Structural Vector Autoregressive Model Research Articles

Macroeconomic Determinants of Household Indebtedness in Romania: An Econometric Approach

Abstract This paper examines the reaction of the household indebtedness to various shocks in the economy between 2011Q1 and 2021Q4, using a Structural Vector Autoregressive model. The results of the econometric model indicate that average net wage was the driver of the loans granted by credit institutions to individuals’ variation after their own innovations at all time horizons. This result was achieved in the conditions that households’ resilience to shocks has improved significantly in the period 2011-2020 from lei 243 billion to lei 480 billion. The evolution of the economy starting from March 2020 was influenced by the emergence of the COVID-19 pandemic and the imposition of restrictions to prevent the spread of the disease. In this situation, between March 2020 and September 2021, the new standard mortgage loans recorded an average growth rate of 2.2 percent in nominal terms and 1.91 percent in real terms, standing above the pre-pandemic level (1.34 percent, respectively 1.17 percent). The low interest rates and general household income growth (the minimum wage in the economy increased 14 times between 2011 and 2021) were responsible for high household debt. The rise in household indebtedness growths its vulnerability to shocks in the economy, potentially having a negative impact mainly on the creditors’ balance sheet.

What causes house prices to fluctuate? Evidence from South Korea

AbstractIn this study, we build a structural vector autoregressive model of the housing market with supply and demand shocks to determine the main causes of house price movements in South Korea. We include housing permits, basic housing demand, and the growth rates of real housing prices in the model. Changes in house prices are decomposed into three structural components: housing supply shocks, shocks to basic housing demand, and shocks to housing market‐specific demand. We find that the main driver of the movements in house prices is housing market‐specific demand shocks, and these are associated with beliefs regarding future house prices and the real estate market, as measured by the Consumer Sentiment Indices for the housing market, rather than credit conditions or financial variables such as interest rates or stock returns.

Measuring the impacts of monetary policy in Turkey: an extended structural vector autoregressive model with structural breaks

Measuring the impacts of monetary policy in Turkey: an extended structural vector autoregressive model with structural breaks

Impact of imported dairy products on China’s raw milk production

With the continuous opening up of China’s dairy market to foreign countries, dairy products import volume continues to grow rapidly. The structural vector autoregressive model (SVAR) was used in this article to analyze the impact of dairy product imports on China’s raw milk production from 1996 to 2017. It is found that, dairy product import volume has a positive impact on China’s raw milk production, and negative impact on the liquid dairy product; and mainly negative impacts on the cost control variables in the short term. The price of corn has a stronger impact on the raw milk production compared with that of the soybean meal prices and crude oil price; the impact of Domestic raw milk demand on raw milk production fluctuates frequently in the short term, and has a positive impact on the diary export. Based on this, this article believes that adjusting the milk industry policy, optimizing the dairy products import structure and the dairy cows’ source structure, and advocating scientific feeding can effectively alleviate the impact caused by dairy products import.

The impacts of demand and supply shocks in the dry bulk shipping market

The freight rate is a representative variable in the shipping market and is characterized by a cyclical relationship. Even though downturns in the shipping market, such as the shipping industry recession in the 1980s, the global financial crisis in 2008 and COVID-19 crisis in 2020, recur, few studies have analyzed the dynamic relationship between supply and demand in terms of its effect on freight rates. Thus, this study classifies the factors affecting fluctuations in dry cargo freight rates into demand, supply, and freight rate specific demand factors, which play the most important role in managing risk in the shipping market. Based on the recursive structural vector autoregressive (recursive SVAR) model, we analyze the historical contributions of the effects of each factor across different time periods. Two main findings are summarized as follows: first, we identify the dynamic relationship between factors affecting BDI in the shipping market, and reveal that the magnitude and direction of factors are different. Second, we verify that in an extreme situation in which freight rates exceed the normal range, the market is overheated, and freight rates are therefore determined by the freight rate specific demand of market participants rather than by the actual supply and demand.

