Factor-augmented Vector Autoregression Approach Research Articles

Term structures and firm dynamics: A FAVAR approach

This study employs a Factor-Augmented Vector Auto-Regression approach with Chinese bond markets and aggregated firm-level data to investigate the impact of term structure shocks on firm dynamics and aggregate firm-level variables. We address the potential endogenous issue using a novel instrumental variable based on textual analysis, and find that term structure shocks have long-run and persistent influences on several firm-level outcomes, and that firm dynamics play a critical role in the transmission of term structure shocks.

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

Оценка влияния глобальных шоков на российскую экономику в рамках факторной модели

In this study, we estimate the contribution of global demand, supply and commodity shocks to the dynamics of Russian macroeconomic variables. The main tool used in this work is a factor augmented vector autoregression (FAVAR) that allows extracting global factors from a wide range of variables. Recursive and sign restrictions are used to identify global shocks. Russian economy is represented by a large set of informational series aggregated into a small number of factors. FAVAR approach allows for extended inference on the reaction of Russian macroeconomic variables to global shocks. Impulse response function analysis shows that Russian economy is affected by all three specifi ed global shocks and forecast error decompositions indicate that those shocks account for nearly 80 per cent of key variables dynamics. We also showed that global demand and global commodity shocks were more crucial compared to the third type of shocks in explaining macroeconomic dynamics.

Output, Employment, and Price Effects of U.S. Narrative Tax Changes: A Factor-Augmented Vector Autoregression Approach

This paper examines the short- and long-run effects of U.S. federal personal income and corporate income tax cuts on a wide array of economic policy variables in a data-rich environment. Using a panel of U.S. macroeconomic data set, made up of 132 quarterly macroeconomic series for 1959-2018, the study estimates factor-augmented vector autoregression (FAVARs) models where an extended narrative tax changes dataset combined with unobserved factors. The narrative approach classifies if tax changes are exogenous or endogenous. This paper identifies narrative tax shocks in the vector autoregression model using the sign restrictions with Uhlig's (2005) penalty function. Empirical findings show a significant expansionary effect of tax cuts on the macroeconomic variables. Cuts in personal and corporate income taxes cause a rise in output, investment, employment, and consumption; however, cuts in personal taxes appear to be a more effective fiscal policy tool than the cut in corporate income taxes. Real GDP, employment, investment, and industrial production increase significantly and reach their maximum response values two years after personal income tax cuts. The effects of corporate tax cuts have relatively smaller effects on output and consumption but show immediate and higher effects on fixed investment and price levels.

The impacts of oil price shocks in Turkey: sectoral evidence from the FAVAR approach

This paper investigates the effects of crude oil price shocks on the Turkish economy from 2005:01 to 2018:04 using a relatively new technique: the factor-augmented vector autoregressive (FAVAR) approach. The findings indicate the importance of crude oil prices to inflation, sectoral growth, and monetary policy. The main results of the impulse response analyses are as follows: (1) Oil price shocks did not explain changes in industrial production growth or its subsectors; (2) the responses of different price indices to positive oil price shocks are statistically significant and persistent. The largest number of price increases occurs in the transportation and food and beverage sectors; (3) monetary policy does not respond to oil price shocks. One can claim that the interest rate does not respond to oil price and allow the prices to adjust. Afterward, the price adjustment neutralizes the production effects of the oil price shocks.

Testing for the Effectiveness of Inflation Targeting in India: A Factor Augmented Vector Autoregression (FAVAR) Approach

Abstract Employing Factor Augmented Vector Autoregression (FAVAR) model where factors are obtained using the principal component analysis (PCA) and the parameters of the model are estimated using Vector Autoregression framework, we analyse how changes in monetary policy variables impact inflation, output, money supply, and the financial sector in India. Our results for the period 2001:04 to 2016:03 show that the benchmark FAVAR model showed more reliable results than baseline VAR model. Benchmark FAVAR model shows the existence of weak ‘liquidity puzzle’ in India. The impulse responses from the FAVAR approach reveal that monetary policy is more efficient in explaining the variations in inflation rather than stimulating output indicating its effectiveness in attaining the objective of price stability.

