Structural Vector Autoregressive Model Research Articles

The transmission of monetary policy shocks through the markets for reserves and money

This paper identifies supply and demand curves for bank reserves and a Divisia aggregate of monetary services within a structural vector autoregressive time-series model. Estimated over four sample periods spanning 1967 through 2020, the model illustrates how monetary policy actions can be interpreted with reference to their initial impact on bank reserves and the federal funds rate and their subsequent effects on Divisia money, nominal consumption spending, the aggregate nominal price level, and the unemployment rate. Model estimates attribute strong inflationary effects to monetary policy in the late 1960s and 1970s and also show that changes in the supply of reserves associated with the Fed's large-scale asset purchases since 2008 worked, as intended, to offset deflationary pressures and reduce unemployment. The model describes a much richer monetary policy process than one focused on interest rates alone.

The Bitcoin price and Bitcoin price uncertainty: Evidence of Bitcoin price volatility

AbstractThis study examines the Bitcoin price by taking into account global factors, including the Chicago Board Options Exchange's Market Volatility Index (VIX), the US dollar index, the gold price, the oil price, and Bitcoin price volatility. The analysis is conducted using the structural vector autoregression (SVAR) model. The variance decomposition findings revealed that the influence of the VIX on the Bitcoin price was initially restricted, but progressively intensified over time. Among the indicators, Bitcoin price volatility had the highest explanatory share in both daily and weekly data analysis. The impulse response functions demonstrated a statistically significant inverse relationship between the VIX and the Bitcoin price. Furthermore, the analysis revealed that the Bitcoin price was mostly impacted by its own volatility. This implies that investing in Bitcoin requires a certain level of risk‐taking.

The impact of monetary policy on income inequality: Does inflation targeting matter?

Does inflation targeting affect the nexus between monetary policy and income inequality? We address this question by estimating country-specific structural vector autoregression models across the G7 economies. We find that contractionary monetary policy shocks increase income inequality when using a long period of data from 1974–2019. However, controlling for the adoption of inflation targeting reduces the size of this effect in Japan and the UK, and it disappears altogether in Canada and the US. This suggests that country-specific inflation targeting may mitigate the inequality-inducing effects of unanticipated monetary contractions. In contrast, mixed evidence is found for Eurozone countries: France, Germany, and Italy.

Global risk and the dollar

The dollar is a safe-haven currency and appreciates when global risk goes up. We investigate the dollar’s role for the transmission of global risk to the world economy within a Bayesian proxy structural vector autoregressive model. We identify global risk shocks using high-frequency asset-price surprises around narratively selected events. Global risk shocks appreciate the dollar, induce tighter global financial conditions and a synchronized contraction of world economic activity. We benchmark these effects against counterfactuals in which the dollar does not appreciate. In the absence of dollar appreciation, the contractionary impact of a global risk shock is much weaker, both in the rest of the world and the US. For the rest of the world, contractionary financial channels thus dominate expansionary expenditure switching when global risk rises and the dollar appreciates.

Oil price shocks in real time

Oil prices contain information on global shocks of key relevance for monetary policy decisions. We propose a novel approach to identify these shocks at the daily frequency in a Structural Vector Autoregression (SVAR). Our method is devised to be used in real time to interpret the developments in the oil market and their implications for the macroeconomy, circumventing the problem of publication lags that plagues monthly data used in workhorse SVAR models. It proves particularly valuable for monetary policymakers at times when macroeconomic conditions evolve rapidly, like during the COVID-19 pandemic or the invasion of Ukraine by Russia.

The ability of energy commodities to hedge the dynamic risk of epidemic black swans

The outbreak of COVID-19 increased global financial risks and economic shocks. Although epidemic risk is on the decline, the likeliness of a similar black swan event occurring in the future cannot be ruled out. In this study, we constructed epidemic search indices using the Google Search Volume Index (SVI) to examine whether investor sentiment regarding the COVID-19 pandemic affected stocks, gold, silver, copper, crude oil, and natural gas. The results reveal that, over the entire epidemic period, energy assets (crude oil and natural gas) were the most “resistant” to epidemic panic, making them suitable for hedging the risk associated with epidemic panic risk. We employed a time-varying parameter structural vector autoregression model with stochastic volatility (TVP-SVAR-SV) to analyze the time-varying contagion effects of epidemic search indices on various asset prices at different points in time. The findings indicate that in the early stages of the COVID-19 pandemic when epidemic panic risk was highest, natural gas prices exhibited the most risk-averse characteristics. Only during the middle to later stages of the epidemic did natural gas prices begin to be positively affected by epidemic panic sentiment. However, during this period, the epidemic search indices show a stable declining trend, indicating a reduction in risk. We observe that natural gas prices only reflected panic sentiment during periods of stability. The results of this study suggest that energy commodities, especially natural gas, are suitable for hedging against unforeseen high epidemic risks. When epidemic risk decreases, profits can be realized and reinvested in assets with a higher contagion effect due to epidemic risks, further benefiting from the rebound in the prices of these assets. In addition to explaining why energy commodities are particularly suitable for mitigating epidemic-related black swan events, we provide a detailed investment strategy for hedging future epidemic risk and further elaborate on how investors should dynamically adjust their portfolios according to the severity of the epidemic and whether it is under control.

