the spillover effects of chinese economy on southeast asia ... · the chinese economy is maturing...

NBP Working Paper No. 315

The spillover effects of Chinese economy on Southeast Asia and Oceania

Anna Sznajderska, Mariusz Kapuściński

Narodowy Bank PolskiWarsaw 2019

NBP Working Paper No. 315

The spillover effects of Chinese economy on Southeast Asia and Oceania

Anna Sznajderska, Mariusz Kapuściński

Published by: Narodowy Bank Polski Education & Publishing Department ul. Świętokrzyska 11/21 00-919 Warszawa, Poland www.nbp.pl

ISSN 2084-624X

© Copyright Narodowy Bank Polski 2019

Anna Sznajderska – Warsaw School of Economics and Narodowy Bank Polski; [email protected]

Mariusz Kapuściński – Warsaw School of Economics and Narodowy Bank Polski; [email protected]

This work was supported by the National Science Center under Grant No. 2016/21/D/HS4/02798. The authors are grateful for helpful comments of conference participants at EcoMod 2019 in Ponta Delgada.This paper represents the opinions of the author. It is not meant to represent the position of the NBP. Any errors and omissions are the fault of the author.

3NBP Working Paper No. 315

ContentsAbstract 4

1. Introduction 5

2. Related literature 6

3. Methods 8

3.1. GVAR model 8

3.2. Robustness of the results – BVAR models 10

4. Data 13

5. Empirical results and discussion 15

5.1. The output spillover effects from China to Southeast Asia and Oceania 15

5.2. Robustness analysis - the results from BVAR models 17

5.3. Factors differentiating responses to a China GDP impulse 22

Conclusions 24

References 25

Appendix 27

Narodowy Bank Polski4

Abstract

1

The spillover effects of Chinese economy on Southeast Asia and Oceania1

Anna Sznajderska* Mariusz Kapuściński**

The slowdown of economy and widening of domestic imbalances in China bothers economists and politicians across the globe. The effects of a Chinese transition to a new growth model for other countries are uncertain. We quantify them by estimating the influence of a negative output shock in China on a number of different economies. We concentrate on China’s neighbouring countries. We compare the results from the Global VAR model and from the Bayesian VAR models that include Chinese variables as endogenous. Also we search for determinants of Chinese spillovers for the global economy. To this end, having a large number of factors potentially explaining differences in responses compared to the number of observations, we use Bayesian model averaging. We find that spillovers are stronger to economies with less flexible exchange rates, a higher share of manufacturing in gross value added and to economies which are larger.

JEL codes: C32, E32, F10, O53

Keywords: global VAR, Bayesian VAR, China’s slowdown, spillovers, structural characteristics

* Warsaw School of Economics and Narodowy Bank Polski, Email: [email protected]

** Warsaw School of Economics and Narodowy Bank Polski, Email: [email protected]

1This work was supported by the National Science Center under Grant No. 2016/21/D/HS4/02798. The authors are grateful for helpful comments of conference participants at EcoMod 2019 in Ponta Delgada.

Abstract


Chapter 1

2

1. Introduction

Over the past four decades China has become a global force in the world economy. China has contributed to approximately one-third of world economic growth since 2005 (see Chart 27 in Dieppe et al. 2018). But having reached over 14% in 2007, real GDP growth slowed to about 6.4% in 2018 (see Figure 14). The Chinese economy is maturing and rebalancing from import-intensive investment towards consumption.2 Dieppe et al. (2018) point out that domestic imbalances in China are widening and may lead to a number of vulnerabilities. These fragilities include rapid credit growth, as well as increased complexity and leverage in the financial system.

China plays a profound role in regional trade. It is a central point of the Asian supply chains. Thus, the slowdown of the Chinese economy is particularly troubling for its neighbouring countries. In this paper we decided to concentrate on China’s spillover effects to Southeast Asia and Oceania countries.

The main contribution of this paper is the application of GVAR and a set of BVAR models to quantify the implications of China’s slowdown for a number of economies, particularly in Southeast Asia and Oceania. The usage of a set of BVAR models in this context is a novel approach. We compare the two types of VAR models. Also we use a novel and large dataset for 59 economies. Lastly, using Bayesian model averaging, we examine the structural features that determine the impact of the Chinese economy on other economies.

