Hot money and quantitative easing: the spillover effect of U.S. monetary policy on Chinese housing, equity and loan markets
We study a factor-augmented vector autoregression model to estimate the effects of changes in U.S. monetary policy, as well as changes in U.S. policy uncertainty, on the Chinese economy. We find that since the Great Recession, a decline in the U.S. policy rate would result in a significant increase in Chinese regulated interest rates, and rise in Chinese housing investment. One possible reason for this is the substantial inflow of hot money into China. Responses of Chinese variables to U.S. shocks at the zero lower bound are different from that in normal times, which suggest structural changes in both the Chinese economy and the U.S. monetary policy transmission mechanism. Moreover, an increase in U.S. policy uncertainty negatively impacts Chinese stock and real estate market during normal times, but not at the zero lower bound.
AUTHORS: Ho, Steven Wei; Zhang, Ji; Zhou, Hao
The Double-Edged Sword of Global Integration: Robustness, Fragility & Contagion in the International Firm Network
We estimate global inter-firm networks across all major industries from 1981 through 2016 and provide the first empirical tests for both robust (beneficial) and fragile (harmful) network behavior, relating firms' health with global integration. More connected firms are less likely to be in distress and have higher profit growth and equity returns, but are also more exposed to direct contagion from distressed neighboring firms and network level crises. Our analysis reveals the centrality of finance in the international firm network and increased globalization, with greater potential for crises to spread globally when they do occur.
AUTHORS: Yung, Julieta; Grant, Everett
Minimum Distance Estimation of Dynamic Models with Errors-In-Variables
Empirical analysis often involves using inexact measures of desired predictors. The bias created by the correlation between the problematic regressors and the error term motivates the need for instrumental variables estimation. This paper considers a class of estimators that can be used when external instruments may not be available or are weak. The idea is to exploit the relation between the parameters of the model and the least squares biases. In cases when this mapping is not analytically tractable, a special algorithm is designed to simulate the latent predictors without completely specifying the processes that induce the biases. The estimators perform well in simulations of the autoregressive distributed lag model and the dynamic panel model. The methodology is used to re-examine the Phillips curve, in which the real activity gap is latent.
AUTHORS: Gospodinov, Nikolay; Komunjer, Ivana; Ng, Serena
Impacts of Monetary Stimulus on Credit Allocation and Macroeconomy: Evidence from China
We develop a new empirical framework to identify and estimate the effects of monetary stimulus on the real economy. The framework is applied to the Chinese economy when monetary policy in normal times was switched to an extraordinarily expansionary regime to combat the impact of the 2008 financial crisis. We show that this unprecedented monetary stimulus accounted for as high as a 4 percent increase of real gross domestic product (GDP) growth rate by the end of 2009. Monetary transmission to the real economy was through bank credit allocated disproportionately to financing investment in real estate and heavy industries. Such an asymmetric credit allocation resulted in the persistently high investment rate and debt-to-GDP ratio. Our findings provide a broad perspective on a tradeoff between short-run GDP growth and longer-run accumulated debt in response to large monetary interventions.
AUTHORS: Chen, Kaiji; Waggoner, Daniel F.; Higgins, Patrick C.; Zha, Tao
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.
AUTHORS: Swanson, Eric T.; Spiegel, Mark M.; Fernald, John G.
Foreign Effects of Higher U.S. Interest Rates
This paper analyzes the spillovers of higher U.S. interest rates on economic activity in a large panel of 50 advanced and emerging economies. We allow the response of GDP in each country to vary according to its exchange rate regime, trade openness, and a vulnerability index that includes current account, foreign reserves, inflation, and external debt. We document large heterogeneity in the response of advanced and emerging economies to U.S. interest rate surprises. In response to a U.S. monetary tightening, GDP in foreign economies drops about as much as it does in the United States, with a larger decline in emerging economies than in advanced economies. In advanced economies, trade openness with the United States and the exchange rate regime account for a large portion of the contraction in activity. In emerging economies, the responses do not depend on the exchange rate regime or trade openness, but are larger when vulnerability is high.
AUTHORS: Iacoviello, Matteo; Navarro, Gaston
Monetary Policy, Real Activity, and Credit Spreads : Evidence from Bayesian Proxy SVARs
This paper studies the interaction between monetary policy, financial markets, and the real economy. We develop a Bayesian framework to estimate proxy structural vector autoregressions (SVARs) in which monetary policy shocks are identified by exploiting the information contained in high frequency data. For the Great Moderation period, we find that monetary policy shocks are key drivers of fluctuations in industrial output and corporate credit spreads, explaining about 20 percent of the volatility of these variables. Central to this result is a systematic component of monetary policy characterized by a direct and economically significant reaction to changes in credit spreads. We show that the failure to account for this endogenous reaction induces an attenuation bias in the response of all variables to monetary shocks.
AUTHORS: Caldara, Dario; Herbst, Edward
Financial stress regimes and the macroeconomy
Some financial stress events lead to macroeconomic downturns, while others appear to be isolated to financial markets. We identify financial stress regimes using a model that explicitly links financial variables to macroeconomic outcomes. The stress regimes are identified using an unbalanced panel of financial variables with an embedded method for variable selection. Our identified stress regimes are associated with corporate credit tightening and with NBER recessions. An exogenous deterioration in our financial condition index has strong negative effects in economic activity, and negative amplification effects on inflation in the stress regime. We employ a novel factor-augmented vector autoregressive model with smooth regime changes (FAST-VAR).
AUTHORS: Galvão, Ana B.; Owyang, Michael T.
Specification and Estimation of Bayesian Dynamic Factor Models: A Monte Carlo Analysis with an Application to Global House Price Comovement
We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model performance for different specifications of factor models across three different estimation procedures. We consider three general factor model specifications used in applied work. The first is a single- factor model, the second a two-level factor model, and the third a three-level factor model. Our estimation procedures are the Bayesian approach of Otrok and Whiteman (1998), the Bayesian state space approach of Kim and Nelson (1998) and a frequentist principal components approach. The latter serves as a benchmark to measure any potential gains from the more computationally intensive Bayesian procedures. We then apply the three methods to a novel new dataset on house prices in advanced and emerging markets from Cesa-Bianchi, Cespedes, and Rebucci (2015) and interpret the empirical results in light of the Monte Carlo results.
AUTHORS: Kose, M. Ayhan; Owyang, Michael T.; Jackson, Laura E.; Otrok, Christopher
A New Tool for Robust Estimation and Identification of Unusual Data Points
Most consistent estimators are what Müller (2007) terms “highly fragile”: prone to total breakdown in the presence of a handful of unusual data points. This compromises inference. Robust estimation is a (seldom-used) solution, but commonly used methods have drawbacks. In this paper, building on methods that are relatively unknown in economics, we provide a new tool for robust estimates of mean and covariance, useful both for robust estimation and for detection of unusual data points. It is relatively fast and useful for large data sets. Our performance testing indicates that our baseline method performs on par with, or better than, two of the currently best available methods, and that it works well on benchmark data sets. We also demonstrate that the issues we discuss are not merely hypothetical, by re-examining a prominent economic study and demonstrating its central results are driven by a set of unusual points.
AUTHORS: Garciga, Christian; Verbrugge, Randal