Pass‐through of shocks into different U.S. prices

AbstractThis article estimates the pass‐through of different shocks into different U.S. prices that are important for policy makers. The investigation is based on a structural vector autoregression model, where quarterly data are used. The empirical results depict oil price pass‐through, exchange rate pass‐through, import‐price pass‐through, and producer price pass‐through into import prices, producer prices, and consumer prices for the U.S. economy. Policy implications suggest that achieving and sustaining consumer price stability highly depend on monitoring the developments in oil prices, followed by import prices and producer prices.

Locally robust inference for non‐Gaussian SVAR models

All parameters in structural vector autoregressive (SVAR) models are locally identified when the structural shocks are independent and follow non‐Gaussian distributions. Unfortunately, standard inference methods that exploit such features of the data for identification fail to yield correct coverage for structural functions of the model parameters when deviations from Gaussianity are small. To this extent, we propose a locally robust semiparametric approach to conduct hypothesis tests and construct confidence sets for structural functions in SVAR models. The methodology fully exploits non‐Gaussianity when it is present, but yields correct size/coverage for local‐to‐Gaussian densities. Empirically, we revisit two macroeconomic SVAR studies where we document mixed results. For the oil price model of Kilian and Murphy (2012), we find that non‐Gaussianity can robustly identify reasonable confidence sets, whereas for the labor supply–demand model of Baumeister and Hamilton (2015) this is not the case. Moreover, these exercises highlight the importance of using weak identification robust methods to assess estimation uncertainty when using non‐Gaussianity for identification.

ANALYZING THE BALANCE SHEET OF THE EURO AREA NCBS AND THE ECB. THE IMPACT OF SECURITIES ON INFLATION AND ECONOMIC ACTIVITY

When assessing the decisions of a central bank, such as the European Central Bank (ECB), the traditional focus has been on its interest rate policy and the resulting implications. However, given the increasing use of unconventional monetary policy instruments in recent years, an examination of the Central Bank's balance sheet and its implications has become imperative. This paper investigates the evolution and impact of the Eurosystem's balance sheet on the Eurozone's economy. The empirical analysis utilizes a Structural Vector Autoregressive (SVAR) model to analyze the responses to exogenous shocks resulting from innovations in securities held for monetary policy purposes within the Eurosystem. Impulse responses indicate that unconventional monetary policy innovations exert positive impacts on prices and output. These results support the efficacy of unconventional monetary policy and advance knowledge of the links between the balance sheet policies of the European Central Bank and the broader Eurozone economic dynamics.

Dynamics between FinTech and financial market: Supply-driven or Demand-guided?

<p>This paper examined the dynamic relationship between FinTech development and financial development using the time-varying parameter structural vector autoregression (TVP-SV-VAR) model to analyze their impulse response relationship. The results showed that the impact of FinTech development on financial development varies across different periods. In China, before the first half of 2021, financial development mainly drove FinTech development through demand. Afterward, FinTech development promoted financial development by providing new technological tools and services. In the United States, FinTech innovation and application mainly influenced financial development through supply-driven mechanisms. After the second half of 2022, as FinTech infrastructure improved, its positive impact on the financial market strengthened. The study also found that the effects of policy changes and market fluctuations on impulse responses at specific time points differed even in countries with different systems. The findings of this paper provide valuable insights for policymakers to address the challenges and opportunities brought on by FinTech.</p>

The effects of the volatilities in global determinants on the Istanbul stock exchange

This study investigates the impact of the VIX, volatility in the oil price, gold price, exchange rate, interest rate, and traded value on volatility in the Istanbul stock exchange. The structural vector autoregression (SVAR) model and pairwise and multivariate Granger causality tests were utilized for weekly data ranging from January 2002 to November 2020. According to causality tests, the volatility in global determinants were the primary factors that determined the volatility in the stock exchange. The variance decomposition analysis indicated that the VIX was the main contributor to stock exchange volatility, and its influence increased in successive time periods. The impact of the volatility in the gold price had the second highest influence, while the influence of the oil price remained more limited. The volatility in the stock exchange was significantly affected by volatility in the exchange rate, but the significance of its movements decreased over time. In the early stages, the gold price had no effect, but then it started to have a significant and continuous positive impact. The volatility in the oil price was initially positive and significant, but after a few weeks, its influence became insignificant. Interest rate volatility was insignificant, but exchange rate volatility remained positive and significant for a few of weeks. The volatility in traded value initially appeared insignificant, but it eventually became significant, had a positive influence, and had a long-lasting effect on stock exchange volatility. Hence, the VIX is the most important global driver of stock exchange volatility in both long and short terms.

