Standard Vector Autoregression Research Articles

Forecasting and stress testing with quantile vector autoregression

SummaryA quantile vector autoregressive (VAR) model, unlike standard VAR, traces the interaction among the endogenous random variables at any quantile. Quantile forecasts are obtained by factorizing the joint distribution in a recursive structure but cannot be obtained from reduced form estimation. Identification strategies and structural quantile impulse response functions are derived as generalization of the VAR model. The model is estimated using real and financial variables for the euro area. The dynamic properties of the system change across quantiles. This is relevant for stress testing exercises, whose goal is to forecast the tail behavior of the economy when hit by large financial and real shocks.

Impulse response function analysis for Markov switching var models

We exactly derive the regime-dependent impulse response functions for a Markov switching vector autoregression (VAR) model in terms of neat matrix expressions in closed form. The key is to recognize that the latent first-order Markov switching process in the model has a VAR(1) representation, and that the model can be cast into a state-space form. Using such a representation, the regime-dependent impulse response function analysis can be processed with respect to either an asymmetric discrete shock or to a symmetric continuous shock. Our results extend and correct those obtained by Ehrmann et al. (2003) and coincide with those by Hamilton (1994) for the case of standard VAR models.

Estimating both directed and undirected contemporaneous relations in time series data using hybrid-group iterative multiple model estimation.

Researchers across varied fields increasingly are collecting and analyzing intensive longitudinal data (ILD) to examine processes across time at the individual level. Two types of relations are typically examined: lagged and contemporaneous. Lagged relations capture how variables at a prior time point can be used to explain variance in variables at a later time point. These are always modeled using auto- and cross-regressions by means of vector autoregression (VAR). By contrast, there are two types of relations commonly used to model the contemporaneous relations, which model how variables relate instantaneously. Until now, researchers must opt to either model contemporaneous relations as undirected relations among residuals (e.g., partial or full correlations) or as directed relations among the variables (e.g., paths or regressions). The choice for how to model contemporaneous relations has implications for inferences as well as the potential to introduce bias in the VAR lagged relations if the wrong type of relation is used. This article introduces a novel data-driven method, hybrid-group iterative multiple model estimation (GIMME), that provides a solution to the problem of having to choose one or the other type of contemporaneous relation to model. The modeling framework utilized in hybrid-GIMME allows for both types of contemporaneous relations in addition to the standard VAR relations. Both simulated and empirical data were used to test the performance of hybrid-GIMME. Results suggest this is a robust method for recovering contemporaneous relations in an exploratory manner, particularly with an ample number of time points per person. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Sraffian Supermultiplier and Cycles: Theory and Empirics

ABSTRACT This paper provides a theoretical and empirical reassessment of supermultiplier theory. First, we show that, as a result of the passive role it assigns to investment, the Sraffian supermultiplier predicts that the rate of utilization leads the investment share in a dampened cycle or equivalently that a convergent cyclical motion in the utilization-investment share plane would be counter-clockwise. Second, impulse response functions from standard recursive Vector Autoregressions for post-war US samples strongly indicate that the investment share leads the rate of utilization, or that these cycles are clockwise. These results raise questions about the key mechanism underlying supermultiplier theory.

Addressing COVID-19 Outliers in BVARs with Stochastic Volatility

Abstract The COVID-19 pandemic has led to enormous data movements that strongly affect parameters and forecasts from standard Bayesian vector autoregressions (BVARs). To address these issues, we propose BVAR models with outlier-augmented stochastic volatility (SV) that combine transitory and persistent changes in volatility. The resulting density forecasts are much less sensitive to outliers in the data than standard BVARs. Predictive Bayes factors indicate that our outlier-augmented SV model provides the best fit for the pandemic period, as well as for earlier subsamples of high volatility. In historical forecasting, outlier-augmented SV schemes fare at least as well as a conventional SV model.

