Back Matter
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 2 https://isni.org/isni/0000000404811396, International Monetary Fund

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Annex A. Estimation of autonomy-impairing spillovers

The estimation of autonomy-impairing spillovers from U.S. interest rates follows the two-stage VAR approach in Caceres, Carrière-Swallow, and Gruss (2016). In the first stage we estimate a country-specific VAR(p) model including domestic variables only:

[ΔXΔi]t=A0+Σj=1pAj[ΔXΔi]tj+[eXei]t(A1)

where i denotes the nominal domestic interest rate and X = {π, y} are domestic macroeconomic conditions in the small economy.

The reduced-form innovations e^X and e^i are orthogonal to lagged values of ΔX and Δi, but they are likely to display substantial contemporaneous correlation. We then regress the innovations e^i on the residuals from the other equation, e^X:

e^i=α+βe^X+uti.(A2)

The residuals u^ti from this regression are orthogonal to the reduced-form innovations to domestic economic conditions e^X, corresponding to a timing restriction whereby expectations about the domestic outlook are predetermined with respect to monetary policy. 25 These residuals can then be interpreted as deviations from the central bank’s historical policy reaction function characterizing its pursuit of price and output stabilization.

In the second stage, we seek to quantify to what extent these residual movements in domestic interest rates can be explained by movements in U.S. interest rates. To do so, we estimate the following country-specific VAR(p) model:

[Z*u^i]t=B0+Σj=1pBj[Z*u^i]tj+[ν*νi]t,(A3)

where vector Zt* is a vector of global variables, including changes in U.S. interest rates (Δi*). The matrices Bj are restricted to ensure the block exogeneity of Zt*. This restriction assumes that global variables are not affected by lagged domestic variables.

Autonomy-imparing spillovers from U.S. interest rates are defined as the response of u^ti from a shock to Δi*, with identification coming from a timing restriction imposed through Cholesky decomposition.

Annex B. Robustness exercises and data description

Figure B1.
Figure B1.

Determinants of spillovers – Testing the trilemma’s hypothesis Pre zero lower bound sample (January 2000 to June 2009)

Citation: IMF Working Papers 2016, 195; 10.5089/9781475543056.001.A999

Source: IMF staff calculations.Note: The charts show the cumulative monetary policy spillover (as defined in the text) to a 100-basis cumulative increase in the U.S. federal funds rate. Panel A shows the response under a fixed exchange rate (1st decile of the distribution in our sample, corresponding to an index value of 1in the Reinhart and Rogoff, 2004, course exchange rate classification), floating exchange rate (median in our sample, or index value 3) and fully flexible exchange rate (corresponding to the 9th decile in our sample and an index value of 4), while conditioning on high financial openness (the 9th decile of Aizenman, Chinn, Ito 2010 index of financial openness in our sample). Panel B shows the response under a closed, mid-open, and open financial openness, corresponding to the 1st decile, the median, and the 9th decile of Aizenman, Chinn, Ito (2010) index in our sample. The solid line reports the median response, conditional on the fundamental values. The dotted lines denote the 90 percent confidence interval, calculated using the bootstrap technique described in Towbin and Weber (2013).
Table B1.

Cumulative impulse response of domestic rates after 12 months; Robustness

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Note: The table reports the cumulative impulse response of domestic rates after one year to a shock to the federal funds rate that leaves it 100 basis points higher. * denotes statistical significance at the 10 percent level, and * denotes statistical significance at the 10 percent level.
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1

Birkbeck College, University of London, U.K.

1

This paper broadens the analysis presented in IMF (2015), which focused on emerging economies in Latin America and the Caribbean.

2

The debate on the ability of open economies to implement autonomous monetary policies in the context of a highly integrated global financial system has intensified recently. See, for instance, Rey (2015), Obstfeld (2015), and Caceres, Carrière-Swallow, and Gruss (2016).

3

For instance, Chile shifted its policy rate from a real (inflation-indexed) rate to a nominal rate in August 2001.

4

See Annex B for further details of index construction, and country-level data sources.

5

Chen, Mancini-Griffoli, and Sahay (2014) find that the VIX may amplify or dampen the effects of U.S. monetary policy. But they also argue that it contains additional information that may affect global asset prices, such as investor sentiment and risk appetite. Based on this, we include the VIX in the exogenous block of the model.

