Standard Vector Autoregression Research Articles

Exploring Monetary Policy Transmission Mechanisms with Gvar Models

In an era of globalization, all economic entities are closely connected. Under such a trend we need to consider about more aspects than before. Because of the standard vector autoregression (VAR) model cannot meet our needs, this study applies a more comprehensive and systematic Global Vector Autoregressive (GVAR) model proposed by Pesaran, Schuermann and Weiner (hereafter PSW) in 2004 and uses quarterly data, from 1st quarter of 1981 to 1st quarter of 2013 to investigate monetary policy transmission mechanisms. Our results indicate credit channels in the transmission mechanisms of monetary policy do exist in Singapore, the UK, Japan, and the PRC; and GVAR models appear to be poorly suited for small countries acting as the origin of shock.

Macroeconomic Effects of Disruptions in Global Food Commodity Markets: Evidence for the United States

We use two approaches to examine the macroeconomic consequences of disruptions in global food commodity markets. First, we embed a novel quarterly composite global production index for the four basic staples (corn, wheat, rice and soybeans) in a standard vector autoregression (VAR) model, and we estimate the dynamic effects of global food commodity supply shocks on the US economy. As an alternative, we also estimate the consequences of thirteen narratively identified global food commodity price shocks. Both approaches deliver similar conclusions. Specifically, an unfavorable food commodity market shock raises food commodity prices, and leads to a rise in food, energy and core inflation, and to a persistent fall in real GDP and consumer expenditures. A closer inspection of the pass-through reveals that households do not only reduce food consumption. In fact, there is a much greater decline in durable consumption and investment. Overall, the macroeconomic effects turn out to be a multiple of the maximum impact implied by the share of food commodities in the consumer price index and household consumption.

Does Regulatory Bank Oversight Impact Economic Activity? A Local Projections Approach

Existing research generally finds that the magnitude of the effect of supervisory rating shocks on real economic activity is small and short-lived. This is puzzling because corrective actions addressing weaknesses in underwriting and other practices frequently include lending restrictions and thus would be expected to have a stronger effect on real activity. As supervisory actions curb poorly underwritten or uneconomic loans, transmission of macroprudential policy throughout the macroeconomy should be evident in the dynamic responses of the real GDP and other measures of real activity. We use the local projections approach to estimate impulse responses from a vector autoregression (VAR) model. We show that the effect of supervisory stringency shocks is larger than the one estimated with the standard Cholesky structural VAR approach. We find that the effects are asymmetric: bank downgrades lead to a pronounced decline in real activity, while upgrades do not result in its increase. This would follow if the decrease is driven by poor lending practices that would not be expected to resume when the bank is upgraded. The linear framework averages out these effects, overstating the impact of upgrades, and understating that of downgrades. Furthermore, we document the presence of nonlinear effects for the downgrade shocks, as their impact increases disproportionately with its size. Such effects are not observed for upgrade shocks. Finally, we demonstrate that our results are robust to the inclusion of a variety of controls and additional endogenous variables.

Macroeconomic Effects of Disruptions in Global Food Commodity Markets: Evidence for the United States

We use two approaches to examine the macroeconomic consequences for the United States of disruptions in global food commodity markets. First, we embed a novel quarterly composite global production index for the four basic staples—corn, wheat, rice, and soybeans—in a standard vector autoregression model, and we estimate the dynamic effects of global food commodity supply shocks on the U.S. economy. As an alternative, we also estimate the consequences of 13 narratively identified global food commodity price shocks. Both approaches lead to similar conclusions. Specifically, an unfavorable food commodity market shock raises food commodity prices, and leads to a rise in food, energy, and core inflation, and also to a persistent decline in real GDP and consumer expenditures. A closer inspection of the pass-through reveals that households do not only reduce food consumption. In fact, there is a much greater decline in durable consumption and investment. Overall, the macroeconomic effects turn out to be a multiple of the maximum impact implied by the share of food commodities in the consumer price index and household consumption.