A FAVAR modeling approach to credit risk stress testing and its application to the Hong Kong banking industry

In October 2018, the Basel Committee on Banking Supervision (BCBS) published its stress testing principles. One of these principles is about stress testing model validation, aided by business interpretation, benchmark comparison and backtesting. In this paper, a credit risk stress testing model based on the factor-augmented vector autoregressive (FAVAR) approach is proposed to project credit risk loss under stressed scenarios. Inherited from both factor analysis (FA) and the vector autoregressive (VAR) model, the FAVAR approach ensures that the proposed model has many appealing features. First, a large number of model input variables can be reduced to a handful of latent common factors to avoid the curse of dimensionality. Second, the dynamic interrelationship among macroeconomic variables and credit risk loss measures can be studied without exogeneity assumptions. Moreover, the application of the impulse response function facilitates the multiperiod projection of credit risk loss in response to macroeconomic shocks. All of these features make the proposed modeling framework a potentially handy solution to fulfilling the BCBS requirement of quantitative adequacy assessment of banks’ internal stress testing results with a benchmark model. The scope of its application can also extend to impairment modeling for International Financial Reporting Standard 9, which requires the projection of credit risk losses over consecutive periods under different macroeconomic scenarios.

The Role of a Latent Value-Relevant Measure in Tracking and Predicting Stock Returns: A FAVAR Approach

This paper attempts to estimate and study the role of 'other information', as posited in the residual income valuation model of Ohlson (1995), for tracking and predicting future returns of the S&P 500. 'Other information' is an unobserved variable and defined as a summary of value-relevant information about events and their effect on future profitability, which is captured in a company's current stock price and returns, but not yet reflected in a company's current financial statements. This suggests a potential to predict subsequent returns. Previous literature has found that traditional valuation metrics (e.g. B/P, E/P, and D/P ratios) have poor predictive power. In this study, we apply a factor augmented vector autoregression (FAVAR) to estimate this value-relevant latent variable and assess its predictive performance. The FAVAR is a suitable model because it enables us to analyze and quantify the linkages of stock market value, profitability, and unobserved factors that are broadly captured by big data. We use a two-step principal components estimation approach to extract the unobserved factors of 78 informational variables from financial market, accounting, investor and consumer sentiment, and macroeconomic data. Our analysis shows that, in comparison to competing measures, the estimated latent value-relevant variable can track contemporaneous stock returns and has statistically reliable power to predict both future real stock returns and excess returns over a Treasury Bill rate, both in- and out-of-sample.

A Factor-Augmented Vector Autoregressive Approach to Analyze the Transmission of Monetary Policy

Using the factor-augmented vector autoregressive (FAVAR) model proposed by Bernanke et al. (2005), this study explores the effect of monetary policy on a wide range of macroeconomic and financial variables for the US, Canada and the UK. The study makes use of financial data from 1990 to 2016, comprising 55-70 variables of the three major nations to show (1) that factors come with additional informational capability, which summarizes the performance of key macroeconomic variables and (2) the manner in which these variables are affected by contractionary monetary policies. Our findings confirm that monetary policy tightening results in decrease in industrial production, employment, share prices, housing starts and inflation; however, it leads to increase in the three-month treasury bill rate, long-term interest rates and unemployment. Overall, the impact of standardized monetary tightening is similar across the countries studied. These results from the major economies and the inclusion of larger data sets containing more variables would be relevant for policy theorists and practitioners from other countries.

Energy price shocks and dynamics of current account in Turkey: a FAVAR Approach

Purpose- In this article, we examine the linkages between current account and energy prices, namely crude oil, coal and natural gas in Turkey the years between 2005 and 2016. Methodology- This article investigates how the changes in the energy prices affect the current account in Turkey. For this purpose, a Factor-Augmented Vector Auto Regression (FAVAR) model is used with economic data from the Turkish economy and world commodity price indexes to obtain empirical results for current account dynamics in the Turkish economy. The impact of various factors that include crude oil prices, coal prices, natural gas prices and fuel (Energy) index (Crude oil, natural gas, and coal price indices) data have been studied by obtaining impulse response functions. Findings- Natural gas prices gradually affect the current account. The impact is permanent after a period indicating a very limited elasticity. Oil prices have an immediate The current on the current account. Cycles, in the long run, indicate some degree of elasticity. The negative effect of coal price on the current account deficit occurs with a significant delay. Crude oil price index shocks graphs and fuel index, which includes oil price, coal price and natural gas, and crude oil price shocks graphs are very similar. Conclusion- In this comprehensive study, we can conclude that crude oil prices, coal prices, natural gas prices deteriorate current account balance. From all of the energy indices, the crude oil price is the most effective on the current account.

