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Impairment of Monetary Autonomy: Case of “Trilemma” vs. “Duo”

Abhishek Kumar Rohit†, Ankit Kumar‡, Pradyumna Dash§

Keywords: Mundellian trilemma; Monetary autonomy; Policy spillover; Autonomy impairment; TVP-FAVAR JEL Codes: E43, E52, F42

† Corresponding author, Department of Finance & Strategy, T A Pai Management Institute, Manipal, Karnataka, India-576104; [email protected]‡Department of Economics & Business Environment, Indian Institute of Management Raipur, Raipur, Chhattisgarh, India-492015; [email protected]§Department of Economics & Business Environment, Indian Institute of Management Raipur, Raipur, Chhattisgarh, India-492015; [email protected]

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mailto:[email protected]



Contents

Appendix A Sample data and Description 3

Appendix B Details of TVP-FAVAR estimation 4

Appendix C Detailed results of TVP-FAVAR estimation 6

Appendix D Categorization of sample economies based on exchange rate regime and capital openness 14

Appendix E Robustness checks 18

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Appendix A Sample data and Description

The study is based on a set of economies with diverse open-economy policy choices (exchange rate regime and degree of capital openness). We limit the study to 33 EMEs and AEs because of data considerations. The TVP-FAVAR model used for this study includes the variables of monetary reaction function for each of the economies. We specify that the short-term term interest rates in an economy is a function of the inflationary pressures, output, and exchange rates. The inclusion of exchange rates in our model has been motivated by (Engel, 2011).

The list of economies used in the study along with the variables and the data sources have been tabularized in Table A.1.

Table A.1. List of countries, variables under study, and data source

S. No. Country Variables & Data Source1 Australia 3 months T-bills rate has been used a proxy for

short-term rates. For economies where the data was unavailable, money market rates have been used. The data has been sourced from the IMF International Financial Statistics database.

Index of Industrial Production (IIP) has been used as a proxy for output. For economies where it was unavailable, interpolated quarterly GDP has been used. The data has been sourced from the CEIC database.

CPI has been used as an indicator of inflation. When unavailable, we have used core inflation. The data has been sourced from the CEIC database and Bloomberg.

Nominal exchange rate with respect to USD has been used as exchange rates for all the economies. The data has been sourced from the IMF International Financial Statistics database.

2 Brazil3 Canada4 Chile5 China6 Colombia7 Czech Republic8 Denmark9 Euro area (19 countries)10 Hong Kong11 Hungary12 Iceland13 India14 Indonesia15 Israel16 Japan17 Jordan18 Rep. of Korea19 Malaysia20 Mexico21 New Zealand22 Norway23 Peru24 Philippines25 Poland26 Romania27 Singapore28 South Africa29 Sweden30 Switzerland31 Thailand32 Turkey33 United Kingdom

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Appendix B Details of TVP-FAVAR estimation

The TVP-FAVAR methodology used in our study is based on the works of (Baumeister, Liu, & Mumtaz, 2013). To be precise, the methodology allows for time variations by including drift in the coefficients and the error covariance matrix of the transition equation. The alternative specification in which time-variations are included in the factor loading matrix (Del Negro and Otrok, 2008), does not seem suitable for our exercise as it seems plausible that the direct impact of macroeconomic conditions in 33 sample economies (captured by three latent factors) on rt is time-variant.

The observation equation, transition equation, and modelling of time-varying parameters have been discussed in Eq. 1 – 6 in the manuscript. Here, we present the selection of priors and the estimation methodology. The choice of priors is consistent with (Baumeister et al., 2013). The estimation technique mainly follows (Benati & Mumtaz, 2007; Cogley & Sargent, 2005; Primiceri, 2005).

Priors and Estimation

We assume that the elements of disturbance vector ¿ in equations (1) to (6) in the manuscript are distributed as

[e t

ηt

εt

τ t

∈t

] N (0 , V ) ,withV =[ζ 0 0 0 00 Κ t 0 0 00 0 M 0 00 0 0 R 00 0 0 0 G

]∧G=[σ12 0 0 0

0 σ22 0 0

0 0 σ 32 0

0 0 0 σ 42] (7)

We apply principal component estimator to each X i , t to centre our prior on the factors as done in (Bernanke et al., 2005). A large prior covariance of the states (P0|0) has been assumed to take into account large uncertainty surrounding the choice of starting values. The initial values of the factor loadings are also obtained from PC estimator, following (Baumeister et al., 2013). We assume that the priors on the diagonal component of ζ follow inverse gamma:

ζ ii IG (0.01,5)

The prior of the VAR coefficient is estimated through fixed-coefficient VAR model (using first four years of sample period i.e. Jan 2000 to Dec 2003). VAR coefficient ω0 is set equal to

ω0 N (ω̂OLS ,V )

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where V is the OLS estimates of var (ω̂OLS)on the main diagonal.