Real-Time Forecasting With a Mixed-Frequency VAR

This article develops a vector autoregression (VAR) for time series which are observed at mixed frequencies—quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to implement a data-driven hyperparameter selection. Using a real-time dataset, we evaluate forecasts from the mixed-frequency VAR and compare them to standard quarterly frequency VAR and to forecasts from MIDAS regressions. We document the extent to which information that becomes available within the quarter improves the forecasts in real time. This article has online supplementary materials.

Euro area monetary policy transmission in Estonia

This paper studies the effect of a monetary policy shock in the euro area on the main Estonian economic and financial variables between 2000 and 2012. Using a standard structural vector autoregression (SVAR) model we find strong and persistent effects on Estonian GDP, private consumption, corporate investment, and imports. A monetary policy shock also has strong and sluggish effects on the housing loan and consumer credit interest rates. The estimated reaction of Estonian GDP and the GDP deflator-based inflation rate is about four times stronger than the reaction of euro area-wide aggregates. The strength of the impact depends on the inclusion of the data from the years of the recent financial and economic crisis.

The Effects of Monetary Policy Shocks in the USA: A Forecast‐Augmented VAR Approach

This paper formulates a forward‐looking monetary policy function for the USA in a structural vector autoregression (VAR) model, by using forecasts of key macroeconomic variables, in addition to the ex post realised variables used in a standard VAR. Since this forecast‐augmented VAR (FOAVAR) uses both forecasted and realised variables, and the standard VAR uses only realised variables, the standard VAR is nested in the FOAVAR. I find that the Fed responds to forecasted macroeconomic variables more significantly than realised variables. I also find that the monetary policy shock in the FOAVAR generates impulse responses of variables that are consistent with the predictions of economic theories, while the policy shock in the standard VAR causes a price puzzle: an increase in the price level due to a contractionary policy shock. These results suggest that a monetary policy function identified in a standard VAR, by using only realised macroeconomic variables, may incorrectly represent the Fed's policy function.

Alternativní specifikace, odhad a identifikace vektorových autoregresí

The article focuses on various aspects of specifi cation, estimation and identifi cation of vector autoregression (VAR) models. Key VAR-specifi c topics of verifi cation of an estimated model are also covered, as well as the differences between a standard (unrestricted) and structural VAR model. Subsequently, we address theoretical properties and practical aspects of impulse response functions (IRFs) as calculated upon estimated VAR models. Topics such as Cholesky decomposition (CHD), orthogonalised and generalised IRFs are discussed. Properties of VAR models are compared against alternative econometric modelling tools, such as simultaneous equation models and dynamic stochastic general equilibrium (DSGE) models. The article is supplemented with an illustrative example: on an aggregated EMU-wide level, we estimate a VAR (2) model for real GDP, CPI and PPI infl ation. IRFs are calculated using two different CHD orderings and compared to generalised IRFs. We fi nd the IRFs from our illustrative model to be very robust against the chosen IRF calculation method and against equation ordering changes.

Identification and Bayesian Estimation of Dynamic Factor Models

We consider a set of minimal identification conditions for dynamic factor models. These conditions have economic interpretations and require fewer restrictions than the static factor framework. Under these restrictions, a standard structural vector autoregression (SVAR) with measurement errors can be embedded into a dynamic factor model. More generally, we also consider overidentification restrictions to achieve efficiency. We discuss general linear restrictions, either in the form of known factor loadings or cross-equation restrictions. We further consider serially correlated idiosyncratic errors with heterogeneous dynamics. A numerically stable Bayesian algorithm for the dynamic factor model with general parameter restrictions is constructed for estimation and inference. We show that a square-root form of the Kalman filter improves robustness and accuracy when sampling the latent factors. Confidence intervals (bands) for the parameters of interest such as impulse responses are readily computed. Similar identification conditions are also exploited for multilevel factor models, and they allow us to study the “spill-over” effects of the shocks arising from one group to another. Supplementary materials for technical details are available online.

