Factor-augmented Vector Autoregression Research Articles

The shale oil boom and the US economy: Spillovers and time‐varying effects

SummaryWe provide new evidence that the transmission of oil price shocks to the US economy has changed with the shale oil boom. To show this, we develop a time‐varying parameter factor‐augmented vector autoregressive (FAVAR) model with a large data environment of state‐level, industry, and aggregate US data. The model effectively captures potential spillovers between oil and non‐oil industries, as well as variation over time. Specified in this way, we find that investment, income, industrial production, and (non‐oil) employment in most oil‐producing and some manufacturing‐intensive US states increase following an oil‐specific shock—effects that were not present before the shale oil boom.

Output, employment, and price effects of U.S. narrative tax changes: a factor-augmented vector autoregression approach

Output, employment, and price effects of U.S. narrative tax changes: a factor-augmented vector autoregression approach

How is the ECB’s quantitative easing transmitted to the financial markets?

Purpose Over the last few years, the European Central Bank (ECB) has adopted unconventional monetary policies. These measures aim to boost economic growth and increase inflation through the bond market. The purpose of this paper is to study the impact of the ECB’s quantitative easing (QE) on the investor’s behavior in the stock market. Design/methodology/approach First, the authors theoretically identify the transmission channels of the QE shocks to the stock market. Then, the authors empirically assess the financial market’s responses to QE shocks in a data-rich environment using a factor augmented VAR (FAVAR). Findings The results show that the ECB’s unconventional monetary policy positively affects the stock market. A QE shock leads to an increase in stock prices and a drop in the realized volatility and the implied risk premium. The authors also suggest that the ECB’s QE is transmitted to the stock market through five main channels: the liquidity, the expectation, the portfolio reallocation, the interest rates and the risk premium channels. Practical implications The findings help to better understand the behavior of stock market assets in a data-rich economic context and guide investors and policymakers in the presence of unconventional monetary tools. For instance, decision-makers and investors should consider the short-term effect of the QE interventions and the changing behavior of the financial actors over time. In addition, high stock market returns can increase risk appetite. This can lead investors to underestimate the market risk. Decision-makers and market participants should take into consideration the impact of the large injection of money through the QE, which may raise the risk of a speculative bubble in the financial market. Originality/value To the best of the authors’ knowledge, this is the first study that incorporates a theoretical and empirical analysis to explore QE transmission to the stock market in the European context. Unlike previous studies, the authors use the shadow rate proposed by Wu and Xia (2017) to quantify the effect of the ECB’s QE in a data-rich environment. The authors also include two key risk indicators – the stock market risk premium and the realized volatility – to capture investors’ behavior in the stock market following QE shocks.

Measuring the Impact of Monetary Policy and Macroeconomic Volatility in Rwanda: Application of Factor Augmented Vector Autoregressive (FAVAR) Approach

Measuring the Impact of Monetary Policy and Macroeconomic Volatility in Rwanda: Application of Factor Augmented Vector Autoregressive (FAVAR) Approach

Financial Condition Indices in an Incomplete Data Environment

Abstract We construct a Financial Conditions Index (FCI) for the United States using a dataset that features many missing observations. The novel combination of probabilistic principal component techniques and a Bayesian factor-augmented VAR model resolves the challenges posed by data points being unavailable within a high-frequency dataset. Even with up to 62 % of the data missing, the new approach yields a less noisy FCI that tracks the movement of 22 underlying financial variables more accurately both in-sample and out-of-sample.

Chinese monetary policy spillovers on its international portfolio investment flows

This paper examines Chinese monetary policy spillovers on its international portfolio investment flows with a factor-augmented vector autoregression (FAVAR) model. The paper extracts a FPI factor from certain time series to measure monthly foreign portfolio investment flows (FPI). Four Chinese monetary policies are considered: central-bank target policy rate; required reserve ratio; M2; banks' loans. The robust and significant results show that interest rates and banks' loans have significant impact on Chinese international portfolio investment flows, while changes in reserve requirements are not effective. It reveals the increasingly important effects of Chinese monetary policies on its cross-border portfolio investment flows following decades' capital account openness and the reform of interest rate liberalization in China, which indicates that Chinese monetary policy is approaching to a market-based framework.