An Alternative Bootstrap for Proxy Vector Autoregressions

We propose a new bootstrap algorithm for inference for impulse responses in structural vector autoregressive models identified with an external proxy variable. Simulations show that the new bootstrap algorithm provides confidence intervals for impulse responses which often have more precise coverage than and similar length to the competing moving-block bootstrap intervals. An empirical example shows how the new bootstrap algorithm can be applied in the context of identifying monetary policy shocks.

Assessment of interest rate channel Effectiveness in the transmission of Monetary Policy in Nigeria

This study attempted to investigate the effectiveness of the interest rate channel in the transmission of monetary policy by employing a structural vector autoregressive (SVAR) model using sign restriction. It used a set of policy and non-policy macroeconomic variables based on monthly data spanning the period 2007 and 2020. The structural impulse response functions provided evidence to support the use of the MPR as a signaling rate for domestic interest rates but was, however, found to be ineffective in stabilizing prices or increasing output. Furthermore, results from the variance decomposition of the non-policy variables found the effect of exchange rate innovations to be more significant in explaining variations in the price level. The study, therefore, concludes that the effectiveness of the policy rate in stabilizing prices is dampened by shocks prevalent from the external sector. Given the importance of international trade, the study recommends aggressive exchange rate management including policies that encourage import-substitution to build reserves and strengthen the value of the domestic currency.

The Origins and Dynamics of Inflation in Turkey: An SVAR Approach

This study aims to present empirical evidence on the relative importance of supply and demand-side factors in determining the fluctuations in the general level of prices in the Turkish economy. The employed strategy uses the view that supply and demand pressures can be distinguished from each other depending on the direction of their effects on price and quantity. After classifying the related economic variables as supply, demand, and common factors, the main determinants of domestic supply and demand were estimated econometrically using sample data from 2003:1–2021:4, and their relative contribution to inflation was calculated. By using these basic determinants, the estimated structural vector autoregressive (SVAR) model shows that the pressures arising from the supply side are more dominant than the pressures of the demand side on the inflationary process in Turkey. The results indicate that the methodology suggested in this study will be useful in separating the factors that contribute to inflation, which has recently gotten out of control in Turkey and is gradually moving away from the targeted inflation. Policymakers considering these findings can reach optimal decisions in conducting the monetary policy toward the targeted level of inflation.

The influence from a demand perspective with real economic activity: China versus the United States in world oil markets

AbstractIn this paper, we provide direct evidence for an increasingly critical role of China in the world oil markets. Specifically, our empirical results confirm that the influence of China's oil demand on the oil price of the world has increased over time and surpassed that of the United States. The contribution of demand perspective from China are formally quantified by three key variables of world oil market, which are world oil production, real economic activity and the real price of crude oil. The impact of oil demand from China is used to compared with those from other oil markets, including shocks from the United States. We also construct suitable proxies that reflect real economic activity from these economies, and identify them via a structural vector autoregressive model to disentangle the influence of oil demand from China, the United States and the rest of the world. Our results confirm the finding of recent research, which is the increasing demand for oil from developing countries has become a key driver of the price of oil.

Assessing economic growth-energy consumption-CO2 nexus by climate zone: international evidence.

The energy economics literature lacks a consensus on the short- and long-term linkages along with the Granger causality direction between economic growth and energy consumption. This paper examines the relationships between economic growth, fossil fuel and renewable energy consumption, CO2 emissions, temperature, and population in 56 countries from 1990 to 2019. We contribute to the literature by uniquely grouping countries by climate zone (i.e., tropical, arid, temperate, and continental) following the Köppen-Geiger climate classification approach and employing a panel structural vector autoregressive model (P-SVAR) to study these relationships. In addition, rather than mistakenly using a single energy consumption variable, we divide it into two categories: fossil fuel and renewable energy consumption. Our findings indicate that temperature change has the most negligible impact on economic growth, while CO2 and renewable energy have the two most significant effects. Our Granger causality tests support all four hypotheses (growth, feedback, conservative, and neutral) that describe the relationship between economic performance and energy consumption, indicating the level of difficulty in recommending an overarching energy policy worldwide. We find that conservative energy consumption and clean energy policies that avoid CO2 emissions may benefit the selected countries' economic prosperities.

