Factor-augmented VAR Model Research Articles

Emerging market economies and the world interest rate

We use a Factor Augmented VAR model to estimate the dynamic responses of interest rates in emerging market economies to the ‘world’ interest rate, which we extract from a dynamic factor model of yields in industrialized countries. Our results provide evidence that many emerging market yields respond to world rate shocks, at least gradually, which is broadly consistent with capital market integration. Our findings also suggest that the world rate captures information about emerging market yields not contained in US rates, which are typically used to proxy for the world rate.

Emerging Market Economies and the World Interest Rate

We use a Factor Augmented VAR model to estimate the dynamic responses of interest rates in emerging market economies to the 'world' interest rate, which we extract from a dynamic factor model of yields in industrialized countries. Our results provide evidence that many emerging market yields respond to world rate shocks, at least gradually, which is broadly consistent with capital market integration. Our findings also suggest that the world rate captures information about emerging market yields not contained in US rates, which are typically used to proxy for the world rate.

A factor-augmented VAR analysis of business cycle synchronization in east Asia and implications for a regional currency union

Debate continues over whether a monetary or currency union will be a viable alternative to the current exchange arrangements in East Asia. This paper adds to the literature by assessing the level of business cycle synchronization among 10 major East Asian countries, which is considered as a key precondition for a regional currency union. Unlike previous studies, this paper employs a factor-augmented VAR model that encompasses a large set of 62 foreign and domestic variables simultaneously. Five common shocks are identified, and we examine how and to what extent these shocks affect each economy in the region. Empirical results indicate that the majority of East Asian countries respond similarly to world and regional shocks. Of particular importance is the finding that individual GDPs are well synchronized in response to the two major determinants of world and regional GDP shocks. Overall, the evidence presents a positive case for consideration of a regional currency arrangement in East Asia. Some suggestions are offered concerning steps to build a foundation towards greater monetary cooperation in East Asia.

Monetary Policy and Factor-Augmented VAR Model

Measuring the transmission of monetary policy is the main subject in a large empirical literature, the conclusions about the role that monetary policy plays in the economy and the way it should be conducted by the central banks depending on the way the monetary policy affects each economy. Although the VAR approach to measuring the effects of monetary policy shocks appears to deliver a great deal of useful structural information, there are some problems that might arise during implementation. Factor Augmented Vector Autoregression models have been introduced into the economic literature in order to solve the issues that have been raised during a wide number of studies on the effect of monetary policy innovations on macroeconomic variables. For estimation purposes, 92 variables representing the evolution of production index, producer price index, consumer prices, unemployment rate, over the period 2001:M1 and 2013:M6 are considered. Principal component analysis is used in order to estimate the factors included in the model and Bayesian inference is used afterwards, more precisely Gibbs sampling algorithm, with the purpose of computing the posterior distribution of the unobservable states and the hyper-parameters.

Tests for overidentifying restrictions in Factor-Augmented VAR models

This paper develops tests for overidentifying restrictions in Factor-Augmented Vector Autoregressive (FAVAR) models. The identification of structural shocks in FAVAR can involve infinitely many restrictions as the number of cross sections goes to infinity. Our focus is to test the joint null hypothesis that all the restrictions are satisfied. Conventional tests cannot be used due to the large dimension. We transform the infinite-dimensional problem into a finite-dimensional one by combining the individual statistics across the cross section dimension. We find the limit distribution of our joint test statistic under the null hypothesis and prove that it is consistent against the alternative that a fraction of or all identifying restrictions are violated. The Monte Carlo results show that the joint test statistic has good finite-sample size and power. We implement our tests to an updated version of Stock and Watson’s (2005) data set. Our test result is further confirmed by the impulse responses of major macroeconomic variables to the monetary policy shock: the impulse responses based on the specification selected by our test are generally more plausible than those based on the specifications rejected by our test.

The transmission of international shocks to the UK. Estimates based on a time-varying factor augmented VAR

The aim of this paper is to gauge the importance of foreign demand, supply and interest rate shocks on the UK economy and assess how their role has changed over time. To that end we devise a time-varying factor augmented VAR model that captures the relationship between 17 industrialised countries and the UK and accounts for any temporal evolution in this relationship. The response of UK macroeconomic variables to a foreign interest rate shock is estimated to have changed significantly since the early 1990s. International demand shocks play an important role in driving World and UK real activity, especially during the recent recession.

Tests for Overidentifying Restrictions in Factor-Augmented VAR Models

Tests for Overidentifying Restrictions in Factor-Augmented VAR Models

Forecasting with factor-augmented error correction models

As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor-augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over the standard ECM and FAVAR models. In particular, it uses a larger dataset than the ECM and incorporates the long-run information which the FAVAR is missing because of its specification in differences. In this paper, we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simulations and several empirical applications. We show that FECM generally offers a higher forecasting precision relative to the FAVAR, and marks a useful step forward for forecasting with large datasets.

Global commodity cycles and linkages: a FAVAR approach

In this article, we examine linkages across non-energy commodity price developments by means of a factor-augmented VAR model (FAVAR). From a set of non-energy commodity price series, we extract two factors, which we identify as common trends in metals and food prices. These factors are included in a FAVAR model together with selected macroeconomic variables, which have been associated with developments in commodity prices. Impulse response functions confirm that exchange rates and economic activity affect individual non-energy commodity prices, but we fail to find strong spillovers from oil to non-oil commodity prices or an impact of the interest rate. In addition, we find that individual commodity prices are affected by common trends captured by the food and metals factors.

The effect of monetary policy on house price inflation

PurposeThis paper seeks to assess the impact of monetary policy on house price inflation for the nine census divisions of the US economy.Design/methodology/approachA factor‐augmented VAR (FAVAR) model is estimated using a large data set comprising of 126 quarterly series over the period 1976:01 to 2005:02.FindingsOverall, the results of this investigation show that house price inflation responds negatively to a positive monetary policy shock, suggesting that the framework does not experience the widely observed price puzzle encountered while analyzing monetary policy shocks with standard sized VARs.Research limitations/implicationsThe paper only considers house price inflation and ignores other housing market variables. Moreover, given the recent economy‐wide decline in the house price growth rates, it would be worthwhile to update the data set to a more recent period, to capture the possible breakdown in the relationship of house prices with fundamentals driving the market.Practical implicationsThe results based on the impulse response functions indicate that, in general, house price inflation responds negatively to monetary policy shock, but the responses are heterogeneous across the census divisions. In addition, the findings suggest, in particular, the importance of South Atlantic, East South Central, West South Central, Mountain and the Pacific divisions in shaping the dynamics of US house price inflation.Originality/valueTo the best of one's knowledge, this is the first paper to analyze the effect of monetary policy on house price inflation in the nine census divisions of the US economy using a FAVAR model.

Global Commodity Cycles and Linkages: A FAVAR Approach

In this paper we examine linkages across non-energy commodity price developments by means of a factor-augmented VAR model (FAVAR). From a set of non-energy commodity price series, we extract two factors, which we identify as common trends in metals and a food prices. These factors are included in a FAVAR model together with selected macroeconomic variables, which have been associated with developments in commodity prices. Impulse response functions confirm that exchange rates and of economic activity affect individual non-energy commodity prices, but we fail to find strong spillovers from oil to non-oil commodity prices or an impact of the interest rate. In addition, we find that individual commodity prices are affected by common trends captured by the food and metals factors.