Non-linear dimension reduction in factor-augmented vector autoregressions

This paper introduces non-linear dimension reduction in factor-augmented vector autoregressions to analyze the effects of different economic shocks. I argue that controlling for non-linearities between a large-dimensional dataset and the latent factors is particularly useful during turbulent times of the business cycle. In simulations, I show that non-linear dimension reduction techniques yield good forecasting performance, especially when data is highly volatile. In an empirical application, I identify a monetary policy as well as an uncertainty shock excluding and including observations of the COVID-19 pandemic. Those two applications suggest that the non-linear FAVAR approaches are capable of dealing with the large outliers caused by the COVID-19 pandemic and yield reliable results in both scenarios.

Identifying oil price shocks with global, developed, and emerging latent real economy activity factors

SummaryThis paper proposes an identification strategy for international oil price shocks while accounting for the heterogeneous sources of oil demand from global, developed, and emerging economies. Unlike existing works, we isolate global oil demand shocks, associated with a global real economic activity factor, from oil demand shocks originating specifically from developed and emerging economies, associated with real economic activity factors within these two groups of economies. The paper uses a structural factor‐augmented vector autoregression (FAVAR) model with latent global and specific factors to model crude oil demand and supply. To identify the shocks, we extract real economic activity factors from a large panel of emerging and developed economies' real activity variables using a two‐level factor model. The paper shows how structural shocks can be identified by solving equations that arise from economically meaningful zero restrictions on the impact matrix of the reduced‐form FAVAR model innovations. The empirical application shows that identifying the international oil demand shocks based on the global and specific latent factors is essential to appropriately quantify their heterogeneous impacts on these factors, the crude oil production, and the real oil price.

Understanding the global drivers of inflation: How important are oil prices?

This paper examines the global drivers of inflation in 55 countries over 1970–2022. The paper estimates a factor-augmented vector autoregression model for each country and assesses the importance of several domestic and global shocks, including oil price shocks. It reports three main results. First, global shocks have explained about 26% of inflation variation in a typical economy. Oil price shocks accounted for only about 4% of inflation variation, but they had a statistically significant impact on inflation in three-quarters of the countries. Second, global shocks have become more important in driving inflation variation over time. The share of inflation variance caused by oil price shocks increased from 4% prior to 2000 to roughly 9% during 2001–22. They also accounted for some of the steep runup in inflation between mid-2021 and mid-2022. Third, oil price shocks tended to contribute significantly more to inflation variation in advanced economies, countries with stronger global trade and financial linkages, commodity importers, net energy importers, countries without inflation-targeting regimes, and countries with pegged exchange rate regimes. The headline results are robust to a wide range of sensitivity exercises—including alternative measures of global factors and oil prices—and aggregation of countries.

What is the role of perceived oil price shocks in inflation expectations?

Not much. We identify the perceived oil price shock, as well as perceived global demand and supply shocks, using sign restrictions in a factor-augmented vector autoregression model that includes forecasts for crude oil price growth, real GDP growth, and inflation across 84 economies. The perceived oil price shock explains only 10% of the fluctuations, on average, in global inflation expectations from January 2012 to December 2022, and accounts for an even smaller fraction during the COVID-19 pandemic. Allowing for the oil price noise shock – reflecting exogenous shifts in agents’ optimism and pessimism – does not materially change the limited pass-through of the perceived oil price shock to inflation expectations. In contrast, perceived global supply and demand shocks dominate, especially since the onset of the pandemic. Over the first eight months, professional forecasters viewed the pandemic, on net, as a negative demand shock and lowered their short-term inflation expectations. In early 2021, professionals quickly switched their views and sharply increased their inflation expectations amid burgeoning and persistent supply chain disruptions and labor constraints.

The sources of economic uncertainty: Evidence from eurozone markets

This study examines the sources of economic uncertainty in Euro Area countries with a special focus on the effects of the ECB’s monetary policy on economic uncertainty, for the period between 2001 and 2020. The contribution of the research lies (a) on the employment of an uncertainty factor that combines information from different uncertainty variables, rather than just one as in many previous studies, (b) on the fact that we obtain our results with the employment of a significant number of variables that consist a large data set which offers a great amount of information and (c) the examination of the behavior of Core and Peripheral Eurozone countries, since many studies indicate that their reaction to economic shocks may be different. We examine a sample of four Core and four Peripheral Eurozone Countries and employ for each country and for panel data a Factor-Augmented Vector Autoregression framework (FAVAR) with six different factors (real activity, inflation, interest rates, monetary policy, financial market and uncertainty). The results indicate that real activity and financial markets are important contributors to economic uncertainty, especially for the Core Eurozone countries, while for the Peripheral countries the uncertainty factor itself seems to contribute to its own level; in addition, our results indicate that periods of high uncertainty tend to be followed by periods of low uncertainty.