To assess the effects of negative demand shock in China we use the GVAR model that enables us to model the whole global economy and to capture economic linkages between countries. We implement the model with the original set of variables and time-varying link matrixes. Also we estimate a BVAR model for each economy separately as a robustness check. Each BVAR model includes Chinese variables as partially exogenous.

Our results show that the response of neighbouring countries to negative demand shock in China is stronger and faster than the average response of other economies. The results from BVAR models seem to confirm the results from the GVAR model. According to our results Chinese Taipei is the most affected economy by negative GDP shock in China. Also Indonesia, Singapore, Hong Kong, Thailand, Malaysia are strongly affected. Australia seems to be also affected but to a lesser degree. We find weak GDP response to China GDP shock for New Zealand and the Philippines. The response of GDP in South Korea and Japan is quite significant, but only according to the results from the GVAR model. Moreover, we find that spillovers are stronger to economies with less flexible exchange rates, a higher share of manufacturing in gross value added (GVA) and to economies which are larger.

The article is structured as follows. In the second section we describe related literature. In the third section methods. The fourth section lists variables and their sources. In the fifth section we describe results, including a sensitivity analysis. The last section concludes.

2 Investment slowed from over 17% in 2000 (12) to 3% in 2015, while private consumption slowed from 13% in 2000 (12) to 9% in 2015.

2

1. Introduction








1

The spillover effects of Chinese economy on Southeast Asia and Oceania1

Anna Sznajderska* Mariusz Kapuściński**

The slowdown of economy and widening of domestic imbalances in China bothers economists and politicians across the globe. The effects of a Chinese transition to a new growth model for other countries are uncertain. We quantify them by estimating the influence of a negative output shock in China on a number of different economies. We concentrate on China’s neighbouring countries. We compare the results from the Global VAR model and from the Bayesian VAR models that include Chinese variables as endogenous. Also we search for determinants of Chinese spillovers for the global economy. To this end, having a large number of factors potentially explaining differences in responses compared to the number of observations, we use Bayesian model averaging. We find that spillovers are stronger to economies with less flexible exchange rates, a higher share of manufacturing in gross value added and to economies which are larger.

JEL codes: C32, E32, F10, O53

Keywords: global VAR, Bayesian VAR, China’s slowdown, spillovers, structural characteristics

* Warsaw School of Economics and Narodowy Bank Polski, Email: [email protected]

** Warsaw School of Economics and Narodowy Bank Polski, Email: [email protected]

1This work was supported by the National Science Center under Grant No. 2016/21/D/HS4/02798. The authors are grateful for helpful comments of conference participants at EcoMod 2019 in Ponta Delgada.

8

3.2 Robustness of the results – BVAR models

We apply the Bayesian VAR models to check the robustness of the results from the GVAR model. BVAR models are estimated for each country separately.10 To ensure maximum comparability to GVAR model, we estimate the models on first differences (all variables except of interest rate are in first differences).

�� , �� is a vector of endogenous variables,

�� is a vector of exogenous variables, and it corresponds to foreign variables and the dominant unit in the GVAR model,

��, �� is a vector of residuals.

We identify shocks using the Cholesky decomposition, which is easier to reasonably implement within the BVAR framework, than within the GVAR.

The specification of the BVAR models is chosen to be as similar to the GVAR model as possible.

The endogenous variables are as follows: Chinese GDP (yChina), Chinese interest rate (iChina), domestic GDP (y), domestic price level (p), domestic interest rate (i), domestic stock price index (s), and domestic real effective exchange rate (r). The order of variables for the Cholesky identification is as written above11. The exogenous variables are foreign GDP (without China) (yforeign), foreign price level (pforeign), and oil price level (oil). The choice of variables is similar to Peersman and Smets (2001). The number of lags is 4.

Note that in the models for Algeria, Croatia, Saudi Arabia, and Romania, stock price indexes are not included due to the unavailability of data.

We use Minnesota prior with the following parameters:

Table 1. Hyperparameters for Minnesota prior

auto-regressive coefficient: 0.8

overall tightness λ1: 0.2

cross-variable weighting λ2: 0.5

lag decay λ3: 2

exogenous variable tightness λ4: 100000

Auto-regressive coefficient is equal to 0.8, because our variables are known to be stationary (see Dieppe 2016). The rest of the hyperparameters are chosen on the basis of suggestions in Canova (2007).