If ν̂ols is the OLS estimate of the VAR covariance matrix using sample period of Jan-2000 to Jan 2003, the priors for the diagonal elements of VAR covariance matrix can be given by

ln h0 N ( ln μ0 , 10 × I N )

where 0 are the diagonal elements of the matrix ν̂ols .

The priors on off-diagonal elements of matrix At follow

A0 N (b̂ols ,V (b̂ols))

where b̂ols are the off-diagonal elements of the Choleski decomposition of ν̂ols with each row scaled by the corresponding elements on the diagonal. The diagonal elements of the diagonal matrix V (b̂ols) is assumed to be 10 times the corresponding elements of b̂ols .

The prior on M is assumed to be inverse Wishart

M 0 IW (M 0 ,T 0)

where T 0 is sample period used for calibration and M 0 is assumed to be var (ω̂OLS)×10−4 ×T 0.

Next, the prior distribution of R is also assumed to be inverse Wishart

Ri , 0 IW (R0 , K i)

where i=1 , 2 ,3 are the indices of the blocks of R. K i is degree of freedom which is set equal to i+1. R0is a diagonal matrix whose value is set equal to b̂ols ×10−3.

The elements of G are assumed to be inverse gamma following (Cogley & Sargent, 2005)

σ i2 IG( 10−4

2, 12)

The factors and factor loadings are estimated following (Bernanke, Boivin, & Eliasz, 2005), where, the estimation proceeds as in (Carter & Kohn, 1994; Kim & Nelson, 1999). Once the factors have been estimated, the model takes the form of a standard VAR with drifting coefficients and covariances. As is standard in the literature, we draw the VAR coefficients using the approach outlined in (Carter & Kohn, 1994). The diagonal elements of the VAR covariance matrix have been sampled using methods explained in (Cogley & Sargent, 2005; Jacquier, Polson, & Rossi, 1994). The elements of At have been extracted using state space models as in (Carter & Kohn, 1994). We use 15000 Gibbs sampling

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replications and discard the first 14500 as burn-in. Additional details on the estimation of vector autoregression models which allows the possibility of time-variations in the coefficients and error covariance matrix can be found in (Benati & Mumtaz, 2007; Cogley & Sargent, 2005; Primiceri, 2005).

Appendix C Detailed results of TVP-FAVAR estimation

We analyze the impulse responses of the interest rates in sample economies to a 100 bps US monetary policy shock. We plot the impulse responses for each of the economies across all horizons and all sample years in Figure C.1.

These impulse responses have been defined as the degree of autonomy impairment in our study. Two findings emerge from the analysis of Figure C.1. Firstly, it can be seen that the autonomy impairment for all of the economies dies out by 12 months horizon. Hence, we conduct an extensive investigation of autonomy impairment in the main paper till 12 months horizon only. Secondly, the autonomy impairment in the pre-GFC period appears to be higher and more persistent as compared to the post-GFC period. We compute the average value of the median impulse responses for each of the economies in both of these periods. Further, we compute the difference between these two values. The details have been tabularized in Table C.1. We find the difference to be positive for all of the economies signaling a higher autonomy impairment in the pre-GFC period.

3-D plot of impulse responses across years and horizons

Impulse responses for year 2005 (pre-GFC period)

Impulse responses for year 2014 (post-GFC period)

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Figure C.1. Impulse response functions for short-term interest rateNotes: The figure shows the impulse response functions of short term interest rates for each of the sample economies to a 100-basis point increase in the US monetary policy rate. The left panel presents all time-varying median responses till 12 months horizon. The center and right panels cover responses for the pre-GFC (2005) and post-GFC (2014) years, respectively. Solid dark lines represent the 50 th percentile of responses, while the dotted lines represent the 16th and 84th percentiles.