THE SHORT-TERM CORONAVIRUS (COVID-19) PANDEMIC EFFECT: AN EMPIRICAL INVESTIGATION OF INDIAN STOCK MARKET (BSE)

India’s precautionary step toward COVID-19 led to 1.3 billion people enduring lockdown, consequently halting the wheels of the Indian economy. Though the crash of the stock market was evident and explanatory, it left all stakeholders (including the investors and government) with no choice. Deteriorating SENSEX and other indices forced investors to withdraw and lose attraction from the market and looped in more serious issues to the Indian stock market. This paper empirically analyses the short-term impact of COVID-19 on five selected BSE indices for SENSEX, FMCG, Bank, Corporate-Bond, and Industrial using econometric models. Stationarity was checked by Augmented Dickey-Fuller and Philips-Perron test. Autocorrelation was assessed and mitigated by Durbin-Watson, Breusch-Godfrey Serial Correlation LM test, and Cochrane-Orcutt transformation method. This study attempts to apply Multiple Regression, the GARCH model, Standard Vector Autoregression (S-VAR), and Impulse Response Function (IRF) to decode the relation. The results indicated that the COVID-19 pandemic is adversely affecting the performance of the Indian stock market in the short run. All the indices showed a negative relationship antecedent with the effect of COVID-19, except for the SPICBI index. This paper implies investment solutions for the investors and policymakers to cope with the unprecedented situation amidst COVID-19.

Understanding monetary and fiscal policy rule interactions in Indonesia

ABSTRACT We examine the interaction of monetary and fiscal policies in Indonesia from 1974Q2 to 2019Q1. Within a standard structural vector autoregression framework, we show that the reactions of the policy rules are consistent with theoretical predictions. For instance, a contractionary monetary policy is trailed by a contractionary fiscal policy with lower government expenditure. We extend the analysis to evaluate the interaction of policy rules during active and passive regimes. We show that monetary and fiscal policies are not synchronized over the full sample period, suggesting structural and institutional rigidities, particularly in the past. Restricting the sample to a recent period, we find the policies are more harmonized. We attribute this to the recent joint policy coordination initiatives between the monetary and fiscal authorities.

Oil price shocks and the US stock market: A nonlinear approach

We study the response of US stock market returns to oil price shocks and to what extent it behaves asymmetrically over the different phases of the business cycle. For this purpose, we decompose the oil price changes into supply and demand shocks in the oil market and assess the state-dependent dynamics of structural shocks on US stock returns using a smooth transition vector autoregression model. When nonlinearity is considered, quantitatively very different asymmetric dynamics are observed. Our findings show that the responses of US stock returns to disaggregated shocks are asymmetric over the business cycle and that the impact of demand-driven shocks on US stock returns is stronger and more persistent, especially when economic activity is depressed. Furthermore, the contribution of shocks to expectation-driven precautionary demand in recessions accounts for a larger share of the variability of US stock market returns than that predicted by standard linear vector autoregressions. • State-dependent effect of oil price shocks on US stock returns is investigated. • We decompose oil price changes into supply and demand shocks. • A smooth transition VAR is used for asymmetric dynamics over the business cycle. • Demand-driven shocks are larger and more persistent during recessions. • Oil price shocks explain around 40% for US stock market variations in recessions.

Granger Causality Testing in High-Dimensional VARs: A Post-Double-Selection Procedure

Abstract We develop an LM test for Granger causality in high-dimensional (HD) vector autoregressive (VAR) models based on penalized least squares estimations. To obtain a test retaining the appropriate size after the variable selection done by the lasso, we propose a post-double-selection procedure to partial out effects of nuisance variables and establish its uniform asymptotic validity. We conduct an extensive set of Monte-Carlo simulations that show our tests perform well under different data generating processes, even without sparsity. We apply our testing procedure to find networks of volatility spillovers and we find evidence that causal relationships become clearer in HD compared to standard low-dimensional VARs.

Credit supply shocks and household consumption in Russia

The paper presents a comparative analysis of the reaction of different consumer groups to the bank credit supply shocks. As parameters of consumer heterogeneity, we consider differences ( i ) in the form of home ownership (households with and without mortgages and tenants) and ( ii ) in the form of credit (households with large debt (mortgages, car loans), households with small (consumer) credit and without loans). For empirical analysis we use RLMS-HSE longitudinal Household Panel Survey for the period from 2006 to 2019, which covers two episodes of the credit crisis and post-crisis recovery. Bank credit supply shocks are estimated using the standard structural vector autoregression (SVAR) model, which also identifies aggregate supply and demand shocks and monetary policy shocks. Our results show that with a positive credit supply shock, which is equivalent to a 0.5 percentage point reduction in the interest rate on loans, households with mortgages increase their consumption by 2.1—2.5% more than households without mortgages. The observed effect is significant, since the average annual growth rate of consumption of households with mortgages in our sample is 3.8%. Our results indicate that the Bank of Russia’s macroprudential policy, which uses countercyclical capital buffers (Basel III) as a tool, can be very effective in smoothing the consumption of households with mortgage debt.