Impact of climate change on food yield in Senegal: FAVAR approach

The main objective of this research is to evaluate the impact of climate change on food crop yields in Senegal using the Factor Augmented Vector Auto Regression (FAVAR) approach. The estimation method used is principal components analysis. We identified two major shocks representative of climate change. The first is an increase of temperature (thermal shock) and the second is a decrease in the quantity of precipitation (rainfall shock). The data covers the period 1970-2014 and each of the shocks is carried out over the prior year. The impact of each shock is observed along a time horizon of 10 years. The results show a positive impact of the thermal shock on the yields of rice, maize and millet, with a much greater impact on rice and maize yield. Rising temperatures are, however, detrimental to sorghum. A decline in rainfall has a negative impact on the yields of all cereals, which is in line with expectations.

Dynamic response of bank-intermediated trade finance to a global liquidity shock

ABSTRACTThis paper investigates the bank-level responses of a bank-intermediated instrument of trade finance to a negative global liquidity shock in Korea. Using a factor-augmented vector autoregression approach, the results show that there exists significant heterogeneity in bank-level trade-finance responses to a global liquidity shock. In addition, we find that the source of the heterogeneity is the bank-level foreign currency liquidity condition; banks with a better foreign currency liquidity condition may dampen the negative impact of a global liquidity shock on trade-finance.

Measuring the effects of dollar appreciation on Asia: A FAVAR approach

Exchange rate shocks have mixed effects on economic activity in both theory and empirical VAR models. In this paper, we extend the empirical literature by considering the implications of a positive shock to the U.S. dollar in a factor-augmented vector autoregression (FAVAR) model for the U.S. and three large Asian economies: Korea, Japan and China. The FAVAR framework allows us to represent a country’s aggregate economic activity by a latent factor, generated from a broad set of underlying observable economic indicators. To control for global conditions, we also include in the FAVAR a “global conditions index,” which is another latent factor generated from the economic indicators of major trading partners. We find that a dollar appreciation shock reduces economic activity and inflation not only for the U.S. economy, but also for all three Asian economies. This result, which is robust to a number of alternative specifications, suggests that in spite of their disparate economic structures and policy regimes, the dollar appreciation shock affects the Asian economies primarily through its impact on U.S. aggregate demand; and this demand channel dominates the expenditure-switching channel that affects a country’s export competitiveness.

Effects of Stock Indices of Developed and Emerging Markets on Economic Activity in Colombia: a FAVAR Approach

Analizamos el efecto de las condiciones financieras de economias emergentes y desarrolladas sobre el comportamiento de la economia colombiana. Para ello, estimamos indices de condiciones financieras para mercados accionarios de paises desarrollados y emergentes usando analisis de componentes principales. Posteriormente, estos indices se incluyeron como regresores en un modelo tradicional de vectores auto-regresivos (VAR); es decir, estimamos un modelo de vectores auto-regresivos aumentado con factores (FAVAR) que incluye un factor no observable y una variable observable de la actividad economica real con el fin de evaluar el efecto de las condiciones financieras internacionales sobre las variables macroeconomicas colombianas. De esta manera, encontramos que las condiciones accionarias de los paises emergentes tienen un efecto positivo sobre el comportamiento macroeconomico de Colombia cuando se lleva a cabo el analisis con datos mensuales.

Measuring the Channels of Monetary Policy Transmission: A Factor-Augmented Vector Autoregressive (Favar) Approach

Abstract There is more consensus on the effects of monetary policy than its transmission mechanism. Two channels of transmission mechanisms are the conventional interest rate channel and the credit channel. I investigate the channels of monetary policy transmission in the U.S. using the factor-augmented vector autoregressive (FAVAR) models developed by Bernanke, Boivin & Eliasz (2005). The newly developed FAVAR approach allows the researcher to include all relevant macroeconomic variables in the model and analyze them. Therefore, the FAVAR models span a larger information set and generate better estimates of impulse response functions than the commonly used vector autoregressive (VAR) models that utilize only 4–8 variables. I include 154 monthly U.S. time series variables for the period 1970–2014. The findings support the existence of the credit channel in the U.S. The conclusion remains the same when the non-borrowed reserve operating regime (October 1979–October 1982) is removed from the sample period.