Table C.1. Mean values of impulse responses in the pre-GFC and post-GFC periods.3 months horizon 6 months horizon 12 months horizonPre-GFC

Post-GFC

Difference Pre-GFC

Post-GFC

Difference Pre-GFC

Post-GFC

Difference

Australia 0.13 0.03 0.1 0.34 0.06 0.28 0.47 0.09 0.38Brazil 0.03 0.01 0.02 0.06 0.03 0.03 0.08 0.03 0.05

Canada 0.16 0.05 0.11 0.39 0.11 0.28 0.54 0.15 0.39Chile 0.23 0.19 0.04 0.32 0.22 0.1 0.36 0.23 0.13China 0.2 0.14 0.06 0.32 0.18 0.14 0.38 0.2 0.18

Colombia -0.06 -0.08 0.02 -0.01 -0.07 0.06 0.03 -0.06 0.09Czech

Republic0.19 0.08 0.11 0.4 0.14 0.26 0.55 0.18 0.37

Denmark 0.67 0.6 0.07 0.79 0.65 0.14 0.84 0.66 0.18Euro area 0.31 0.15 0.16 0.64 0.24 0.4 0.86 0.29 0.57

Hong Kong 0.44 0.32 0.12 0.69 0.42 0.27 0.82 0.46 0.36Hungary -0.05 -0.08 0.03 0 -0.05 0.05 0.05 -0.04 0.09Iceland 0.07 0.07 0 0.07 0.08 -0.01 0.07 0.09 -0.02India 0.2 0.12 0.08 0.36 0.18 0.18 0.45 0.21 0.24

Indonesia 0.32 0.25 0.07 0.46 0.31 0.15 0.54 0.34 0.2Israel 0.02 -0.04 0.06 0.14 -0.01 0.15 0.23 0.01 0.22Japan 0.16 0.1 0.06 0.29 0.14 0.15 0.37 0.16 0.21Jordan 0.29 0.24 0.05 0.38 0.27 0.11 0.43 0.28 0.15Korea 0.26 0.12 0.14 0.53 0.2 0.33 0.72 0.24 0.48

Malaysia 0.17 0.08 0.09 0.37 0.12 0.25 0.52 0.15 0.37Mexico 0.11 0.09 0.02 0.15 0.1 0.05 0.18 0.11 0.07

New Zealand

0.27 0.11 0.16 0.59 0.2 0.39 0.8 0.25 0.55

Norway 0.74 0.69 0.05 0.86 0.75 0.11 0.88 0.75 0.13Peru 0.01 0 0.01 0.02 0 0.02 0.04 0.01 0.03

Philippines 0.15 0.13 0.02 0.18 0.14 0.04 0.21 0.15 0.06Poland 0 -0.05 0.05 0.11 -0.03 0.14 0.19 -0.01 0.2

Romania -0.01 -0.02 0.01 0.01 -0.01 0.02 0.03 0 0.03Singapore 0.1 0.08 0.02 0.14 0.1 0.04 0.15 0.1 0.05

South Africa

0.18 0.14 0.04 0.27 0.16 0.11 0.33 0.18 0.15

Sweden 0.64 0.58 0.06 0.75 0.63 0.12 0.78 0.64 0.14Switzerlan

d0.32 0.21 0.11 0.54 0.26 0.28 0.68 0.3 0.38

Thailand 0.1 0.01 0.09 0.3 0.07 0.23 0.43 0.11 0.32

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Turkey 0 0 0 0 0 0 0 0 0United

Kingdom0.49 0.35 0.14 0.75 0.43 0.32 0.91 0.46 0.45

Notes: The values in the columns titled pre-GFC and post-GFC refer to the average values of median impulse responses in these two periods, respectively. The higher of these two values has been presented in bold. The column titled Difference presents the gap between these average values.

Appendix D Categorization of sample economies based on exchange rate regime and capital openness

The second part of the results in the manuscript categorizes the sample economies based on their open-macroeconomic policy choices of exchange rate regime and capital openness. These two policy choices have a direct influence on the degree of monetary autonomy exercised by an economy as has been discussed in (Mundell, 1963). We classify our sample economies as either open or closed, following Chinn and Ito (2006). We have used the value of kaopen>0.66 to categorize an economy as open in a particular year. The exchange rate regime has been categorized as pegged, managed, or flexible, following Ilzetzki, Reinhart, and Rogoff (2017).

Based on the combination of these two policy choices, we broadly classify the sample economies into six categories, i.e., (i) open_pegged, (ii) open_managed, (iii) open_flexible, (iv) closed_pegged, (v) closed_managed, and (vi) closed_flexible. The study is limited to the first five of these categories because we could not find enough sample points in the closed_flexible category. Further, we follow a dynamic classification approach in which an economy is allowed to shift between these categories every year. Table D.1 presents a list of the sample economies along with the categories they belong to for each year of the sample period.