Monetary policy in China: A Factor Augmented VAR approach

This paper investigates the effectiveness of Chinese monetary policy. We employ the data-rich Factor Augmented VAR (FAVAR) model to address the paucity of Chinese data. We find that the Divisia monetary aggregate M2 (DMA2) that considers both the quantity of monetary assets and the user-cost of the money/forgone interest rate is more effective in boosting economic activities and prices than other instruments (e.g., interest rate, simple money supply M2, and required reserve ratio). These other instruments produce outcomes that contradict theoretical predictions in at least one area. Therefore, we suggest the use of the DMA2 to steer the performance of the Chinese economy effectively.

The impact of international commodity price shocks on macroeconomic fundamentals: Evidence from the US and China

In light of the increased volatility in international commodity markets, we aim to investigate the effects of international commodity price shocks on the macroeconomic fundamentals of the United States and China. We expand upon existing literature by employing the Dynamic Hierarchical Factor Model (DHFM) to analyze commodities across different sectors. This approach allows us to uncover the heterogeneity and specific synergies among various commodity categories. Furthermore, we construct the Factor Augmented Vector Autoregression Model (FAVAR) and the Time-varying Parameter FAVAR model, utilizing the common factors extracted and monthly data on fundamental macroeconomic variables from the US and China. By conducting impulse response analysis, we empirically demonstrate the impact of international commodity price shocks on key macroeconomic indicators, including industrial production, CPI, interest rates, stock prices, and exchange rates. Moreover, we provide evidence that these impacts exhibit significant variations over time, particularly in response to major global events such as the 2008 Global Financial Crisis, the 2020 COVID-19 pandemic, and the 2022 Russia-Ukraine conflict crisis. Our findings yield valuable insights for investors, researchers, and policymakers seeking to understand the influence of international commodity prices on macroeconomic conditions.

State transformation of information spillover in asset markets and effective dynamic hedging strategies

This paper aims to develop a strategy to effectively and dynamically hedge risk by considering regime transitions of spillover effects between assets. We take six assets (stocks, bonds, real estate, currency exchange, crude oil, and gold) that are commonly used to construct investment portfolios as examples and analyze asset price data between September 2002 and January 2022. In doing so, we aim to examine the information spillover of different asset prices in both bear and bull market environments to determine whether state transformation affects dynamic hedging effectiveness. Using Markov-Switching Factor-Augmented Vector Autoregression (MS-FAVAR), we construct a regimen-switching model using variables from finance and economic conditions as endogenous variables to define the state transformations of the information spillover. Empirical results reveal that the MS-FAVAR model highlights changes in information spillover during a financial crisis/economic recession. Using dynamic-weighted hedging portfolios constructed with different indicators, we find that hedging effectiveness and volatility vary depending on the state of information spillover between different asset markets and that bear markets significantly impacted hedging effectiveness. Results also show that the panic sentiment (the fear index) explains the probability of a bear market. It is suggested that the state transformation of information spillovers should be monitored periodically, and hedging portfolios should be dynamically adjusted (bear or bull market) with shifting fear sentiment.

The relationship between climate risk, climate policy uncertainty, and CO2 emissions: Empirical evidence from the US

This paper examines the relationship between climate risk and climate policy uncertainty, and CO2 emissions in the US over the 2000–2022 period using a structural Factor- Augmented Vector AutoRegression (FAVAR) model with a two-step principal component analysis based on monthly observations. We employ a very recent measure to proxy for uncertainty regarding climate policy based on the Climate Policy Uncertainty Index (CPU) of Gavriilidis (2021), while Climate Risk is proxied by financial cost of natural disasters and number of deaths. We use different variables for CO2 emissions, based on total and sectoral emission (commercial, electric power, residential sector, transportation, and industrial sector). The results indicate that a significant percentage of the variance of CO2 emissions in the US, is explained by Natural Disasters Cost, which also seem to account for a significant percentage of the US Total Renewable Energy Consumption variance. Shocks to disaster costs seem to decrease all type of emissions significantly and also increase renewable energy use significantly. Natural disasters increase political disagreement among U.S. politicians, as well as, the climate policy uncertainty, highlighting the need for efficient policymaking and regulations. In further results, we find that an increase in Partisan Conflict decreases emissions and explains a significant amount of renewable energy variance.

