relationship between the response of harmonic excitation and the time-varying generalized frequency response function, the expression for the high-order moment spectrum of response is reconstructed in terms of the response of harmonic excitation and the higher-order moment spectrum matrix of the excitations, in which the response of the time-varying auxiliary harmonic excitation can be evaluated efficiently based on the time-domain explicit formulation method. Finally, two examples are investigated to verify the accuracy and efficiency of the proposed method.

Systemwide directional connectedness from Crude Oil to sovereign credit risk

This study examines the spillovers from Crude Oil price fluctuations to sovereign credit risk, proxied by CDS spreads for 16 oil exporters and importers belonging to G20. Besides measuring shocks from Crude Oil to sovereign risk, it also examined the system-wide impacts of CDS shocks to understand their magnified impacts within a system. Furthermore, the study finds the channels that have the potential to act as a carrier of shocks from Crude Oil to sovereign risk considering four country-specific and two global factors. Our study deployed Generalized Impulse Response Functions and Generalized Forecast Error Variance Decomposition for being independent of ordering. Additionally, DCC-GARCH has been applied to test the robustness of the results. Our results highlight higher spillovers to oil-exporting countries from Crude Oil when compared to oil importers, irrespective of their development stage. Interestingly, developed countries are severely impacted by net system-wide shocks from developing and oil-exporting countries. Moreover, Global factors play a dominant role in carrying the shocks from Crude Oil to sovereign risk of countries. Stock market indices are important among important domestic factors that act as carriers of shocks, and VIX is robust amongst global variables. Our results are valuable to Regulators, policymakers, portfolio managers, banks, and financial institutions for proactively planning their respective policies.

Environmental risk of Covid-19 recovery

During Covid-19 pandemic world economy experienced negative growth rate, therefore energy consumption and consequently emission pollution decreased. According to Environmental Kuznets Curve, it is expected that energy consumption and emission pollution increase in response to Covid-19 economic recovery, even higher than its pre-pandemic level. The goal of this paper is to study the environmental risk of Covid-19 economic recovery. We use an Environmentally-Augmented Global Vector Autoregressive Model (E-GVAR) to trace dynamic effects of Covid-19 economic recovery on pollution emission. Using generalized impulse response functions (GIRFs), we investigated the effect of positive economic shocks in real per capita income in China and USA economies on total equivalent emission pollution. The results show that positive economic recovery affects emission pollution significantly. China and emerging economies may experience high risk while Europe region is moderately affected by this positive shock. A positive Economic Shock in China decrease pollution emission in USA over time. It can be attributed to substitution effect of Chinese product in global market. Generally, our results demonstrate spillover effect of transition shocks from large economies to the rest of world and highlights the importance of linkages in the world economy.

The role of oil price shocks on exchange rates for the selected Asian countries: Asymmetric evidence from nonlinear ARDL and generalized IRFs approaches

The primary contribution of the current paper is to explicitly take into consideration the role of asymmetries in oil prices when studying the nexus between oil price shocks and the real exchange rates across the selected countries in three regions of Asia. To achieve this contribution, the nonlinear autoregressive distributed lag (NARDL) and generalized impulse response functions (GIRFs) approaches are used. Our NARDL approach discovers that oil prices asymmetrically influence the currencies in such selected Asian economies as Bangladesh, China, India, Korea, the Philippines, and Thailand in both the short and long run. Our GIRFs reveal that a positive shock is more important than a negative shock in determining the currencies of the selected Asian countries. We believe that these outcomes have profound implications for our empirical modeling and policy analysis.

Role of Remittances in Economic Growth: Evidence from Nepal

This paper analyses the dynamic roles of remittance on the process of economic growth of Nepal using time-series annual data for 37 years from 1981-2017. The basic tools of data analysis are vector error correction model, long-run structural modeling, Granger causality test, generalized impulse response functions, persistence profile, and variance decomposition analysis. The paper shows that the remittance inflow and economic growth have bidirectional relationship in the long run, but there is no relationship in the short run. Remittance affects the dynamics of other variables like investment, financial development, and investment on human capital which indirectly affect the performance of the economy through these variables. Findings indicate that remittance could promote financial development in the short run. It also shows the possibility of negative shocks in remittance flow could have a permanent negative effect on educational attainment. It concludes that an environment for investment should be created for enhancing the role of remittance on economic growth. Policies promoting flow of remittance through formal channels and financial literacy should be effective tools for channelizing remittance for economic growth. The government should prioritize the educational sector to prevent dropout of the students from schools when the households are hit negatively due to remittance shocks.