10 We estimate BVAR models using the modified BEAR 4.2 toolbox. 11 The order of the variables is similar to the order used in the NBP Working Papers on monetary transmission mechanism (see Demchuk et al. 2012, Kapuściński et al. 2014 and 2016, and Chmielewski et al. 2018).

9

We implement block exogeneity to create partial exogeneity within our set of endogenous variables. We want domestic variables (y, p, i, s, r) to have no impact on Chinese variables (yChina, iChina) (see Table 2).

Table 2. Exogeneity block

yChina iChina y p i s r yChina iChina y 1 1 p 1 1 i 1 1 s 1 1 r 1 1

The value of 1 in row i and column j of the table means that variable i has no effect on variable j.

3.3 Bayesian model averaging

After computing responses of GDP in respective economies to a GDP impulse in China from GVAR and BVAR models, we attempt to explain them by the countries’ structural characteristics. In principle, we estimate the parameters of the following linear regression models:

�� ,

where �� is an impulse response (either from the GVAR or from the BVAR model), �� is a vector of structural characteristics for each economy, � is a vector of coefficients and �� is an error term.

As a dependent variable, we take the maximum effect within 20 quarters (5 years) after an impulse.

We consider as many as 18 (16-17 at the same time) structural characteristics12 within the following groups:

the structure of international trade (shares of: agriculture, fuels and mining products, and manufacturing in trade with China),

the structure of gross value added (shares of: agriculture, industry, manufacturing and services in gross value added),

financial openness (Chinn-Ito index, where the higher the value, the higher the capital account openness),

the size of international investment position (the sum of international investment position assets and liabilities to GDP ratio),

distance (between capitals of respective economies and Beijing),

12 Initially we considered 39 structural characteristics, but we deleted 5 of them because of a lot of missing data and next we deleted 16 of them because of a high correlation with other data from a similar group.


Chapter 2

3

2. Related literature

China has become the largest market for Asian economies, surpassing Japan in 2005 and the United States in 2007 (Inoue et al., 2015). Because of the recession and anaemic growth in the United States and the euro zone, since 2008 the demand from advanced economies for Asia-Pacific exports has been decreasing. Asia-Pacific countries, however, have benefited from the surging Chinese domestic demand. Exports from Asia-Pacific countries to China have doubled over the last 5 years.

On the one hand China exerts significant effects on other Asian economies through direct trade linkages, and on the other hand China affects Asian economies indirectly through its impact on commodity prices. Chinese demand for metal and oil is high and increasing3, because of high investment in the domestic economy and urbanisation process in China. Osorio and Unsal (2013) by applying the GVAR model estimate both the direct and indirect channels.4 The authors estimate that within two years about 20% of the spillovers from positive demand shock in China are due to higher commodity prices. Also, indirect impacts transmit faster to inflation in Asian economies than direct impacts.

In the literature much attention has been directed to the international spillover effects of China’s slowdown. Using the GVAR model, Cashin et al. (2016) examine the impact of China’s slowdown and the higher global financial market volatility on other economies. It appears that the negative output shock in China has the largest effect on less-diversified commodity exporters and ASEAN-5 countries (Indonesia, Malaysia, Singapore, and Thailand – without the Philippines). Following a 1 p.p. decrease of the real GDP growth in China, global growth decreases by 0.23 p.p. in the short term, and oil prices fall by 2.8 p.p. in the long term. Similarly, Sznajderska (2019) by estimating a different GVAR model shows that a 1 p.p. negative China GDP shock reduces global output by 0.22 p.p. in the short run, leading to a maximum 1 p.p. decrease in the global output in the longer term.

Blagrave and Vesperoni (2018) show that spillover effects are largest for countries with tighter trade links to China, particularly those in Asia. They analyse demand shocks for Chinese secondary and tertiary sectors separately. They apply a panel VAR model with China’s demand shocks, identified outside the model, as exogenous variables. Authors show that shocks to China’s secondary sector have much larger effects on partner export countries than shocks to China’s tertiary sector. In our study we do not take into account the tertiary sector, in the sense that we measure links between countries using DOTS statistics.

