Factor-augmented Vector Autoregressive Model Research Articles

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.

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.

The confidence channel of U.S. financial uncertainty: Evidence from industry-level data

This paper investigates the role of consumer pessimism in amplifying U.S. financial uncertainty’s effects on sectoral industrial production. The existing literature largely focuses on aggregate data and uses measures of uncertainty that may be endogenous to economic activity. We use a measure of uncertainty that is exogenous to output and estimate a factor-augmented vector autoregressive model. We find heterogeneity in the responses of sectoral output and inflation to financial uncertainty shocks, which reveals important allocative effects. We then conduct a counterfactual analysis and find evidence that the effect of financial uncertainty on aggregate and sectoral output is amplified via a drop in consumer confidence, which reflects the importance of behavioral components in propagating uncertainty shocks. A historical decomposition analysis shows that financial uncertainty was not the main driver behind the 2008–2009 financial crisis and the COVID-19 recession. Nevertheless, in both recessions, financial uncertainty propagated via a drop in consumer confidence.

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 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.

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.

The effects of central bank extraordinary measures on financial conditions: Evidence from Mexico

ABSTRACT This paper analyses the effects of the extraordinary measures implemented by the Central Bank of Mexico during the COVID-19 pandemic on financial conditions. For this purpose, we estimate a factor-augmented vector autoregressive model for the period 2001–2021. Based on this model, we construct a Financial Conditions Index, estimate the response of this indicator and its components from a shock to the outstanding amount of these measures, and conduct a counterfactual exercise to further analyse the effect of the aforementioned measures. The main results indicate that these extraordinary measures seem to have contributed to improve financial conditions. In particular, we find that if these measures had not been implemented, the sovereign risk premium, the 10-year government bond yield, the slope of the yield curve, and the long- and short-term yield spreads between Mexico and the US would have been higher by around 56, 31, 27, 37, and 49 basis points in December 2020, respectively. At the same time, the Mexican peso/US dollar exchange rate and its volatility would have been higher by 5 and 2 percentage points, respectively. In turn, the Mexican stock market index would have been lower by 10 percentage points.

The Effect of Economic Growth Volatility on Selected Industries in Tehran Stock Exchange

The main purpose of this study is to investigate the effects of economic growth rate shock on the selected industries value in the Tehran Stock Exchange; For this purpose, in the present study, the effects of economic growth rate shock were investigated by applying the time-varying parameter factor-augmented vector autoregressive model (TVP-FAVAR) and using quarterly data during the period (2011-2018).The results show that the response of different industries value to economic growth rate shock has been different; Also, this response has been different for each industry over time, which highlights the need for a TVP approach. Also, by entering the economic growth rate impulse (as much as one standard deviation), it was found that the stock market response to this impulse is associated with a time lag and the effects of this variable appear after several periods on the selected industries value. As the results of the research show, even in the years when we have had a negative economic growth rate, the response of some industries' value has been completely opposite to the variable direction of economic growth rate; One of the reasons for this subject is the low quality of economic growth rate in the country.

The monetary policy impact on agricultural growth and food prices

Agricultural growth is closely associated with sustainable economic development. This is especially true from the perspective of developing countries, such as Indonesia and Pakistan, where significant portions of the labour force are dependent on agriculture for their livelihood. This paper aims to examine the effects of macroeconomic policy (i.e. monetary policy) on employment, food inflation, and agricultural growth by analysing to what extent monetary policy is effective in controlling food price inflation, the effect of contractionary monetary policy on the agricultural sector’s employment and productivity, and the extent of monetary policy transmission to money market rates and 10-year interest rates. We employed a factor-augmented vector autoregressive model proposed by Bernanke et al. (2005) to agricultural data from 1995 and 1996 to 2016 for Indonesia and Pakistan, respectively. Main findings of the study demonstrate that tight monetary policy significantly reduced food inflation and agricultural production while increasing the rural unemployment rate. Short-term and 10-year interest rates increased owing to the contractionary monetary policies pursued by both countries. An inclusive monetary policy whereby policymakers work alongside governments to achieve price stabilisation and reasonable employment rates is recommended.

