Shocks of Federal Reserve's monetary policy on ...

China Industrial Economics No. 01, 25-42, 2017

Citation: JIN Chunyu, ZHANG Long. Shocks of Federal Reserve’s monetary policy on China’s economy, China Industrial Economics, 2017 (01): 25-42.

Shocks of Federal Reserve’s monetary policy on China’s economy JIN Chunyu1,2, ZHANG Long2 1

Center for Quantitative Economics, Jilin University, Changchun 130012, , China; School of Business, Jilin University, Changchun 130012, , China

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Abstract In recent years, the Federal Reserve has frequently switched between the conventional monetary policy and unconventional monetary policy, such heterogeneity have made a dynamic effect on China’s economy, which caused that the spillover effect captured by constant parameter model lacked practical economic meaning. In this paper, we construct a factor-augmented vector autoregressive model with time-varying coefficients and stochastic volatility to study the phenomenon and the mechanism of the dynamic effect from the perspectives of macro-economy, private economy and finance market. It is found that the phenomenon of the dynamic effect lies in two aspects. One aspect is that both the tight monetary policy and quantitative easing monetary policy of Federal Reserve make a negative effect on China’s economy in different periods. This means that the same monetary policy will make different effects on China’s economy in different periods. The other aspect is that there is a decreasing shock of Federal Reserve’s monetary policy on Chinese macro-economy, an increasing shock on Chinese financial market, and a relatively smooth effect on Chinese private economy. The source of the dynamic effect lies in the difference of transmission mechanism. Under the fixed exchange rate regime, the Federal Reserve’s monetary policy affects the capital and financial accounts by affecting the interest margin, leading to the effects on Chinese economy. But under the floating exchange rate regime, the Federal Reserve’s monetary policy affects the current account by affecting the exchange rate, leading to the effects on Chinese Received: 2016-10-11 About the authors: JIN Chunyu (1965– ), Female, from Lishu, Jilin Province, Professor, Doctoral supervisor at Center for Quantitative Economics, Jilin University；ZHANG Long, from Changchun, Jilin Province, Doctoral candidate at the School of Business of Jilin University. E-mail: [email protected] Translated by LIN Congtie; edited by WANG Liting Supported by Research Program of Soft Science of Science and Technology Development Plan in Jilin Province (20130420035FG )

©2017 China Academic Journals (CD Edition) Electronic Publishing House Co., Ltd.

China Industrial Economics 2017: (1)

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economy. We suggest that the People’s Bank of China should reduce the floating space of CNY exchange rate, make a quantitative easing monetary policy to deal with the Federal Reserve’s tight monetary policy, make quantitative easing fiscal policy and tight monetary policy, and reduce the weight of USD in currency basket to deal with Federal Reserve’s quantitative easing monetary policy. Keywords Federal Reserve, monetary policy, China’s economy

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Introduction

In recent years, as the acceleration of its opening up process and the deepening integration of the global economy, China’s economy has become more susceptible to global economic situation and other countries’ macroeconomic policies. At the same time, Kim (2001) believed that developed countries’ macroeconomic policies have demonstrated significant spillover effect on other countries (or regions), especially on developing countries. Moreover, the United States, as the largest economy in the world, has decisive role in global economy. Therefore, its monetary policy has more remarkable spillover effect on other countries. Especially, in the late stage of its subprime mortgage crisis, the US Federal Reserve (short as Fed) implemented four rounds of quantitative easing (short as QE) policies, which brought about serious impact on other countries, attacked their manufacturing industries, shocked their financial markets, and caused fluctuation in their macro economy, (Lin Yueqin, 2009). Therefore, China’s economy will inevitably be affected by Fed’s monetary policy. However, Fed frequently has switched between conventional and unconventional monetary policies in recent years, which makes its policies demonstrate remarkable heterogeneity. The heterogeneity, in turn, further pushes the Fed’s monetary policies to exert dynamic influence on China’s economy. Thus, the past conclusion that Fed’ monetary policy has a long-term and steady influence on China’s economy, which is based on the constant parameter model, lacks practical economic significance. Hence, it is necessary to build a model capable of capturing Fed monetary policy’s dynamic shock to analyze Fed monetary policy’s dynamic influence on China’ economy and explore the transmission mechanism of the dynamic