Global Demand and Supply Sentiment: Evidence From Earnings Calls*

AbstractThis paper quantifies global demand and supply conditions and compares two major global recessions: the 2009 Great Recession and the COVID‐19 pandemic. First, we compute demand and supply sentiment by applying Natural Language Processing techniques on earnings call transcripts. Second, we corroborate our sentiment measure by identifying demand and supply shocks using a structural Bayesian vector autoregression model. The results highlight sharp contrast in the size of supply and demand conditions over time and across sectors. While the Great Recession was characterized by weak demand, COVID‐19 caused sizable disruptions to both demand and supply, with varying relative importance across major sectors. Furthermore, certain sub‐sectors, such as professional and business services, internet retail, and grocery/department stores, fared better than others during the pandemic.

Growth, cycles, and residential investment

The empirical literature on models of cyclical growth and distribution has not investigated the differential roles of residential vs. nonresidential investment. These flows are similar in that they are largely undertaken by profit-seeking corporations, but differ greatly in that residential (nonresidential) investment is a leading (lagging) indicator of the business cycle. We estimate two Structural Vector Autoregressive (SVAR) models of the cyclical components of US post-WWII time series with restrictions motivated by neo-Goodwinian theory. The 4D model includes utilization rate, accumulation rate, employment rate and labor share. The 5D model disaggregates investment into residential and nonresidential flows. Results (i) support profit-led demand and profit- squeeze distribution, and (ii) confirm that residential investment leads the cycle, whereas nonresidential investment lags it.

Public debt forecasts and machine learning: the Italian case

PurposePublic debt forecasts represent a key policy issue. Many methodologies have been employed to predict debt sustainability, including dynamic stochastic general equilibrium models, the stock flow consistent method, the structural vector autoregressive model and, more recently, the neuro-fuzzy method. Despite their widespread application in the empirical literature, all of these approaches exhibit shortcomings that limit their utility. The present research adopts a different approach to public debt forecasts, that is, the random forest, an ensemble of machine learning.Design/methodology/approachUsing quarterly observations over the period 2000–2021, the present research tests the reliability of the random forest technique for forecasting the Italian public debt.FindingsThe results show the large predictive power of this method to forecast debt-to-GDP fluctuations, with no need to model the underlying structure of the economy.Originality/valueCompared to other methodologies, the random forest method has a predictive capacity that is granted by the algorithm itself. The use of repeated learning, training and validation stages provides well-defined parameters that are not conditional to strong theoretical restrictions This allows to overcome the shortcomings arising from the traditional techniques which are generally adopted in the empirical literature to forecast public debt.

The dominant risks in the interest rate channel: evidence from the urban housing market in Taiwan

ABSTRACT The main purpose of this paper is to investigate the city-level data on mortgage rates of banks which may hide significant information about the monetary transmission mechanism. This study, by regional housing loans and risks data, investigates the behaviour of banks after a monetary policy tightening. By using the structural vector autoregressive model with the counterfactual impulse response function and the spatial regression model, our results show that monetary tightening causes mortgage rates to rise. This response is mainly driven by price and credit risks. Consequently, these housing activities have a significant effect on mortgage rates, by amplifying the shock from the tightening monetary policy. The latter result provides evidence that financial intermediation and regional housing risks play an important role in the interest rate channel.

The macroeconomic effects of exchange rate movements in a commodity-exporting developing economy

This paper empirically examines the macroeconomic effects of exchange rate movements and their transmission mechanisms (trade and financial channels) in Mongolia, a net debtor in foreign currency and a commodity-exporting developing economy, using Structural Bayesian Vector Autoregression (SBVAR) models. Our findings suggest that both the financial and trade channels of exchange rates are operative and have a notable impact at the macroeconomic level. We identify a significant financial channel, causing GDP contractions and affecting investments, particularly in sensitive sectors such as manufacturing and construction in response to DWER depreciation shocks. The traditional trade channel, driven by NEER depreciation, leads to increased net exports with pass-through to CPI. Despite these effects on key macro variables, exchange rate shocks do not substantially destabilize the economy. Foreign shocks, including federal funds rates, China's GDP, and copper prices, have a more pronounced impact. The trade channel plays a crucial role in the transmission of external demand and commodity price shocks, while the financial channel is essential in the transmission of commodity price shocks.

