This post introduces a new dataset for a large cross-section of short-term monetary policy interest rates at a monthly frequency over a long period. This dataset was collected to assess international monetary policy spillovers in Peeters, Girard and Gnabo (forthcoming). For example, it contains monetary policy rates for 29 monetary areas observed between 1982 and 2021, which is the baseline scenario used in the paper.[1]
DOWNLOAD LINKS: RData, CSV, Microsoft Excel
Sources
The dataset is primarily composed of data collected by the Bank for International Settlements (BIS). To expand the time span of the samples, we supplement these data with information gathered from the Organization for Economic Co-operation andDevelopment (OECD), Macrobond, Datastream, and Central Banks as well as specific data for the shadow rates (see below).
Eurozone countries
Eurozone countries are treated as a unique entity in the paper, reflecting that these countries have shared a common monetary policy set by the European Central Bank (ECB) since 1999. Before 1999, the Eurozone countries are represented in the paper by German monetary policy rates as this country played the role of anchor, or base, country for most of the period since 1973 (notably within the European Monetary System).[2] Nevertheless, the dataset also provides monetary policy interest rates for all countries in the Eurozone, allowing the user to choose what fit better their needs.
Shadow rates
In addition to "conventional" monetary policy interest rates, we also supplement the dataset with shadow rates. For a large part of the samples, conventional short-term monetary policy rates ensure an appropriate representation of the monetary policy inclination of central banks and monetary authorities. However, the 2007-9 Global Financial Crisis (GFC) and the more recent COVID-19 crisis showed that nominal policy rates can be constrained by the zero lower bound and therefore do not account for recent monetary policy accommodations implemented through unconventional measures. To alleviate these shortcomings and take into account unconventional monetary policies, the dataset is completed with the estimates of so-called shadow rates in advanced economies constrained by the zero lower bound. These shadow rates can fall below zero during periods of unconventional policy and are more accurate to reflect the implied monetary policy.[3] More details about the definition and the computation of shadow policy rates can be found in Krippner (2013) and Wu and Xia (2016). The benchmark shadow rates used in the dataset are those defined by Leo Krippner and LJK Limited (https://www.ljkmfa.com/) for the United States, the Eurozone, Japan, the United Kingdom, Switzerland, Canada, Australia and New Zealand.[4] As a result, in additional to conventional policy rates, we provide a composite monetary policy indicator that equals short-term nominal monetary policy rates during conventional periods and shadow rates when monetary policy rates are constrained by the zero lower bound. The dataset contains a specific column explicitly mentionning if the row corresponds to conventional policy rates or this composite monetary policy indicator.
Details by countries
Details on the construction of the dataset (sources and definitions of the short-term interest rates) for every country can be found in the following link: details for every country.
Visualization of data
Additional useful links
- R code used in the paper to perform time-vaying spatial regression analyses
References
James, Harold (2012), Making the European monetary union. Harvard University Press.
Krippner, Leo (2013), “Measuring the stance of monetary policy in zero lower bound environments.” Economics Letters, 118, 135–138.
Peeters, Benjamin, Girard, Alexandre and Gnabo, Jean-Yves (forthcoming), “Monetary policy response or economic integration: whatdrives international monetary policy spillovers?”
Schnabl, Gunther (2009), “Exchange rate volatility and growth in emerging europe and eastasia.” Open Economies Review, 20, 565–587
Wu, Jing Cynthia and Fan Dora Xia (2016), “Measuring the macroeconomic impact ofmonetary policy at the zero lower bound.” Journal of Money, Credit and Banking, 48,253–291.
[1] The monetary areas included in this sample are: Algeria, Australia, Bangladesh, Belize, Canada, Denmark, Ecuador, Eurozone, Gambia, Ghana, Guyana, India, Iran, Jamaica, Japan, Jordan, Lebanon, Malaysia, Mauritania, Mexico, Nepal, New Zealand, Norway, Sierra Leone, South Africa, Sweden, Switzerland, the United Kingdom (UK) and the United Sates of America (US). Except for the Eurozone, all monetary areas are countries.
[2] One of the main reference to discuss of european monetary construction is James (2012). Schnabl (2009) also considers Germany as a relevant proxy for Eurozone countries before January 1999.
[3] By way of illustration, in the aftermath of the GFC, the Federal Reserve (Fed) reaches the zero lower bound from January 2009 until November 2015. The United Kingdom displays negative shadow rates from February 2010 to August 2017, while negative shadow rates for the ECB are reported since January 2012. In Japan, negative shadow rates occurs periodically from from October 1995 and their frequency and magnitude has significantly increased since January 2009.
[4] Alternative estimations for the shadow rates exist. For example, see sites.google.com/view/jingcynthiawu/shadow-rates.