Identifying Structural Vector Autoregression via Leptokurtic Economic Shocks

We revisit the generalized method of moments (GMM) estimation of the non-Gaussian structural vector autoregressive (SVAR) model. It is shown that in the n-dimensional SVAR model, global and local identification of the contemporaneous impact matrix is achieved with as few as suitably selected moment conditions, when at least n – 1 of the structural errors are all leptokurtic (or platykurtic). We also relax the potentially problematic assumption of mutually independent structural errors in part of the previous literature to the requirement that the errors be mutually uncorrelated. Moreover, we assume the error term to be only serially uncorrelated, not independent in time, which allows for univariate conditional heteroscedasticity in its components. A small simulation experiment highlights the good properties of the estimator and the proposed moment selection procedure. The use of the methods is illustrated by means of an empirical application to the effect of a tax increase on U.S. gasoline consumption and carbon dioxide emissions.

Structural VAR and financial networks: A minimum distance approach to spatial modeling

SummaryIn this paper, I interpret a time series spatial model (T‐SAR) as a constrained structural vector autoregressive (SVAR) model. Based on these restrictions, I propose a minimum distance approach to estimate the (row‐standardized) network matrix and the overall network influence parameter of the T‐SAR from the SVAR estimates. I also develop a Wald‐type test to assess the distance between these two models. To implement the methodology, I discuss machine learning methods as one possible identification strategy of SVAR models. Finally, I illustrate the methodology through an application to volatility spillovers across major stock markets using daily realized volatility data for 2004–2018.

CCTV News’ Asymmetric Impact on the Chinese Stock Market during COVID-19: A Combination Analysis Based on the SVAR and NARDL Models

ABSTRACT This study uses the structural vector autoregression (SVAR) and nonlinear autoregressive distributed lag (NARDL) models to examine the long- and short-term asymmetric effects of structural state media shocks on the Chinese stock market. The findings, obtained using Xinwen Lianbo as a stand-in for state media, indicate that attention shocks on Xinwen Lianbo have an asymmetrical impact on the aggregate stock market returns in both the short and long run. The sectoral and overall stock market results are similar, with CCTV having a stronger impact in the first half of the pandemic. Employing other COVID-19 news measurements, we validated our primary findings and discovered that the price function differs among various state media’s attention to COVID-19.

Change point test for structural vector autoregressive model via independent component analysis

This paper considers the problem of testing for parameter changes in structural vector autoregressive (SVAR) models via using the independent component analysis (ICA). For this task, we first transform the SVAR model into a vector autoregressive (VAR) model and estimate the VAR estimates. Then, we transform the VAR model again to an SVAR model with independent errors using the ICA method. This ICA method is hybridized with the cumulative sum (CUSUM) test developed for location-scale time series models. In this paper, we also consider the maximum likelihood estimator (MLE)-based CUSUM test and derive its limiting null distribution for providing a theoretical background on the CUSUM test. Our empirical studies affirm the validity of the CUSUM test based on both methods in various situations and particularly exhibit the excellency of the ICA method in terms of computing speed.

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.

Does Foreign Direct Investment Affect Macroeconomic Dynamics? An S-VAR Approach for Turkey

This study analyzes the effects of foreign direct investments (FDI) on the macroeconomic dynamics of the Turkish economy through the Structural Vector Autoregressive (SVAR) model. The results obtained, by the economic theory, reveal the positive effects of FDI on economic growth and domestic investment volume. The results also confirm the assumption of economic theory that domestic and foreign investments are complementary. It is understood that the FDI put some pressure on prices to increase, but it is balanced by the decisions of the monetary authority. While FDI does not play a critical role in reducing unemployment, it significantly contributes to the increase in imports, especially in capital goods.

Do house prices play a role in unconventional monetary policy transmission in Japan?

