Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound

Wu, Jing Cynthia; Xia, Fan Dora

doi:10.3386/w20117

Cited by 179 publications

(287 citation statements)

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“…From an econometric standpoint, however, it is less clear how to empirically model the stance of monetary policy over our full 1986-2014 sample period. In our baseline VAR results, we use the Wu and Xia (2016) shadow rate as our indicator of monetary policy. In Appendix A.2, we also show that our key stylized fact remains unchanged if we use alternative measures for the stance of monetary policy in the VAR.…”

Section: Empirical Evidencementioning

confidence: 99%

Uncertainty Shocks in a Model of Effective Demand

2017

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THIS SECTION PROVIDES additional details on the data construction and estimation procedure for the empirical evidence from Section 2 of the main text. We estimate our baseline VAR using data on the VXO, GDP, consumption, investment, hours worked, the GDP deflator, the M2 money stock, and the Wu and Xia (2016) shadow rate. To match the concept in the model, we measure consumption in the data as the sum of non-durable and services consumption. Then, we use the sum of consumer durables and private fixed investment as a measure of investment in our baseline empirical model. To match the quarterly frequency of the macroeconomic data, we average a weekly VXO series for each quarter. Thus, our measure of uncertainty captures the average implied stock market volatility within a quarter. We convert output, consumption, investment, and hours worked to percapita terms by dividing by population. Except for the shadow rate, all other variables enter the VAR in log levels.We include four lags in the estimation of the VAR and generate our confidence intervals using the Bayesian method outlined in Sims and Zha (1999).2 Figure A.1 plots the VXO over time as well as the series of identified, structural uncertainty shocks from the VAR. The empirical model identifies large uncertainty shocks after the 1987 stock market crash, the failure of Lehman Brothers, and the euro area sovereign debt crisis.To generate the unconditional moments in Table II of the main text, we detrend the log of each empirical data series using the HP filter with a smoothing parameter of 1600. We measure the unconditional volatility using the sample standard deviation of the detrended variable. We compute the empirical moments over the 1986-2014 sample period, which is the same time frame used in our empirical VAR. In Appendix Section D.3, we provide further analysis of the unconditional moments predicted by the model.We estimate stochastic volatility using a simple model-free and nonparametric method based on rolling sample standard deviations. Given an empirical data series, we estimate a rolling 5-year standard deviation. This procedure provides a time-series of realized volatility estimates for the given data series. Then, we compute the standard deviation of this time-series of estimates. This simple measure provides an estimate of the stochastic volatility in the data series. If the actual data were homoscedastic, the estimates of the 5-year rolling standard deviations should show little volatility and the resulting statistic would be near zero.

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Section: Empirical Evidencementioning

confidence: 99%

Uncertainty Shocks in a Model of Effective Demand

2017

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“…To show our results are robust, we estimate the model with interest rates that were not constrained by the ZLB. The last three columns of Table 1 show the correlations between real GDP growth and its estimated uncertainty series with three substitutes for the federal funds rate, including Wu and Xia's (2016) shadow rate, the 10-year T-Bond rate, and an ex ante real rate computed with the median onequarter ahead forecast of the GDP deflator from the Survey of Professional Forecasters (SPF). In each case, the ZLB correlation is more negative than the pre-ZLB correlation and the differences between the correlations are positive in less than 10% of draws.…”

Section: Time-varying Parameter Var With Stochastic Volatilitymentioning

confidence: 99%

The Zero Lower Bound and Endogenous Uncertainty

Plante

Richter

Throckmorton

2017

The Economic Journal

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This article examines the correlation between uncertainty and real GDP growth. We use the volatility of real GDP growth from a VAR, stock market volatility, survey‐based forecast dispersion and macro uncertainty index as proxies for uncertainty. In each case, a stronger negative correlation emerged in 2008. We contend the zero lower bound (ZLB) on the federal funds rate contributed to our finding. To test our theory, we estimate a New Keynesian model with a ZLB constraint to generate a data‐driven, forward‐looking uncertainty measure. The correlations between that measure and real GDP growth are close to the values in the data.

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“…From 2009, the Wu-Xia shadow policy rate (Wu and Xia 2015) is used as the US short-term interest rate to better represent the US monetary policy stance at zero lower bound. 3 Most of the data are converted into natural log form, except for the variables in percentage form.…”

Section: Empirical Frameworkmentioning

confidence: 99%

Spillovers of United States and People's Republic of China Shocks on Small Open Economies: The Case of Indonesia

et al. 2016

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Asian Development Bank InstituteThe Working Paper series is a continuation of the formerly named Discussion Paper series; the numbering of the papers continued without interruption or change. ADBI's working papers reflect initial ideas on a topic and are posted online for discussion. ADBI encourages readers to post their comments on the main page for each working paper (given in the citation below). Some working papers may develop into other forms of publication. The views expressed in this paper are the views of the author and do not necessarily reflect the views or policies of ADBI, ADB, its Board of Directors, or the governments they represent. ADBI does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequences of their use. Terminology used may not necessarily be consistent with ADB official terms.Working papers are subject to formal revision and correction before they are finalized Asian Development Bank Institute Kasumigaseki Building 8F 3-2-5 Kasumigaseki, Chiyoda-ku Tokyo 100-6008, JapanTel:+81-3-3593-5500 Fax:+81-3-3593-5571 URL:www.adbi.org E-mail: info@adbi.org AbstractThis paper examines the impact of certain external shocks originating from the United States (US) and People's Republic of China (PRC) on Indonesia as a small open economy. The spillover effects of tapering off, an interest rate hike, exchange rate devaluation, and real gross domestic product (GDP) are analyzed. Two versions of the global vector autoregression model are employed, which covers 33 countries and considers both financial and trade relations among countries. Spillover assessments are conducted through impulse responses with 1,000 bootstrap replications, and compared to the responses of peer countries.The results suggest that the main risk for Indonesia's real GDP is a shock to the PRC's real GDP, while a US interest rate hike is the greatest risk to Indonesia's exchange rate depreciation in the short term, especially compared to the US tapering off. Moreover, the dominant transmission channel of US monetary tightening is through finance, dampening economic growth in small open economies. JEL Classification: C32, E17, F47ADBI Working Paper 616 Harahap, et. al.

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Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound

Cited by 179 publications

References 0 publications

Uncertainty Shocks in a Model of Effective Demand

Uncertainty Shocks in a Model of Effective Demand

The Zero Lower Bound and Endogenous Uncertainty

Spillovers of United States and People's Republic of China Shocks on Small Open Economies: The Case of Indonesia

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