Also the Chinese stock market crash (2015-2016) shows that China is becoming more integrated with the regional financial markets. Abdullahi and Rui (2019), for instance, by investigating daily data between 2015 and 2016, show strong shock spillover effects from China to most Asia-Pacific stock markets (except New Zealand).

3 China consumed about 50% of copper and aluminium (2016), 60% of iron ore and 12% of oil (2015), produced globally. 4 It appears that the level of inflation is most strongly affected in Indonesia and next in Malaysia, India, Philippines, Thailand, Singapore, and Korea. While Hong Kong, New Zealand, Australia, and Japan are less affected.

3








3








2

1. Introduction








3








2

1. Introduction









Related literature

4

It is important to note that Chinese financial linkages are smaller than linkages through trade channels. Capital flows in and out of China are restricted legally. Despite capital controls, financial integration of China is de facto non-negligible (see Dieppe et al. 2018). Dieppe et al. note that the share of China and Hong Kong's global gross asset and liability position in 2015 was approximately 8% (to compare with 10% share in global imports or 22% share in global energy consumption in 20155). Based on the data from BIS locational international banking statistics we may notice that the highest financial flows are between China and Hong-Kong6, followed by the euro area, the United States, the United Kingdom, Japan, Taiwan7, Korea, Australia, and Canada, in descending order (see Figure 11 in the Appendix).

The important question is why some economies are affected more by China’s spillovers and some are less. We try to answer this question by looking at structural characteristics of the economies. Georgiadis is the author of two papers in which he shows that differences within the monetary transmission mechanism depend on the differences in the structural characteristics of the economies. He applies GVAR methodology with sign restrictions.

In his first paper Georgiadis (2015a) studies the transmission of the common euro area monetary policy across the individual euro area economies. The euro area countries in which a share of sectors with the demand sensitive to interest rate changes in the accumulated industry is bigger exhibit a stronger monetary transmission to real economic activity. Similarly, stronger transmission is observed in the countries with more real wages rigidities and/or fewer unemployment rigidities. In his second paper, Georgiadis (2015b) studies spillover effects from the US economy. The results of the study suggest that policymakers could mitigate the US spillover effects by encouraging trade integration, financial market development, flexibility of exchange rates and by reducing labour market rigidities. On the other hand, other policies, such as slowing the process of financial integration and industrialisation or participation in the global value chains, could also reduce the spillover effects, but would probably decrease the level of long-run economic growth as well.

We analyse the factors which explain differences in spillovers similarly as Georgiadis (2015a, 2015b), by regressing maximum point impulse responses on structural characteristics of respective economies. However, due to having a large number of candidate characteristics compared to the number of observations, we have used Bayesian model averaging. A similar approach was used, for example, by Havranek and Rusnak (2013) in order to analyse the transmission lags of monetary policy. In contrast to their analysis, we do not carry out a meta-analysis of existing studies.

5 These statistics do not include Hong-Kong. 6 A special administrative region of China. Hong Kong is a major renminbi center. 7 In 2012, an agreement was signed between the central banks of China and Taiwan enabling direct settlement of their currencies. As a result, Taiwan has become a local Chinese renminbi center.

4






4






4





5 These statistics do not include Hong-Kong. 6 A special administrative region of China. Hong Kong is a major renminbi center. 7 In 2012, an agreement was signed between the central banks of China and Taiwan enabling direct settlement of their currencies. As a result, Taiwan has become a local Chinese renminbi center. 3








3








4





5 These statistics do not include Hong-Kong. 6 A special administrative region of China. Hong Kong is a major renminbi center. 7 In 2012, an agreement was signed between the central banks of China and Taiwan enabling direct settlement of their currencies. As a result, Taiwan has become a local Chinese renminbi center. 4







Chapter 3

5

3. Methods

3.1.GVAR model

We use the global vector autoregressive model to investigate the impact of the Chinese economy on other economies.8

The GVAR model is estimated within two steps (see Pesaran et al. (2004) and Dees et al. (2007a)). First let us consider the VARX(��, ��) models for each country (� = 1,�� is the number of countries), where�� and �� are lags lengths selected by SBC.