JIN Chunyu: Shocks of Federal Reserve’s monetary policy on China’s economy

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influence. Thus, related departments can design pertinent measures to scientifically and reasonably deal with different monetary policies adopted by the Fed in different economic situations. Over the past 50 years, the spillover effect of monetary policy in the international level has attracted extensive attention from macro economists. Ahmed and Yoo (1989), Canova and Marrinan (1989), Prasad (1999), Denicol and Acute (2000), Canova and Nicolo (2003) and Kim (2001) found that international monetary policies have different transmission mechanisms in different external environments, and thus produce different effects, which is also an important factor that triggers regional and global economic fluctuations. Firstly, taking commodity market, monetary market and foreign exchange market as research objects, Fleming (1962) and Mundell (1963) used simultaneous equations model to analyze the monetary policy’s spillover effect in international level. Afterwards, Obstfeld and Rogoff (1995), Cushman and Zha (1997), Kim and Roubini (2000), Canova (2005) and others respectively studied General Equilibrium Model, Vector Autoregression Model (VAR Model), Structural Vector Autoregression Model (SVAR Model) and Bayesian Structure Vector Autoregression Model (Bayesian SVAR Model), respectively. They used these models to study Fed monetary policy’s spillover effect on other countries and came up with different results. Meanwhile, domestic scholars like Xiao Yu (2011), Ding Zhiguo et al. (2012), Sheng Xia (2013) and others respectively used the above models to study Fed monetary policy’s spillover effect on Asia and China. They all found that Fed monetary policy has strong impact on China’s macro economy and financial market, and that such impact is transmitted mainly through interest rate and exchange rate. However, while reviewing the above mentioned studies, we find that the methods adopted by scholar from various countries, including Mundell-Fleming-Dornbusch Model, General Equilibrium Model, VAR Model, SVAR Model, Bayesian SVAR Model or Factor Augmented VAR Model (FAVAR Model), are all static models, which are incapable of capturing the time-varying shock of monetary policy. This fact is also the main reason why the above mentioned studies did not get the same conclusion.

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Later, Kydland and Prescott (1982) put forward Dynamic Stochastic General Equilibrium Model (DSGE Model), which has been put into wide use in the studies on monetary policies by various countries afterwards. Blanchard and Galí (2010) and Liboshi (2015) respectively used traditional DSGE Model and Markov Switching DSGE Model to study central bank’s optimal monetary policy. They both found that DSGE Model can not take impact of exogenous monetary policies into consideration. Hence, Cooke (2015) and Gong et al. (2016) used the DSGE Model that takes two countries into consideration to help to optimize their countries’ monetary policies. During construction of the model, they both considered the impact of other countries’ monetary policies. However, the setting of DSGE Model is so strict that it does not respect the objectivity of the operation of economic system. Moreover, traditional DSGE Model can only be used to study the impact of monetary policy within a country, but cannot be used to study the spillover effect of monetary policy in international level. As for small open economy’s DSGE Model, it cannot study the spillover effect of monetary policy between major economies. Thus, after Sims (1980, 1999), Bernake and Mihov (1998a, 1998b) rejected the original hypothesis that coefficients in the VAR model does not vary with time, Sims and Zha (2001) , Cogley and Sargent (2005), Giovanni and Shambaugh (2007) and others built time-varying parameter VAR models respectively to study the transmission of monetary policy in the international level. They all identified that the fluctuation of exchange rate is the main reason for foreign monetary policy to affect domestic economy, and that it has different effects in different periods. Nevertheless, time-varying parameter VAR models usually cannot deal with more than 5 economic variables due to low identifiability. Insufficient economic variables will easily cause the lack of important information of the economic system. By reviewing the above studies, we find that although macro economists have made in-depth study on the shock of monetary policy, there are three questions remaining to be solved. ①The current models are unable to capture the dynamic impact of monetary policy when studying the transmission of monetary policy impact in international level,