The role of logistics infrastructure in development of sustainable road transport in Poland

Despite road transport is recognized as a threat to natural environment, it is a predominant mode of transport in the European countries, what is primarily determined by the availability of road transport infrastructure and the relatively low costs. Therefore, this study examines the relationship between road transport infrastructure development, petroleum consumption, economic growth and air pollutants emissions from road vehicles in Poland using the Granger causality test and generalized impulse response function. Vector autoregression (VAR) model is estimated on the basis of annual data from the period 1994–2017, within the five systems describing the dynamic interdependencies between general expenditure on the modernization and expansion of road network infrastructure, length of roads, number of motor vehicles and air pollutants emissions in Poland. Conducted analysis reveals the existence of a common causality pattern, namely changes in expenditures on development of road transport infrastructure indirectly influences on the changes in emissions of carbon monoxide (CO), nitrogen oxides (NOx), non-methane volatile organic compounds (NMVOC), suspended dust emissions (PM10 and PM2.5) from road transport, thanks to the changes in consumption of fuels. Based on the obtained results, it can be confirmed, that the development of logistics infrastructure in Poland leads to a limited negative impact of road transport on the natural environment.

Islamic Market Index Behavior and Performance: Empirical Evidence from Dow Jones Market Indexes

The main objective of the study is to empirically investigate the impact of the Conventional stock market index on the Islamic stock market index and the comparative performance of the two stock market indexes. For the purpose of the study, daily observations of Dow Jones Islamic Market US Titans 50 (DJUS50) and Dow Jones Composite Index (DJA) spanning a period from January 2015 until December 2021 are obtained from the Investing.com database. Risk-adjusted performance, VAR model, granger-causality test, generalized impulse response functions, and Johansen cointegration tests are used to investigate the behavior and performance of the Islamic market index empirically. Results based on risk-adjusted performance indicate that the Islamic market index performs better than the Conventional market index. Furthermore, the results suggest no long-run association between the indexes, while there is short-run bidirectional causality. This study will contribute both to the literature and practice. It will contribute to the already existing literature through the usage of the newest data, while the practical implication will help investors to better understand the behavior of the Islamic stock market index.

The impacts of global liquidity on international housing prices

PurposeThe purpose of this paper is to examine how global liquidity affects international housing prices. The data sample covers 35 economies from 2000Q1 to 2017Q4.Design/methodology/approachThe existing papers seldom investigated whether the impacts of global liquidity on housing prices display differences between advanced and developing economies. Cesa-Bianchi et al. (2015) is an exceptional study in that they focused on the impulse response of house price volatility to global liquidity shocks but did not examine the long-run equilibrium relationship. To fill the gap in the existing research, this paper used panel cointegration of Pedroni (2000, 2004) to estimate the long-run linkage between global liquidity and housing prices in both advanced and developing economies, and generalized impulse response function (GIRF) and generalized variance decomposition (GVDC) were also applied to capture the relative strengths and contribution of global liquidity shock on house price volatility.FindingsFirst, the global liquidity elasticity of housing prices is 0.0679 in developing economies, and 0.0454 in advanced economies, implying that the positive effect of global liquidity on housing prices is higher in developing economies. Next, the results of generalized impulse response indicate that the innovation of global liquidity can significantly and positively impact housing prices only in developing economies and the duration is two quarters. Third, in light of the long-run portions of the global liquidity shock on house price volatility in individual economies, the two highest portions are 28.51% in the USA and 20.04% in the UK, while there are low portions, less than 10%, in most of the European economies. Moreover, comparing the long-run contributions of global liquidity and other variables shock on house price volatility, the contribution of the global liquidity shock ranks the highest or second highest in 21 out of 35 economies, confirming that it played a more important role than other economic variables in explaining house price volatility for most economies.Originality/valueCompared with the related literature, the contributions of this paper are as follows. First, except for Cesa-Bianchi et al. (2015), the existing papers seldom investigated whether the impacts of global liquidity on housing prices display differences between advanced and developing economies. Hence, the study adopted a wider data sample, including 7 developing economies and 28 advanced economies, to examine the differences in the impact of global liquidity on housing prices between advanced and developing economies. Second, most of the relative literature calculated global liquidity by applying a monetary aggregate, such as M2 or M3, while Cesa-Bianchi et al. (2015) argued that global liquidity being measured by the international supply of credit is intuitively connected to housing prices. This paper follows the argument of Cesa-Bianchi et al. (2015) to use the international supply of credit as the measure of global liquidity, and both the long-run effects and the short-run relative strengths of global liquidity on housing prices are analyzed. Hence, this paper uses not only the GIRF to discuss the short-run relative strengths, as with Cesa-Bianchi et al. (2015) but also the panel cointegration of Pedroni (2000, 2004) to identify the long-run linkage between global liquidity and housing prices. Moreover, GVDC was used to estimate the contribution of a global liquidity shock on house price volatility in individual economies, which can confirm that global liquidity innovations are a very important factor in explaining house price volatility in most countries.