�� = �� + �� + Φ��,�� + ⋯+Φ��,�� + Λ��∗ + ⋯+ Λ��,��∗

+ Ψ�� + ⋯+Ψ�� + ��,

(1)

�� – is a vector of domestic variables,

��∗ – is a vector of foreign variables,

�� – is a vector of dominant unit model variables (here: oil prices).

Note that �� is treated as a foreign variable in the GVAR model and shares the same lag order as foreign variables.

Foreign variables are calculated as a weighted average of domestic variables:

��∗ = ��

��, �� = 0,

and �� are weights calculated on the basis of bilateral trade flows (∑ �� = 1).��is the

number of domestic variables and ��∗is the number of foreign variables in �th country.

Following other studies we estimate the error correction form (VECMX(��, ��) models) of the VARX(��, ��) specification for each country (cf. Pesaran et al. (2004), Backé et al. (2013), Bussière et al. (2012), Dees et al. (2007b), Inoue et al. (2015)):

�� = �� +��,��

��+��,��

��

��+�Λ��,��∗

��

��+�Ψ��

��

��+ ��.

�� are error correction terms and �� is the number of cointegrating relations. In Table 5 we present the results of unit root tests for domestic variables. Most of them are nonstationary with stationary first differences. The usage of VECM models allows as to capture long-run relationships that exists among the domestic and the country specific foreign variables.

The dominant unit is modelled as:

�� = �� + �� + �� + ⋯+ �� + �� + ⋯+ �� + ��, (2)

8 We estimate a GVAR model using the modified GVAR 2.0 toolbox.

5

3. Methods

3.1.GVAR model



�� = �� + �� + Φ��,�� + ⋯+Φ��,�� + Λ��∗ + ⋯+ Λ��,��∗

+ Ψ�� + ⋯+Ψ�� + ��,

(1)






��∗ = ��

��, �� = 0,




�� = �� +��,��

��+��,��

��

��+�Λ��,��∗

��

��+�Ψ��

��

��+ ��.



�� = �� + �� + �� + ⋯+ �� + �� + ⋯+ �� + ��, (2)


6

where �� are feedback variables, constructed as a weighted average of variables included in the models for non-dominant units (see Smith and Galesi (2014) for details).

In the second step, the corresponding VARX models (Eq.1) are recovered from the estimated VECMX models. Then the individual country models are stacked into one model:

�� = �� + �� + �� + ⋯+ �� + Ψ�� + ⋯+Ψ�� + ��,

(3)

�� =

⎝⎜⎛��…

��⎠⎟⎞ ,�� =

⎝⎜⎛��…

��⎠⎟⎞ , �� =

⎝⎜⎛��…��⎠

⎟⎞ , �� =

⎝⎜⎛��…��⎠

⎟⎞ , �� =

⎝⎜⎛��…��⎠

⎟⎞.

where�� = ��, −Λ��, �� = ��, Λ��, � = �,… �, � = ��(��) , � = ��(��). �� is the (�� + ��∗) � � (� = ∑ ��

�� ) link matrix for ith country defined by trade weights ��, so that:

��∗ � = ��.

When solving the GVAR model equations (1) and (2) are written in one equation, which is solved recursively.

Next we calculate the generalised impulse response functions (GIRFs), that were developed in Koop, Pesaran, and Potter (1996) and Pesaran and Shin (1998), and we calculate 90% bootstrap confidence bands. Because of the large number of variables in the GVAR model we cannot use the standard IRFs that assume orthogonal shocks. GIRFs do not depend on the ordering of the variables. GIRFs show how changes in Chinese GDP affect the other variables in the GVAR over time regardless of the source of the change. We do not know whether such a shock stems from a shift in the demand or supply of output in China or in other countries.

The baseline GVAR model includes five variables for each country: these are real GDP, CPI, stock price index, REER, and short-term interest rate. Furthermore, it includes oil prices as a global variable and real GDP and short-term interest rate as a foreign variable (see Eq.1). The model uses a time-varying matrix of trade flow (IMF DOTS). Also, we include a dominant unit to model oil prices with two feedback variables (real GDP and short-term interest rate).