Table D.1. List of sample economies under each of the five categories.Yea

rCategories of economies

open_flexible open_managed open_fixed closed_managed

closed_pegged

2004 AustraliaJapanEuro

CanadaChileHungaryIcelandIndonesiaIsraelMexico

Czech RepublicDenmarkHong KongJordanSweden

BrazilColombiaKoreaPolandSouth AfricaThailandTurkey

ChinaIndiaMalaysiaPhilippines

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New ZealandNorwayPeru


CanadaChileHungaryIcelandIndonesiaIsraelMexicoNew ZealandNorwayPeruRomaniaSingaporeSwitzerlandUnited Kingdom


BrazilColombiaKoreaPhilippinesPolandSouth AfricaThailandTurkey

ChinaIndiaMalaysia


CanadaChileHungaryIcelandIndonesiaIsraelMexicoNew ZealandNorwayPeruRomaniaSingaporeSwitzerlandUnited Kingdom


BrazilColombiaKoreaMalaysiaPhilippines

ChinaIndia


CanadaChileHungaryIcelandIsraelMexicoNew ZealandNorwayPeruSingaporeSwitzerland

Czech RepublicDenmarkHong KongIndonesiaJordanRomaniaSweden

BrazilColombiaKoreaMalaysiaPhilippinesPolandSouth AfricaThailandTurkey

ChinaIndia

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United Kingdom


CanadaChileColombiaHungaryIsraelMalaysiaMexicoNew ZealandNorwayPeruSingaporeSwitzerlandUnited KingdomCanadaChile

Czech RepublicDenmarkHong KongIndonesiaJordanRomaniaSweden

BrazilIcelandKoreaPhilippinesPolandSouth Africa

ChinaIndia

2009 AustraliaJapanUnited KingdomEuro

CanadaChileIsraelMexicoNew ZealandNorwayPeruSingaporeSwedenSwitzerland

Czech RepublicDenmarkHong KongHungaryIndonesiaJordanRomania

BrazilColombiaIcelandIndiaKoreaMalaysiaPhilippinesPolandSouth AfricaThailandTurkey

China


CanadaChileIsraelMexicoNew ZealandNorwayPeruSingaporeSwedenSwitzerland

Czech RepublicDenmarkHong KongHungaryIndonesiaJordanRomania

BrazilColombiaIcelandIndiaKoreaMalaysiaPhilippinesPolandSouth AfricaThailandTurkey

China

2011 AustraliaJapanUnited

CanadaChileIsrael

Czech RepublicDenmarkHong Kong

BrazilColombiaIceland

ChinaIndonesia

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KingdomEuro

KoreaMexicoNew ZealandNorwayPeruSingaporeSwedenSwitzerland

HungaryJordanRomania

IndiaMalaysiaPhilippinesPolandSouth AfricaThailandTurkey


CanadaChileIsraelKoreaMexicoNew ZealandNorwaySingaporeSweden

Czech RepublicDenmarkHong KongHungaryJordanPeruRomaniaSwitzerland

BrazilColombiaIcelandIndiaMalaysiaPhilippinesSouth AfricaThailandTurkey

ChinaIndonesiaPoland




BrazilColombiaIcelandMalaysiaPhilippinesSouth AfricaThailandTurkey

ChinaIndiaIndonesiaPoland




BrazilColombiaIcelandMalaysiaPhilippinesSouth AfricaThailandTurkey

ChinaIndiaIndonesiaPoland


CanadaChileIsraelKoreaMexico

Czech RepublicDenmarkHong KongHungaryJordan

BrazilColombiaIcelandMalaysiaPhilippines

ChinaIndiaIndonesia

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New ZealandNorwaySingaporeSwedenSwitzerland

PeruPolandRomania

South AfricaThailandTurkey


CanadaChileIsraelKoreaMexicoNew ZealandNorwaySingaporeSwedenSwitzerland

Czech RepublicDenmarkHong KongHungaryJordanPeruPolandRomania

BrazilColombiaIcelandIndonesiaMalaysiaPhilippinesSouth AfricaThailandTurkey

ChinaIndia

Appendix E Robustness checks

We test the sensitivity of our main results by conducting robustness checks in two ways. Firstly, we re-estimate the TVP-FAVAR model using a different number of factors. The estimation in the main paper is based on three latent factors as suggested by Bai and Ng (2002) IC1 criterion. For robustness, we resort to the criterion provided by Alessi, Barigozzi, and Capasso (2010) which suggests five factors. Following (Barigozzi, Conti, & Luciani, 2014), we re-estimate our model using four (the average of what these two criteria suggest) and five factors. The impulse responses averaged for each of the categories have been shown in Figure E.1 and Figure E.2 for four and five number of factors, respectively.