Factor-Augmented Vector Autoregression with narrative identification. An application to monetary policy in the US

We extend the Bayesian Factor-Augmented Vector Autoregressive model (FAVAR) to incorporate an identification scheme based on an external instrument approach. Using this novel modelling framework, we show that a monetary policy tightening in the United States has contractionary effects on the economy. Moreover, accounting for a large information set seems to help mitigate price and real economic puzzles in the estimated impulse responses.

Estimation and Inference on Time-Varying FAVAR Models

ABSTRACT We introduce a time-varying (TV) factor-augmented vector autoregressive (FAVAR) model to capture the TV behavior in the factor loadings and the VAR coefficients. To consistently estimate the TV parameters, we first obtain the unobserved common factors via the local principal component analysis (PCA) and then estimate the TV-FAVAR model via a local smoothing approach. The limiting distribution of the proposed estimators is established. To gauge possible sources of TV features in the FAVAR model, we propose three L 2 -distance-based test statistics and study their asymptotic properties under the null and local alternatives. Simulation studies demonstrate the excellent finite sample performance of the proposed estimators and tests. In an empirical application to the U.S. macroeconomic dataset, we document overwhelming evidence of structural changes in the FAVAR model and show that the TV-FAVAR model outperforms the conventional time-invariant FAVAR model in predicting certain key macroeconomic series.

Is Macroprudential Policy Instrument Blunt? Empirical Analysis Based on Japan's Experience from the 1970s to the 1990s

AbstractMacroprudential instruments, especially sectoral instruments, are considered to be precise tools that work only in areas of concern. However, it has not yet been fully tested in practice whether they affect only the targeted sectors and do not have undesirable spillover effects on nontargeted sectors. To fill this gap, we empirically study the impact of an instrument called Quantitative Restriction (QR), a policy tool used in Japan in the 1990s to curb excessive land price rises by requiring banks to contain real estate lending. We use narrative records to construct QR shocks and estimate their impact using a factor‐augmented vector autoregression (FAVAR). Our findings are summarized as follows. First, contractionary QR shocks reduced not only real estate lending and land prices, but also lending to other industries, putting downward pressure on the macroeconomy and lowering bank solvency. Second, industry groups and banks with balance sheets that were more exposed to changes in land prices and real estate transactions responded greater to these shocks, illustrating the role of balance sheet composition in spillovers and the importance of choosing the right timing for implementation following these shocks, suggesting that there may have been leakages through these institutions. Third, some nonbank financial institutions that were not required to report the loan results to the authorities did not reduce their lending following these shocks, which accords with the view that there were leakages through these institutions.

Sources of emerging market business cycles: an open-economy factor-augmented VAR approach

ABSTRACT This paper constructs an open-economy factor-augmented VAR model to assess the dynamic effects of global shocks on emerging market economies and to quantify their relative importance in explaining macroeconomic fluctuations in emerging countries. An unexpected favourable shock to global demand and supply has a strong and positive effect on emerging markets, whereas an unanticipated rise in global interest rates and commodity prices leads to a significant decline in aggregate activity. Variance decomposition analysis implies that more than 80% of the variation in emerging market output growth can be attributed to the global shocks. In particular, the global demand shock is the most critical, explaining roughly 30% of the fluctuation in output growth. The global supply shock is closely associated with the medium-to-long-term variation in output growth, explaining about 17%, whereas the monetary policy and commodity price shocks are relatively relevant for the short-term variation, explaining about 20% respectively.

An indicator of monetary bias for emerging and partially dollarized economies: The case of Uruguay

The instability of the relationships between interest rates, amount of money, and exchange rate, and the transmission problems between different interest rates hinder the measurement of monetary policy through a single variable. This difficulty is particularly relevant in emerging and partially dollarized economies. This paper proposes a multivariate indicator of monetary bias for these economies in which the monetary and financial variables are considered according to the impact they have on inflation. We use a Factor Augmented Vector AutoRegressive Moving Average model with eXogenous variables (FAVARMAX) to estimate these effects in the case of Uruguay. Using the evolution of Monetary Conditions Index (MCI) proposed, we characterize the policy adopted by the Central Bank of Uruguay between 2010–2019, a period of inflation targeting.