We obtained a stable GVAR model, meaning convergent persistent profiles9, eigenvalues lying in the unit circle, and non-explosive GIRFs. To arrive with a stable GVAR we reduced the number of cointegrating relations for Argentina from 5 to 1 relation. Also we decided to remove from the sample 3 economies, that faced episodes of very high inflation, namely Venezuela, Turkey, and Bulgaria. When we included the 3 economies in the sample, many results were statistically insignificant.

In Table 6 in the Appendix we present the Johansen’s trace statistics. The order of the VARX* models as well as the number of cointegration relationships are presented in Table 8 9 Persistent profiles show the time profiles of the effects of either variable or system specific shocks on the cointegration relations in the GVAR model. The value of persistent profiles is unity on impact and, if the vector under investigation is indeed a cointegration vector, it should tend to zero as the time horizon tends to infinity.

4






4







Methods

6



�� = �� + �� + �� + ⋯+ �� + Ψ�� + ⋯+Ψ�� + ��,

(3)

�� =

⎝⎜⎛��…

��⎠⎟⎞ ,�� =

⎝⎜⎛��…

��⎠⎟⎞ , �� =

⎝⎜⎛��…��⎠

⎟⎞ , �� =

⎝⎜⎛��…��⎠

⎟⎞ , �� =

⎝⎜⎛��…��⎠

⎟⎞.

where�� = ��, −Λ��, �� = ��, Λ��, � = �,… �, � = ��(��) , � = ��(��). �� is the (�� + ��∗) � � (� = ∑ ��


��∗ � = ��.






6



�� = �� + �� + �� + ⋯+ �� + Ψ�� + ⋯+Ψ�� + ��,

(3)

�� =

⎝⎜⎛��…

��⎠⎟⎞ ,�� =

⎝⎜⎛��…

��⎠⎟⎞ , �� =

⎝⎜⎛��…��⎠

⎟⎞ , �� =

⎝⎜⎛��…��⎠

⎟⎞ , �� =

⎝⎜⎛��…��⎠

⎟⎞.

where�� = ��, −Λ��, �� = ��, Λ��, � = �,… �, � = ��(��) , � = ��(��). �� is the (�� + ��∗) � � (� = ∑ ��


��∗ � = ��.






7

in the Appendix. The cointegration results are based on the trace statistic, which has better small sample power results compared to the maximum eigenvalue statistic.

5

3. Methods

3.1.GVAR model



�� = �� + �� + Φ��,�� + ⋯+Φ��,�� + Λ��∗ + ⋯+ Λ��,��∗

+ Ψ�� + ⋯+Ψ�� + ��,

(1)






��∗ = ��

��, �� = 0,




�� = �� +��,��

��+��,��

��

��+�Λ��,��∗

��

��+�Ψ��

��

��+ ��.



�� = �� + �� + �� + ⋯+ �� + �� + ⋯+ �� + ��, (2)


5

3. Methods

3.1.GVAR model



�� = �� + �� + Φ��,�� + ⋯+Φ��,�� + Λ��∗ + ⋯+ Λ��,��∗

+ Ψ�� + ⋯+Ψ�� + ��,

(1)






��∗ = ��

��, �� = 0,




�� = �� +��,��

��+��,��

��

��+�Λ��,��∗

��

��+�Ψ��

��

��+ ��.



�� = �� + �� + �� + ⋯+ �� + �� + ⋯+ �� + ��, (2)



8















lag decay λ3: 2




8















lag decay λ3: 2




8















lag decay λ3: 2




6



�� = �� + �� + �� + ⋯+ �� + Ψ�� + ⋯+Ψ�� + ��,

(3)

�� =

⎝⎜⎛��…

��⎠⎟⎞ ,�� =

⎝⎜⎛��…

��⎠⎟⎞ , �� =

⎝⎜⎛��…��⎠

⎟⎞ , �� =

⎝⎜⎛��…��⎠

⎟⎞ , �� =

⎝⎜⎛��…��⎠

⎟⎞.

where�� = ��, −Λ��, �� = ��, Λ��, � = �,… �, � = ��(��) , � = ��(��). �� is the (�� + ��∗) � � (� = ∑ ��


��∗ � = ��.