The robustness checks conducted using alternative specifications of four and five factors provide results which are consistent with the main findings of the paper. The finding of higher monetary autonomy impairment during the pre-GFC period still holds. Further, we do not find differences in the degree of autonomy impairment between open_flexible and closed_pegged to be statistically significant in any of the alternative specifications. Thus, our finding of the similar benefits of flexible exchange rates and capital restrictions are further validated. With respect to the debate between the “trilemma” and the “duo” hypotheses, we find that the autonomy impairment for the open_flexible economies is quite lower than that of the open_pegged economies at the 3 months horizon. However, this gap diminishes at the 12 months horizon, thereby signalling that the benefits of flexible exchange rates are short-lived, as has been documented in the man findings of the paper as well.

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Secondly, we shown robustness using sign restrictions as an alternative identification scheme following Canova and De Nicoló (2002) and Uhlig (2005). The following illustration of sign restrictions is in continuation of the TVP-FAVAR specification illustrated in the methodology section of the main paper**. Our procedure of imposing sign restrictions is similar to Ellis, Mumtaz, and Zabczyk (2014). To summarize, we let Θt=Pt Pt

' be a Cholesky decomposition of the VAR covariance matrix Θt. Further, we draw an N∗N matrix, Z, from the N (0, 1) distribution. We then take the QR decomposition of Z, which provides us with a candidate structural impact matrix as Ao ,t=Pt J . We then compute the contemporaneous impulse response of ( X 1, t , . . , XN ,t ) to the shock as:

(irf X 1 ,t

0

irf X 2 ,t0

...irf XN ,t

0

rt

)=(❑11 . ... ❑K 1 ❑11

. . ... . .⋮ ⋮ ... ⋮ ⋮. . ... ❑KN ❑1 N

0 0 ... 0 1)× Ao ,t

where irf Xi, t0 denotes the response of the ith variable at horizon 0. It is then checked if these

responses satisfy our imposed minimal sign restrictions. We store A0,t, if the sign restrictions are satisfied. This procedure is repeated at each time period till we attain 100 A0,t matrices satisfying our sign restrictions. The selection is made from this set of ‘admissible models’. Specifically, we choose the A0,t matrix whose elements are closest to the median of the set of 'admissible models' of 100 estimates as suggested by (Fry & Pagan, 2007).

We motivate our minimal sign restrictions based on the theoretical insights of the Mundell-Fleming model. According to the Mundell-Fleming model, the monetary policy rate in the open economies with pegged exchange rates moves in line with the monetary policy rate in the base economy (US in our case).

Hence, we impose the restriction that a contractionary monetary policy shock in the US leads to an increase in the short-term rate of open economies with pegged exchange rate regime (open_pegged category in our study). We have not imposed any restriction on the other variables, i.e., prices, output, and exchange rate. Further, we impose restrictions only on those economies which are in the open_pegged category for at least seven years (more than half of the sample period). This includes Czech Republic, Denmark, Hong Kong, Jordan, Hungary and Romania.

Table E.1 presents the average values of median impulse responses of short term interest rates at the horizons of 3 months, 6 months and 12 months. Figure E.3 shows the average impulse responses for each of the five categories.

The main findings of the paper remain robust under the alternative identification scheme of sign restrictions. The degree of autonomy impairment is again higher in the pre-GFC period as compare to post-GFC period (see Table E.1). Barring few instances (for example, in 2007), the autonomy impairment is the same for the open_flexible and closed_pegged economies, implying similar roles of flexible exchange rates and capital ** We have used two factors while estimating TVP-FAVAR model with sign restrictions due to computational difficulties.

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restrictions. With regards to the debate between the “duo” and the “trilemma” hypotheses, we find that the sharpest differences in the degree of autonomy impairment between the open_flexible and open_pegged categories are at the 3 months horizon only. This signifies that the flexible exchange rates are unable to insulate monetary autonomy in open economies at longer horizons.