This study comprehensively analyzes the role of house prices in the transmission mechanism of unconventional monetary policy (UMP) in Japan using structural vector autoregression models. The empirical results show that house prices are significantly affected by UMP shocks, and fluctuations in house prices considerably affect macroeconomic variables. With a set of robustness checks and extensions, the findings indicate that house prices provide important channels for UMP transmission.

DO OIL PRICE SHOCKS MATTER TO CONSUMERS IN ZAMBIA? AN SVAR APPROACH

This study investigated the impact of decomposed oil price shocks on household consumption in Zambia from 1985-2019. A structural Vector Autoregressive Model (SVAR) was used to measure the contemporaneous impact of oil price shocks on household consumption, and was complemented by Impulse Response Functions (IRFs), Granger Causality Tests and Forecast Error Variance Decompositions (FEVD). The existence of long-run relationships was determined by cointegration tests. The findings revealed that oil price shocks neither had short-run nor long-run impacts on consumption at the 5% level. Notwithstanding, it was found that oil-specific demand granger-causes consumption and that oil-specific demand shocks were attributed for the largest variation in consumption i.e. 6.5%. The findings implied that historic fuel subsidies insulated consumers from the adverse effects of oil price shocks. Therefore, the Zambian Government should introduce smart, optimal energy subsidies which have a less distortionary effect on its fiscal position. KEYWORDS - Oil Price Shocks, Structural Vector Autogressive Model, Forecast Error Variance Decomposition, Consumption, Impulse Response Functions, Granger Causality, Zambia

The Influence of Exchange Rate and Foreign Capital on the Performance of Inflation Targeting Framework in Indonesia

This study provides empirical evidence on the problem of the trilemma of monetary policy in an open economy, in the context of the effect of exchange rates and foreign capital flows on the performance of the ITF in Indonesia. The method used as an empirical estimate is the Structural Vector Autoregressive (SVAR) model. This model allows to include restrictions in the empirical estimation of parameters that measure the contemporaneous effect of one variable on another variable according to the structure of the macroeconomic model. Meanwhile, the lagged effects are estimated according to the VAR model. Therefore, the SVAR model is considered more appropriate than the ordinary VAR model because it can measure both the instantaneous effect and the intertemporal effect of the problem under study. The SVAR model uses restrictions that are consistent with the theoretical model in its estimation, regardless of the time-to-time effect of one variable on another. There are 9 variables in the SVAR model, namely: global risk, oil prices, federal funds rate, economic growth, inflation, interest rates, monetary policy, credit interest rates, foreign portfolio investment flows, and the rupiah exchange rate. All data used were obtained from several sources, including: Bank Indonesia, Central Statistics Agency, and IMF. Based on the estimation results, the exchange rate and foreign capital flows have a significant effect on inflation and economic growth, thus affecting the performance of the ITF in Indonesia. In particular, there is a relative influence between external factors, particularly global commodity prices, US monetary policy interest rates, and global risks, and domestic factors, particularly economic growth, monetary policy interest rates, and bank interest or credit rates. This study also concludes that in addition to inflation and economic growth considerations, Bank Indonesia also considers exchange rate movements in determining its interest rate policy response.

The Effects of Credit Supply Shocks on Malaysia's Economy

This study has examined the impacts of credit supply shocks and other common economicshocks (aggregate demand & supply and monetary shocks) on Malaysia's macroeconomicvariables, using the Bayesian structural vector autoregressive (SVAR) model andemploying sign restrictions. The results showed that an expansionary credit supply shockpositively affected the Malaysian economy, consistent with the existing literature. Basedon the variance decomposition finding, credit supply shocks explained a significantportion of the anticipated variation in the GDP growth, inflation, and, most importantly,credit growth in Malaysia. This study further decomposed total private non-financialcorporate loans into two components: households and non-financial firms. Unlike othereconomies that have extensively researched this subject matter (US, UK, Euro Area), thegrowth rate of households and non-financial firms differed greatly in Malaysia. The empirical findings revealed considerable distinctions between these two components, indicating that different treatments or policy formulations are required rather than employing the same policy to boost or govern Malaysia's credit market.