Modelling mortality: A bayesian factor-augmented var (favar) approach

AbstractLongevity risk is putting more and more financial pressure on governments and pension plans worldwide due to pensioners’ increasing trend of life expectancy and the growing numbers of people reaching retirement age. Lee and Carter (1992, Journal of the American Statistical Association, 87(419), 659–671.) applied a one-factor dynamic factor model to forecast the trend of mortality improvement, and the model has since become the field’s workhorse. It is, however, well known that their model is subject to the limitation of overlooking cross-dependence between different age groups. We introduce Factor-Augmented Vector Autoregressive (FAVAR) models to the mortality modelling literature. The model, obtained by adding an unobserved factor process to a Vector Autoregressive (VAR) process, nests VAR and Lee–Carter models as special cases and inherits both frameworks’ advantages. A Bayesian estimation approach, adapted from the Minnesota prior, is proposed. The empirical application to the US and French mortality data demonstrates our proposed method’s efficacy in both in-sample and out-of-sample performance.

To be or not to be in the EU: the international economic effects of Brexit uncertainty

This paper evaluates the impact of Brexit-related uncertainty on the economies of the UK, EU, and the US. We propose a measure of Brexit uncertainty that has not been employed before in the literature. We first construct a binary variable by selecting Brexit-related events. We subsequently employ the Qual VAR model of Dueker [2005. “Dynamic Forecasts of Qualitative Variables: A Qual VAR Model of US Recessions.” Journal of Business & Economic Statistics 23: 96–104] to transform this variable to a continuous latent variable that captures uncertainty on important economic and financial variables. Next, this latent variable enters a structural Factor-Augmented Vector AutoRegression model combined with 452 macro and financial variables for the sample countries. Overall, our results indicate that the prolonged period of uncertainty, had a positive effect on the economies of major EU countries and negative effects for the UK economy. Additionally, the UK is the most important net sender of uncertainty spillovers in the EU, while Germany and France are among the most important net receivers of uncertainty shocks.

Policy Impact Analysis of Housing Policies Using Housing Cycles

In Korea, there is a big difference in the rise and fall of apartment prices by region, so it is expected that there will be differences in regional economic indexes. So, to capture the housing market conditions, we developed the housing cycle by using the factor-augmented vector autoregressive model (FAVAR). In addition, we estimated the impacts of the Korean government’s housing policies on the housing cycle. The first is a net policy effect model, and the second is a comprehensive model with macroeconomic variables and regional variables. To estimate the effects of the policies, we considered financial, tax, and housing transaction policies. In the net policy effect model, we found that all policies, including an increase in the DTI (debt to income) ratio, the LTV (loan to value) limit, an acquisition tax reduction, a transfer tax deregulation, deregulation of the housing subscription policy, and a housing purchase right transfer, impacted the housing cycles at the period t − 1. This is why the Korean government announced policy enforcement in advance a month ago before policy implementation. In the comprehensive model, we found that the policies had statistically significant effects on housing cycles at the period t − 1, and we also found that the regional variables had an influence on the housing cycle. Therefore, the Korean government should consider the regional characteristics and time lag when it attempts to intervene in the housing market.

Interdependent capital structure choices and the macroeconomy

This study shows that capital structure choices of US corporations are interdependent across time. We follow a two-step estimation approach. First, using a large cross-section of firms we estimate year-by-year average capital structure choices, i.e., the average firm’s percentage of new funding that is secured through debt, its term composition, and the percentage of new equity represented by retained earnings. Second, these time series are included in a Factor Augmented Vector Autoregressive model in which three factors representing real economic activity, expected future funding conditions, and prices, are included. We test for the interdependence between optimal capital structure decisions and for the influence exerted by macroeconomic conditions on these decisions. Results show there is a hierarchical order in which firms make capital structure decisions. They first decide on the share of debt out of total new funding they will hire. Conditional on this they decide on the term of their debt and on their earnings retention policy. Of outmost importance, macroeconomic factors are key for making capital structure decisions.