Non-linear dynamics of employment, output and real wages in Canada

Purpose – The purpose of this paper is to examine the relationship among employment, real wage, and output growth in Canada. Design/methodology/approach – Using quarterly data from 1994q2 to 2012q3, this paper employs a vector autoregressive framework while allowing for the derivation of output from its historical maximum over the sample period to affect future output, employment, and real wage growth dynamics. Findings – There are three main findings: output growth is significant in predicting employment growth and vice versa; real wage growth neither Granger causes employment growth nor output growth, but employment growth Granger causes real wage growth; and non-linear dynamics, captured by the current depth regression (CDR) effect term, through the sign as well as the magnitude of output changes, are important in characterizing the evolution of the relationship among output, employment, and real wage growth. Practical implications – The findings of this research have significant implications for policy makers. Output and employment growth are important in forecasting each other in Canada. In contrast to the mainstream theory, real growth is insignificant in explaining the future dynamics of employment in Canada. Policies need to be formulated to encourage the growth of employment to ensure sustain output growth. Originality/value – This study examines empirically the real output, real wage, and employment link in Canada. This study uses the most recently revised GDP data arising from the 2012 Historical Revision of the Canadian System of National Accounts. The econometric methodology involves the standard vector autoregression (VAR) model to which the authors introduce non-linear dynamics through a term that controls for the deviation of output from its preceding historical maximum: the CDR effect.

Commodity Price Responses to Monetary Policy Surprises

AbstractInformation contained in high‐frequency financial market data reveals that a 10 basis‐point surprise increase in interest rates causes commodity prices to fall immediately by approximately 0.6%. This is similar to the estimated responses of both the Standard and Poor's 500 and a United States trade weighted exchange rate index, and approximately five times larger than the response in a standard vector autoregression, even twelve months after the shock. Metals prices tend to respond more than agricultural commodities. The point estimate for oil prices is similar to other commodities, but is estimated less precisely.

Empirical Econometric Analysis of Relationship between Fiscal-Monetary Policies and Output on SAARC Countries

The seminal 1968 paper of Anderson and Jordan on the relative effectiveness of monetary and fiscal policy on output stabilization caused many debates among both economists and policy makers. This paper uses a standard unrestricted VAR (vector autoregression) model to analyze the above relationship for five SAARC (South Asian Association of Regional Cooperation) countries (Bangladesh, India, Nepal, Pakistan and Sri Lanka) over the period of 1974–2007. In a system of five-equation of VAR model, this paper reveals that money supply, government expenditure, the real exchange rate and the foreign interest rate are co-integrated for all countries. The results of both the VD (Variance decomposition) and the IRFs (Impulse response functions) indicate that monetary policy has been more effective on output than fiscal policy in the case of Pakistan and Sri Lanka, whereas fiscal policy has had a more powerful effect than monetary policy on Bangladesh, India and Nepal.

Testing the value of lead information in forecasting monthly changes in employment from the Bureau of Labor Statistics

This article examines the value of lead information by investigating the predictive power the automatic data processing (ADP) report has on nonfarm payroll employment data released by the Bureau of Labor Statistics (BLS) 2 days after the ADP. We find that updating a vector autoregression (VAR) forecast with the ADP data improves the forecast accuracy relative to a standard VAR forecast. However, this informational advantage disappears if real-time comparisons are made with the Bloomberg consensus forecasts of the BLS which are available prior to the ADP. We explore the confounding effects of data revisions and the potential pitfalls in testing the value of lead information based on the accumulated historical data.

The Differentiated Research of China’s Monetary Policy’s Effect on Stock Price under the SVAR Model----Empirical Analysis Based on Different Economic Backgrounds

This paper empirically analyzes the impact of China’s monetary policy on stock price in different economic backgrounds by constructing the SVAR (Structural Vector Auto Regression) based on the standard VAR (Vector Auto Regression). The results show that: (i) In different economic backgrounds, the direction and size of China’s monetary policy’s impact on stock price are not same; (ii) There exist different impacts on stock price from monetary policy in short run, but in long-term the impacts do not exist in two economic backgrounds.

Hierarchical vector auto-regressive models and their applications to multi-subject effective connectivity

Vector auto-regressive (VAR) models typically form the basis for constructing directed graphical models for investigating connectivity in a brain network with brain regions of interest (ROIs) as nodes. There are limitations in the standard VAR models. The number of parameters in the VAR model increases quadratically with the number of ROIs and linearly with the order of the model and thus due to the large number of parameters, the model could pose serious estimation problems. Moreover, when applied to imaging data, the standard VAR model does not account for variability in the connectivity structure across all subjects. In this paper, we develop a novel generalization of the VAR model that overcomes these limitations. To deal with the high dimensionality of the parameter space, we propose a Bayesian hierarchical framework for the VAR model that will account for both temporal correlation within a subject and between subject variation. Our approach uses prior distributions that give rise to estimates that correspond to penalized least squares criterion with the elastic net penalty. We apply the proposed model to investigate differences in effective connectivity during a hand grasp experiment between healthy controls and patients with residual motor deficit following a stroke.