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Figure E.1. Result of TVP-FAVAR model with four latent factors. Notes: The figure shows category wise average cumulative median response over 3, 6, and 12 months horizons to 100 basis point increase in the US monetary policy rate.

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Figure E.2. Results of TVP-FAVAR model with five latent factors. Notes: The figure shows category wise average cumulative median response over 3, 6, and 12 months horizons to 100 basis point increase in the US monetary policy rate.

Table E.1. Mean values of impulse responses in the pre-GFC and post-GFC periods. 3 months horizon 6 months horizon 12 months horizonPre-GFC

Post-GFC

Difference Pre-GFC

Post-GFC

Difference Pre-GFC

Post-GFC

Difference

Australia 0.58 -0.11 0.69 0.94 -0.07 1.01 1.13 -0.05 1.18Brazil 0.71 0.40 0.31 0.86 0.44 0.43 0.93 0.45 0.49

Canada 0.80 0.19 0.61 1.11 0.23 0.88 1.27 0.25 1.02Chile 0.97 0.03 0.94 1.40 0.07 1.33 1.63 0.09 1.53China 0.36 0.11 0.25 0.50 0.13 0.37 0.57 0.14 0.43

Colombia 0.41 0.03 0.38 0.56 0.04 0.52 0.63 0.04 0.59Czech

Republic 0.70 0.12 0.58 0.96 0.15 0.81 1.10 0.17 0.93Denmark 0.43 0.24 0.20 0.58 0.27 0.31 0.66 0.28 0.38Euro area 1.02 0.11 0.92 1.46 0.15 1.30 1.70 0.18 1.52

Hong Kong 1.33 0.53 0.80 1.75 0.59 1.16 1.96 0.61 1.35Hungary 0.90 0.31 0.58 1.09 0.33 0.76 1.19 0.34 0.85Iceland 0.51 0.45 0.06 0.49 0.45 0.04 0.47 0.45 0.02

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India 0.43 0.24 0.20 0.58 0.27 0.31 0.66 0.28 0.38Indonesia 1.22 0.45 0.77 1.55 0.49 1.07 1.72 0.51 1.21

Israel 0.80 0.00 0.80 1.17 0.04 1.14 1.38 0.06 1.32Japan 0.09 0.02 0.07 0.13 0.03 0.10 0.15 0.03 0.12Jordan 1.24 0.43 0.81 1.65 0.47 1.18 1.87 0.49 1.38Korea 1.01 0.14 0.87 1.42 0.19 1.23 1.64 0.22 1.42

Malaysia 0.32 0.00 0.32 0.48 0.02 0.46 0.56 0.03 0.53Mexico 0.58 0.27 0.32 0.74 0.29 0.45 0.82 0.30 0.52

New Zealand 1.10 0.09 1.00 1.58 0.15 1.44 1.85 0.18 1.67Norway 0.33 0.28 0.05 0.42 0.31 0.11 0.46 0.32 0.14

Peru 0.55 0.15 0.41 0.73 0.16 0.57 0.82 0.17 0.65Philippines 0.65 0.30 0.35 0.84 0.34 0.51 0.94 0.35 0.59

Poland 0.62 -0.02 0.64 0.90 0.01 0.89 1.06 0.03 1.03Romania 1.70 0.67 1.03 2.05 0.70 1.36 2.23 0.72 1.52Singapore 0.10 0.04 0.06 0.14 0.05 0.09 0.16 0.06 0.11

South Africa 0.56 0.15 0.41 0.77 0.18 0.60 0.89 0.19 0.70

Sweden 0.30 0.20 0.11 0.42 0.23 0.19 0.49 0.24 0.24Switzerland 0.66 0.04 0.61 0.97 0.08 0.89 1.15 0.10 1.05

Thailand 0.70 0.12 0.58 0.96 0.15 0.81 1.10 0.16 0.94Turkey 1.57 0.43 1.13 2.09 0.49 1.60 2.38 0.52 1.86United

Kingdom 0.75 0.08 0.67 1.12 0.14 0.99 1.32 0.17 1.16Notes: The values in the columns titled pre-GFC and post-GFC refer to the average values of median impulse responses in these two periods, respectively. The higher of these two values has been presented in bold. The column titled Difference presents the gap between these average values.

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Figure E.3. Results of TVP-FAVAR model identified using sign restrictions.Notes: The figure shows category wise average cumulative median response over 3, 6, and 12 months horizons to 100 basis point increase in the US monetary policy rate.

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appendix a sample data and description · web viewabhishek kumar rohit corresponding author,...

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