The impact of uncertainty shocks on state-level employment

Using U.S. data over the period 1960Q3–2019Q4 we estimate a structural factor-augmented vector autoregressive model and find that a one standard deviation shock to macroeconomic uncertainty generates declines in state-level employment growth that range from -0.02 to -0.12 percentage points at their peak negative response. Cross-sectional regressions show that industry mix is an important channel through which uncertainty shocks affect employment growth. In particular, a state with a larger manufacturing sector experiences a larger decline in employment growth.

Do energy efficiency improvements reduce energy use? Empirical evidence on the economy-wide rebound effect in Europe and the United States

Increasing energy efficiency is often considered to be one of the main ways of reducing greenhouse gas emissions. However, efficiency gains that reduce the cost of energy services result in energy use rebounding and potential energy use savings being eaten up. Empirical research that quantifies the economy-wide rebound effect while taking the dynamic economic responses to energy efficiency improvements into account is limited. We use a Structural Factor-Augmented Vector Autoregressive model (S-FAVAR) that allows us to track how energy use changes in response to an energy efficiency improvement while accounting for a vast range of potential confounders. We find economy-wide rebound effects of 78% to 101% after two years in France, Germany, Italy, the UK, and the US. This implies that energy efficiency innovations alone may be of limited help in reducing future energy use and emphasizes the importance of tackling carbon emissions directly.

Regional economic structure and heterogeneous effects of monetary policy: evidence from Indonesian provinces

We analyze how regional economic structures affect the impact of monetary policy on rates of inflation across 34 Indonesian provinces. The paper first applies structural factor augmented vector autoregressive model (SFAVAR) to all the 34 provinces based on monthly provincial data in order to measure the length and magnitude of responses of regional inflation to monetary policy shock, derived from the consequential impulse response functions of 34 provinces. In the second step, we analyze the impact of economic structures on the length and magnitude of regional inflationary responses of 34 provinces. We find that the impacts of monetary policy across regions are significantly influenced by economic structural variables such as manufacturing sector share to GDP, mining sector share to GDP, bank lending share to GDP and export share to GDP. In addition, we found the spatial lag, rate of inflation of neighboring provinces, is also statistically significant. In a similar fashion, economic structural variables such as manufacturing sector share to GDP, construction sector share to GDP and investment share to GDP are found statistically significant in explaining regional differences of monetary policy efficiency. Our findings imply economic structures of provinces have to be incorporated to designing monetary policy in Indonesia.

Financial cycle and the effect of monetary policy

Using a time-varying parameter vector vector autoregressive model with stochastic volatility (TVP-SV-VAR), this study evaluates the effect of quantitative and price-based monetary policies on China's financial cycle, which is represented by a dynamic financial condition index (FCI) constructed with a time-varying parameter factor-augmented vector autoregressive (TVP-FAVAR) model. Results reveal that quantitative monetary policy boosts the financial cycle slightly and swiftly, whereas price-based monetary policy has slower but stronger long-term negative effect to restrain boom and control financial risk. Moreover, the effects of both monetary policies are compromised in periods of strong financial fluctuation.

THE EFFECTS OF CHINESE POLICY UNCERTAINTY ON THE GLOBAL ECONOMIC OUTPUT AND TRADE MARKETS

This paper examines the international effects of Chinese policy uncertainty shock. Using the factor-augmented vector autoregression (FAVAR) model which can accommodate several economic variables from multiple countries, we model Chinese policy uncertainty jointly with three latent factors extracted from industrial production index, import value and export value. The results reveal that the change of one standard deviation of Chinese economic policy uncertainty (EPU) has a negative interference effect on global trade markets and industrial production, and so does the fiscal policy uncertainty (FPU). However, the impact of Chinese trade policy and monetary policy on three latent factors is not statistically significant. A country-level analysis shows that due to the heterogeneity of the economic structure and trade structure of each economy, countries have different responses to the uncertainty of Chinese economic policy. In particular, the extent to which imports and exports of each economy are affected is asymmetric.