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including Mundell-Fleming-Dornbusch Model, GE Model, VAR Model, SVAR Model, Bayesian SVAR model or FAVAR Model. ② Although DSGE Model and Time-varying Parameter VAR Model (TVP-VAR Model) can be used to examine time-varying issues, they can use only limited variables and thus lack certain important economic system information when studying the shock of monetary policy. Moreover, general DSGE model cannot study the monetary policy’s spillover effect in international level, and small open economy’s DSGE Model cannot study the spillover effect of monetary policy between major economies. ③ The previous studies mostly target on the spillover effect of monetary policy among industrialized countries or the impact of domestic monetary policy. Few studies focus on the impact of Fed monetary policy on China’s economy, let alone the studies on the Fed monetary policy’s dynamic influence on China’s economy. Aiming at solving the above three problems, this study introduces dynamic factor model into VAR model. First, the study extracts a few common factors from many macroeconomic variables and uses these factors and observable variables to form FAVAR Model. Further, the study inherits the idea of Sims (1980, 1999), and makes coefficient matrix become time-varying to form a TVP-FAVAR Model. Meanwhile, this study also makes covariance matrix of disturbance term become time-varying, so as to form SV-TVP-FAVAR Model with time-varying parameters with stochastic volatility). Next, the study collects data from 1997 Q1 to 2015 Q3 and carries out studies from the perspectives of macro economy, private economy and financial market to analyze the impact of Fed momentary policy in typical situation and the consecutive impacts’ dynamic influence on China’s economy. Thus, the study captures the manifestation of the above dynamic influence and its mechanism. In the end, the study discusses China’s orientation of macroeconomic policy when facing different monetary policies adopted by the Fed. The innovation of this study embodies in three aspects. ① Since constant parameter model is unable to capture the dynamic influence on China’s economy produced by Fed monetary policy, this study introduces dynamic factor model into the typical VAR model, which makes up for the


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lack of important economic system information. Moreover, the study also makes model coefficient matrix and covariance matrix of disturbance term become time-varying, to capture the dynamic influence between variables. ②This study uses the mathematica’s multidimensional quadrature to carry out three-dimensional impulse response analysis, and further analyzes the monotonicity of Fed monetary policy’s impact in terms of time dimension. Combining the monotonicity of time dimension and directivity of response dimension, the study analyzes the manifestation of the above mentioned dynamic influence.① To sum up, the three-dimensional impulse response is an innovative application in both domestic and foreign studies. Unlike time impulse response, it can observe the monotonicity and extreme point of the impact in time dimension. ③The study analyzes the Fed monetary policy’s dynamic influence on China’s economy from the perspectives of macro economy, private economy and financial market, supplementing the deficiency of past studies that only focus on macro economy.

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Construction of the model

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Model setting

SV-TVP-FAVAR Model is developed from the classic VAR Model by introducing the ideal of dynamic factor expansion and extracting a few common factors from many economic variables, which effectively makes up for the lack of important economic system information. Next, the study makes coefficient matrix and covariance matrix of disturbance term become time-varying, so as to capture the dynamic impact of monetary policies. Thus, a basic VAR model is built to study the Fed monetary policy’s dynamic impact on China’s economy: yt′ = [zt′ , mt ]. zt is a dimensional variable vector (l ×1) that includes China’s GDP, consumer price index, interest rate and employment rate. mt is the proxy variable of Fed monetary policy tools. This study uses the ①

The time dimension in this study refers to the time when the impact of Fed monetary policy took place, not the duration of the influence on the observable variables.


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quarterly M2 of the United States as the proxy variable. The coefficient biof each lagged variable yt is (l + 1) ×(l + 1) dimension matrix, i = 1, . . ., p; vt~N (0 Ω), Ω is the (l + 1) × (l + 1) dimension covariance matrix. In a typical VAR model, the dimension of yt′ variable (l + 1) normally does not exceed 20. In most cases, it is very small and stays below 3. However, monetary policy study needs to consider hundreds of variables. The traditional VAR model is insufficient facing high dimension. Therefore, this study adopts a very popular practice that degrades the n dimension observable variable xt to k dimension non-observable common factor ft, and k