Are the Effects of Monetary Policy Asymmetric in India? Evidence from a Nonlinear Vector Autoregression Approach

This paper uses Indian quarterly data for the period of 1960:Q2-2011:Q2 to test for nonlinearity in a standard monetary vector autoregression (VAR) model comprising of output, price and money, using an estimation strategy that is consistent with wide range of structural models. We find that positive and negative monetary policy shocks have an immediate short-live and a delayed persistent asymmetric effect on output and price respectively. In addition, we show that compared to a linear VAR, the nonlinear VAR has a bigger impact of a monetary policy shock on output and price. In general, we conclude that there are clear gains from modelling monetary policy using a nonlinear VAR framework.

Regime dependence of the fiscal multiplier

After the financial market meltdown of the years 2007–2008 the Obama administration responded with large fiscal stimulus package, yet the reaction to this stimulus has been diverse. Some predicted a multiplier effect in the order of 1.5, others argued that the multiplier will be less than 0.5. Such multiplier estimates typically stem from estimated linear vector autoregressions (VARs) or linearized versions of DSGE models. In this paper, we argue that neither conventional VAR analysis nor linearized DGSE models may be appropriate to evaluate demand effects arising from such a stimulus package. The reason is, as recent research suggests, that the timing of demand shocks matters. To assess the multiplier's variability, we adopt a regime-dependent VAR approach. As is shown in detail, our model specification is grounded on theoretical considerations. The empirical analysis presented here suggests that a regime-dependent VAR-specification is favored for U.S. output and employment data, and that the standard (one-regime) VAR methodology is inappropriate for analyzing multi-regime processes. Although we employ a nonlinear VAR framework, the chosen setup allows the use of largely familiar macroeconometric modeling tools. Estimating a two-regime VAR, we show that the fiscal multiplier varies with the state of the business cycle and the particular specifics of the measure taken. For the U.S. we find, for example, the fiscal expansion multiplier is much higher in a regime of a low economic activity than in a regime of high activity. As we also show it is size dependent.

Stationary Vector Autoregressive Representation of Error Correction Models

The paper introduces a stationary vector autoregressive (VAR) representation of the error correction model (ECM). This representation explicitly regards the cointegration error a dependent variable, making the direct implementation of standard dynamic analyses using standard VAR models possible, particularly with respect to the cointegration error. Of course, an ECM does not have an explicit VAR form, and thus, it is not convenient for conducting such dynamic analyses. In this regard, we transform the original nonstationary VAR model into a VAR model with the cointegration error and stationary variables. Finally, we employ the model to dynamically analyze the real exchange rate between the US dollar and the Japanese yen.

Identifying Government Spending Shocks: It's all in the Timing*

Standard vector autoregression (VAR) identification methods find that government spending raises consumption and real wages; the Ramey–Shapiro narrative approach finds the opposite. I show that a key difference in the approaches is the timing. Both professional forecasts and the narrative approach shocks Granger-cause the VAR shocks, implying that these shocks are missing the timing of the news. Motivated by the importance of measuring anticipations, I use a narrative method to construct richer government spending news variables from 1939 to 2008. The implied government spending multipliers range from 0.6 to 1.2.

Gold rush fever in business cycles

A flexible price model of the business cycle is proposed, in which fluctuations are driven primarily by inefficient movements in investment around a stochastic trend. A boom in the model arises when investors rush to exploit new market opportunities even though the resulting investments simply crowd out the value of previous investments. A metaphor for such profit driven fluctuations are gold rushes, as they are periods of economic boom associated with expenditures aimed at securing claims near new found veins of gold. An attractive feature of the model is its capacity to provide a simple structural interpretation to the properties of a standard consumption and output Vector Autoregression.