The international impact of a fragile EMU

This paper aims at quantifying the international impact of euro area stress shocks, which arise also from the incomplete nature of the European Economic and Monetary Union (EMU). We first disentangle such shocks from more general global risk aversion shocks by using sign, magnitude and narrative restrictions in a daily Structural Vector Autoregression (SVAR) model with financial variables. We then assess the impact of euro area stress shocks on macroeconomic variables in the euro area and the rest of the world via panel local projections. We find that these shocks have similar effects to supply-side shocks for other advanced economies, and demand-side shocks for emerging economies. They also result in trade diversion across advanced and emerging economies and they are found to be noticeably more heteroscedastic than global shocks. As in previous literature, global risk shocks have an impact that can be interpreted as a demand-side shock, with a significant fall in economic activity and trade after risk-increasing shocks.

Correlations of structural shocks, dynamic responses of output and inflation to commodities price shocks and monetary union in WAMZ

PurposeThis paper examines correlations of the underlying structural shocks and the degree of synchronization in the impulse responses of output, inflation and trade to a one standard deviation shock to non-oil commodities price index and exchange rates within the West African Monetary Zone (WAMZ) countries from 1990q1 to 2020q1.Design/methodology/approachThis paper uses the structural vector autoregressive model to isolate the underlying structural shocks and compares them with the West African Monetary Union (WAEMU) countries.FindingsFindings from the study suggest that correlations of underlying structural shocks are more profound in the WAEMU than in the WAMZ. Impulse responses of output to price and exchange rate shocks are more symmetric in the WAEMU than in the WAMZ. However, impulse responses of inflation to price and exchange rate shocks are symmetric in the WAMZ than in the WAEMU and responses of trade in both sub-groups are not uniform.Practical implicationsThe paper concludes that the WAMZ does not constitute an Optimum Currency Area concerning the correlations of the structural shocks and output. However, it has achieved convergence in inflation and there are adequate adjustment mechanisms to shocks in the WAMZ than in the WAEMU. Therefore, the WAMZ may not suffer from joining the monetary union. Thus, economic Community of West African States may take steps to roll out the monetary union.Originality/valueThe paper examines correlations of the underlying structural shocks, impulse responses of output and inflation to shocks to commodities price and exchange rates in the WAMZ and compares them with the WAEMU.

Study on the Dynamic Relationship Between Individual Mortgage Policy and Housing Price Fluctuation in Zhejiang Province -Empirical Analysis Based on SVAR

The eastern Chinese province of Zhejiang is highly developed and contributes to the real estate business. The paper uses quarterly data from 2011 to 2021 to create a Structural Vector Autoregressive (SVAR) model to explore the empirical dynamics between personal housing credit policies and house price changes in Zhejiang Province, China. Impulse response and variance decomposition research reveal that loan interest rates considerably affect housing values. Interest rates affect property values by 0.85% to 30.19% over time. The initial rise in loan interest rates boosts real estate values, but the successive increase will lower them until stability is reached. Large personal mortgage loans also affect housing prices. Throughout time, the share of factors affecting property prices with respect to mortgage loans ranges from 2.32% to 4.31%. The size of mortgage debt boosts home values. Thus, a corresponding easing of mortgage borrowing ceilings will boost home prices. The causal relationship between GDP and housing price swings is unclear, although its impact is stable with modest variations within a limited range. Zhejiang Province should be cautious when adjusting personal mortgage loans and interest rates while improving regulatory control. These techniques reduce market speculation and negative factors to ensure market stability and longevity.

The interplay between sales and marketing expenditures: an econometric approach in the B2B market

PurposeManagers engage in marketing efforts to boost sales and in setting marketing budgets based on current or historical sales. Past studies have overlooked the reciprocal relationship between marketing spending and sales. This study aims to examine the nature of the relationship between sales and marketing expenses in the B2B market.Design/methodology/approachFive hypotheses on the relationship between sales and marketing expenditures were framed. A total of 30 of India’s dyeing firms provided data on revenues, sales (in units) and marketing expenditures over time. The structural vector auto-regressive model and the vector error correction model were fitted to the data.FindingsThe results show that marketing expenses and sales are related bidirectionally in a sequential way. Furthermore, sales drive the long-term equilibrium relationship to a greater extent than marketing expenditures.Practical implicationsThe findings of this study should assist managers in predicting sales and marketing budgets simultaneously and devising precise marketing strategies and tactics.Originality/valueUsing econometric models in data-driven research is not a frequent practice in marketing. This study adds value to the body of marketing literature by advancing the theory of the relationship between sales and marketing spending using real-world data and econometric models in the B2B sector.