6

Throughout the paper we focus on models’ cumulative impulse response functions after 12 months to allow transmission to be fully realized.

7

Note however that while the identification strategy cannot distinguish between monetary policy shocks and inflationary surprises, our interest is in distinguishing expected interest rate movements associated with changes in the economic outlook.

8

The term premium can be thought of as the extra return investors require to hold a longer-dated bond instead of investing in a series of short-term securities, and is thought to reflect their uncertainty about the future path of interest rates as well as their degree of risk aversion. As such, movements in the term premium tend to be closely correlated with risk premiums on other assets in global financial markets.

9

The principal component is the linear combination of those series that captures the maximum variance in the available data.

11

We use interest rates on short-term government bonds (with maturities of about three months). Although these interest rates are not the monetary policy instrument, they should be closely linked to changes in the monetary policy stance. In fact, if changes in the policy instrument did not heavily influence these short-term interest rates in local currency, it would be hard to argue that the central bank can affect domestic monetary conditions at all

12

Svensson (1997, 1999) argues that inflation targeting implies inflation forecast targeting, where the central bank’s inflation forecast is an ideal intermediate target, even in the presence of output and/or interest rate stabilization concerns, and model uncertainty. There is also empirical evidence that central banks do react to changes in expected macro conditions rather than actual or lagged changes. For instance, Clarida, Galí, and Gertler (1998) show that the central banks of Germany, Japan and the United States adjust monetary policy rates in response to anticipated inflation, as opposed lagged inflation.

13

It could also be argued that using actual or lagged variables can introduce additional biases in spillover estimates. For example, suppose a given external development is expected to affect aggregate demand both in the United States and in a small open economy sometime in the near future, but has not affected measured activity yet, and both economies adjust their monetary policy stand accordingly in order to achieve their objectives set exclusively in terms of domestic variables. In this context, using actual macro variables would lead to wrongly consider the change in interest rate in the small open economy as a monetary spillover from the United States when, in fact, the domestic authority is acting fully consistently with its policy objective.

14

Besides being forward looking indicators, using expectations about GDP growth allows controlling for domestic conditions at a monthly frequency, which is not possible using GDP data.

15

Consider the case of a central bank that decides to increase interest rates in the face of a shock that would otherwise lead to exchange rate depreciation. Our procedure identifies the part of the rate increase that can be explained by its concern for the second-round effects on inflation, as captured by its historical behavior. The remainder is considered unexplained, even though it could correspond to an explicit intent to contain vulnerabilities from balance sheet mismatches in order to preserve financial stability.

16

We use lagged market forecasts to ensure that they are predetermined with respect to policy decisions, but this reduces their information content.

17

Under this argument and if the central bank is fully credible, market forecasts might not move at all in response to a shock that would otherwise affect output growth and inflation because agents anticipate that the central bank will do whatever is necessary to neutralize the shock.

18

Note that while the coefficients for the domestic interest rate are allowed to vary with country characteristics, we restrict the dynamics of external variables to be independent of country characteristics (e.g., α1,ct11=b111).

19

Some of the fundamentals we use are available at annual frequency. In those cases we use linear interpolation to convert the data to monthly frequency.

21

This corresponds to having a value of three under Reinhart and Rogoff’s (2004) coarse classification index.

22

The results in terms of exchange rate flexibility and financial openness remain valid when we add at third fundamental to the model.

23

The result for CDS spreads is consistent with the findings in Bowman, Londono, and Sapriza (2015) regarding the response of long-term domestic interest rates to unconventional monetary shocks in the United States.

24

While this difference may seem small, it should be noted that it corresponds to a rather limited reduction in the degree of dollarization, from 17 percent to six percent. Some countries in our sample have a much larger degree of dollarization (e.g., is about 60 percent in Peru).

25

The timing restriction is the same that would be imposed through a Cholesky decomposition to obtain structural impulse response functions from monetary policy shocks.

U.S. Monetary Policy Normalization and Global Interest Rates
Author: Carlos Caceres, Mr. Yan Carriere-Swallow, Ishak Demir, and Bertrand Gruss
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    Determinants of spillovers – Testing the trilemma’s hypothesis Pre zero lower bound sample (January 2000 to June 2009)