The distributive cycle: Evidence and current debates

AbstractThis paper surveys current debates on the distributive cycle. The literature builds on Goodwin's seminal 1967 chapter titled “A growth cycle.” We review theoretical motivations for the distributive cycle, which, despite significant differences, all imply that macroeconomic activity leads the labor share in a counterclockwise cycle in the activity‐labor share plane. Subsequently, we summarize and update evidence on the existence of a distributive cycle, with a focus on the post‐war U.S. macroeconomy. We analyze activity and labor share series and their interaction in the frequency domain, and also employ standard vector autoregressions. Results confirm the distributive cycle for the U.S. post‐war period. We contextualize results vis‐à‐vis current debates: (1) we consider a financial cycle, to rebut the theoretical possibility of “pseudo‐Goodwin” cycles, (2) demonstrate that a suppressed labor share and stagnation are compatible with short‐run Goodwin cycles, and argue that this link presents the way forward for research on secular stagnation.

Receptivity of capital structure with financial flexibility: A study on manufacturing firms

AbstractThe purpose of this paper is to empirically investigate the static capital structure decisions' dynamic responses due to shocks and its relative sensitivity in the presence of financial flexibility. To capture this, we use Panel Corrected Standard Error (PCSE) models and Panel Vector Autoregression (Panel‐VAR) models on the sample of 2,094 listed Indian Manufacturing firms from 2009 to 2019. The empirical findings demonstrate substantial difference in the dynamic responses of capital structure under the conditions of shocks given to its key determinants, with and without financial flexibility. Debt ratio of financially flexible firms reacts positively to asset tangibility and size whereas it reacts negatively to growth opportunity, non‐debt tax shield and profitability. These findings may assist firm managers and policy makers in gauging the limitations associated with leveraging their debt structure on the virtue of being financially flexible.

Forecasting with Shadow-Rate VARs

Interest rate data are an important element of macroeconomic forecasting. Projections of future interest rates are not only an important product themselves, but also typically matter for forecasting other macroeconomic and financial variables. A popular class of forecasting models is linear vector autoregressions (VARs) that include shorter- and longer-term interest rates. However, in a number of economies, at least shorter-term interest rates have now been stuck for years at or near their effective lower bound (ELB), with longer-term rates drifting toward the constraint as well. In such an environment, linear forecasting models that ignore the ELB constraint on nominal interest rates appear inept. To handle the ELB on interest rates, we model observed rates as censored observations of a latent shadow-rate process in an otherwise standard VAR setup. The shadow rates are assumed to be equal to observed rates when above the ELB. Point and density forecasts for interest rates (short term and long term) constructed from a shadow-rate VAR for the US since 2009 are superior to predictions from a standard VAR that ignores the ELB. For other indicators of financial conditions and measures of economic activity and inflation, the accuracy of forecasts from our shadow-rate specification is on par with a standard VAR that ignores the ELB.

The Sraffian Supermultiplier and Cycles: Theory and Empirics

This paper provides a theoretical and empirical reassessment of supermultiplier theory. First, we show that, as a result of the passive role it assigns to investment, the Sraffian supermultiplier (SSM) predicts that the rate of utilization leads the investment share in a dampened cycle or, equivalently, that a convergent cyclical motion in the utilization-investment share plane would be counterclockwise. Second, impulse response functions from standard recursive vector autoregressions (VAR) for postwar US samples strongly indicate that the investment share leads the rate of utilization, or that these cycles are clockwise. These results raise questions about the key mechanism underlying supermultiplier theory.

The COVID-19 Shock and Challenges for Time Series Models

We document the impact of COVID-19 on frequently employed time series models, with a focus on euro area inflation. We show that for both single equation models (Phillips curves) and Vector Autoregressions (VARs) estimated parameters change notably with the pandemic. In a VAR, allowing the errors to have a distribution with fatter tails than the Gaussian one equips the model to better deal with the COVID-19 shock. A standard Gaussian VAR can still be used for producing conditional forecasts when relevant off-model information is used. We illustrate this by conditioning on official projections for a set of variables, but also by tilting to expectations from the Survey of Professional Forecasters. For Phillips curves, averaging across many conditional forecasts in a thick modelling framework offers some hedge against parameter instability.