Examining industrial interdependence between Japan and South Korea: A FAVAR approach

This paper investigates the economic relationship between Japan and South Korea by incorporating disaggregated output measures. Using a factor-augmented vector autoregression (FAVAR) model, we conduct several experiments to test the nature of the interdependence, both in the aggregate and by sector. We find that South Korean output shocks affect the Japanese economy in a significant manner, whereas Japanese output shocks have a limited effect on South Korea. By further examining the transmission mechanism of sectoral output shocks and comparing them with the direction of sectoral trade, we find evidence of cross-border production sharing, which explains the asymmetric results seen in the aggregate output.

Are the macroeconomic effects of oil price shock symmetric?: A Factor-Augmented Vector Autoregressive approach

This paper aims to examine the asymmetric effect of oil price shocks on real economic activity in the U.S. within the context of a nonlinear Factor-Augmented Vector Autoregressive (FAVAR) model. By employing simulation methods, we trace the effects of positive and negative oil price shocks on the macroeconomic variables through the Impulse Response Function (IRF). It is found that the negative impacts of higher oil prices are larger than the positive effects of lower oil prices. And the asymmetric effects are more evident when the oil price shocks are larger. The results are robust to different lag specification and choice of factors.

Common dynamics of nonenergy commodity prices and their relation to uncertainty

The purpose of this article is to improve the empirical evidence on commodity prices in various dimensions. First, we attempt to identify the extent of comovements in 44 monthly nonenergy commodity price series in order to ascertain whether the increase in comovement is a recent term phenomenon. Second, we attempt to determine the role of uncertainty in determining comovements among nonenergy prices in the short run. We diagnose the overall comovement using a dynamic factor model estimated by principal components. A factor-augmented vector autoregressive approach is used to assess the relationship of fundamentals, financial and uncertainty variables with the comovement in commodity prices. We find a greater synchronization among raw materials since December 2003. Since that date, uncertainty has played an important role in determining short-run fluctuations in nonenergy raw material prices.

Monetary policy effectiveness in China: Evidence from a FAVAR model

We use a broad set of Chinese economic indicators and a dynamic factor model framework to estimate Chinese economic activity and inflation as latent variables. We incorporate these latent variables into a factor-augmented vector autoregression (FAVAR) to estimate the effects of Chinese monetary policy on the Chinese economy. A FAVAR approach is particularly well-suited to this analysis due to concerns about Chinese data quality, a lack of a long history for many series, and the rapid institutional and structural changes that China has undergone. We find that increases in bank reserve requirements reduce economic activity and inflation, consistent with previous studies. In contrast to much of the literature, however, we find that central-bank determined changes in Chinese interest rates also have substantial impacts on economic activity and inflation, while other measures of changes in credit conditions, such as shocks to M2 or lending levels, do not once other policy variables are taken into account. Overall, our results indicate that the monetary policy transmission channels in China have moved closer to those of Western market economies.

Monetary Policy Effectiveness in China: Evidence from a FAVAR Model

We use a broad set of Chinese economic indicators and a dynamic factor model framework to estimate Chinese economic activity and inflation as latent variables. We incorporate these latent variables into a factor-augmented vector autoregression (FAVAR) to estimate the effects of Chinese monetary policy on the Chinese economy. A FAVAR approach is particularly well-suited to this analysis due to concerns about Chinese data quality, a lack of a long history for many series, and the rapid institutional and structural changes that China has undergone. We find that increases in bank reserve requirements reduce economic activity and inflation, consistent with previous studies. In contrast to much of the literature, however, we find that changes in Chinese interest rates also have substantial impacts on economic activity and inflation, while other measures of changes in credit conditions, such as shocks to M2 or lending levels, do not once other policy variables are taken into account. Overall, our results indicate that the monetary policy transmission channels in China have moved closer to those of Western market economies.