Asymmetric behavior of tobacco consumption in Spain across the business cycle: a long-term regional analysis.

Many theoretical and empirical studies have analyzed the relationship between the economic cycle and tobacco consumption using the GDP and unemployment rates as the key variables for measuring economic phases. However, few studies focus on the pathways that cause tobacco consumption to be linked with the economic cycle, and there are no studies analyzing the heterogeneous effects underlying this relationship across nations and regions. This article explores the relationship and its pathways in 16 Spanish regions for the period 1989-2018. To this end, we apply a Granger causality analysis based on the augmented vector autoregressive (VAR) model in levels and extra lags. This method provides more efficient and robust results than the standard VAR model, which can lead to biased results with limited samples, especially in a region-by-region analysis. The empirical results suggest that the impact of the business cycle on tobacco consumption is heterogeneous and specific to each region. In addition, although recession phases cause a decline in tobacco consumption in Spain, in line with the literature, this procyclical relationship does not occur for expansion phases in all regions. One of the main findings of this article is that in expansion phases, tobacco consumption is sensitive to GDP, while in recession phases, tobacco consumption is affected by unemployment. National and regional governments should consider these results when they develop smoking control policies because homogeneous strategies can lead to heterogeneous results. Thus, the results can be useful for policymakers dealing with tobacco control strategies.

Price dispersion in bitcoin exchanges

Bitcoin is traded in a number of exchanges, and there is a large and time-varying price dispersion among them. We identify the sources of price dispersion using a standard time-varying vector autoregression model with stochastic volatility, and we find that shocks to transaction fees and bitcoin price growth explain on average 20%, and sometimes more than 60%, of the variation of price dispersion.

Price Dispersion in Bitcoin Exchanges

Bitcoin is traded in a number of exchanges, and there is a large and time-varying price dispersion among them. We identify the sources of price dispersion using a standard time-varying vector auto-regression model with stochastic volatility. Using weekly data over the past 3 years, we find that shocks to transaction fees and bitcoin price growth explain on average 20%, and sometimes more than 60%, of the variation of price dispersion. We argue that the two variables are related to the profitability and risk of trading across the exchanges, and the impulse response functions are consistent with our interpretation.

ISLAMIC STOCK MARKET BEHAVIOR TOWARD CHANGES IN MACROECONOMIC VARIABLES

This study was conducted to assess the behaviour of the Islamic stock market towards the changes in the economic indicators in Malaysia. The variables involved were the FTSE Bursa Malaysia Emas Shariah Index (FBMES) which was a proxy to the Islamic Stock Market. The independent variables used were the Consumer Price Index (CPI), Islamic Banking Interbank Rate (IIR), Money Supply (M3) Islamic Unit Trust (IUT) and the Industrial Production Index (IPI).This study utilized the standard Vector Autoregressive (VAR) estimation model with monthly data from January 2007 to December 2017. The findings showed that there was a long-term relationship between Islamic stock market with the Islamic unit trust and the industrial production index. Based on the test results of the Vector Error Correction Model (VECM), the Islamic stock market was not influenced by any of the selected macroeconomic variables. However, the study found that the FBMES variables were significant in shaping the Granger's short-term impact on changes in Islamic interbank rate (IIR) and money supply (M3) variables. As such, this study proved that the Islamic stock market index and the macroeconomic variables could influence one another.

Real-time Forecasting of the Australian Macroeconomy Using Flexible Bayesian VARs

This paper evaluates the real-time forecast performance of alternative Bayesian Vector Autoregressive (VAR) models for the Australian macroeconomy. To this end, we construct an updated vintage database and estimate a set of model specifications with different covariance structures. The results suggest that a large VAR model with 20 variables tends to outperform a small VAR model when forecasting GDP growth, CPI inflation and unemployment rate. We find consistent evidence that the models with more flexible error covariance structures forecast GDP growth and inflation better than the standard VAR, while the standard VAR does better than its counterparts for unemployment rate. The results are robust under alternative priors and when the data includes the early stage of the COVID-19 crisis.