8















lag decay λ3: 2




6



�� = �� + �� + �� + ⋯+ �� + Ψ�� + ⋯+Ψ�� + ��,

(3)

�� =

⎝⎜⎛��…

��⎠⎟⎞ ,�� =

⎝⎜⎛��…

��⎠⎟⎞ , �� =

⎝⎜⎛��…��⎠

⎟⎞ , �� =

⎝⎜⎛��…��⎠

⎟⎞ , �� =

⎝⎜⎛��…��⎠

⎟⎞.

where�� = ��, −Λ��, �� = ��, Λ��, � = �,… �, � = ��(��) , � = ��(��). �� is the (�� + ��∗) � � (� = ∑ ��


��∗ � = ��.







Methods

9







�� ,










9







�� ,










8















lag decay λ3: 2




8















lag decay λ3: 2





10

exchange rate regime (according to the classification of Klein and Shambaugh (2010), where 1 indicates a fixed and 0 – a flexible exchange rate),

trade openness (the sum of exports and imports to GDP ratio), size (GDP in PPP, constant prices), development (GDP per capita in PPP, constant prices), participation in global value chains (GVC) – with China and total, tariffs (weighted average).

As we describe in the next section, we model 40 economies, so by including all the above mentioned variables in linear regression models at the same time we would be left with just above 20 degrees of freedom, which does not appear to be acceptable. We could estimate a model with each structural characteristic separately, but that would put us at risk of getting biased estimates (due to the omitted-variable bias).

Therefore, we decided to use the Bayesian model averaging approach, in which first we estimate linear regression models with different sets of independent variables, and then we average parameters over models, with weights proportional to posterior probabilities of respective models being correct (assuming one of them is correct).13

Formally, a model-averaged Bayesian point estimator is the following:

�[��|�] ��(�)�(��|�),

where �[��|�] is the posterior distribution of a parameter ��, ��(�)� is the posterior mean of the parameter �� under model ��, and �(��|�) is the posterior probability that �� is the correct model, given that one of the models considered is correct. For more details on Bayesian model averaging see, for example, Raftery et al. (2005).

When interpreting the results we focus on posterior inclusion probabilities of each variable being in the model.

Finally, it should be noted that, on top of using two variants of the dependent variable (either from the GVAR or from the BVAR models), we also try two sets of regressors: one with a weight in trade with China, and one with proxies for it based on the gravitational model of trade – GDP in PPP and distance. The latter specification does not directly include any deterministic relationship between responses and trade weights due to the construction of the GVAR model itself. Remaining variables are used in both sets.

In the baseline model we explain responses from the GVAR model by the set of variables including GDP in PPP and distance, while the remaining three variants make up a sensitivity analysis.

13 We used the bicreg function in the BMA package in R.

10






�[��|�] ��(�)�(��|�),






9







�� ,










9







�� ,











Chapter 4

11

4. Data

Our sample consists of 59 economies, where the 19 euro area countries are grouped into one economy. We use quarterly observations. The data span from 1995Q1 to 2017Q4.

The primary data source for GDP, price level (CPI), and short term interest rate was the IMF IFS. If for any country these data were not available we used the OECD MEI database. If the data were still not available we used national sources of data, and next annual data from the IMF WEO (with Chow-Lin interpolation). In cases of data on stock market indexes the primary data source was MSCI. If the data were not available we used the data sources listed above.

The data on real effective exchange rates (REER) for each country was taken from the Bank of International Settlements (BIS). The monthly data were aggregated to quarterly frequency. An increase in the index indicates an appreciation.

Detailed data sources for each country are presented in Table 3.

Real GDP, CPI, stock market indexes, and REER are 2010 indices (2010=100). These variables are in logarithms.

Oil prices were taken from FRED, as an average from Brent, West Texas Intermediate and Dubai Fateh indexes.

We used the World Economic Outlook database of the IMF for construction of the country-specific PPP-GDP weights. We took the average from 1995 to 2017 from the annual data on purchasing power parity in billions of international dollars.

The matrixes of trade flows were constructed on the basis of International Monetary Fund statistics, namely the Direction of Trade Statistics (DOTS). These are annual data, so they allow construction of the matrix of trade flows for each year separately and subsequent estimation of the model with time-varying link matrixes. It is important to note, that the data for Chinese Taipei as a reporting country were not available. The data for Chinese Taipei were available only as a counterpart country. Thus, we made the symmetry assumption here (meaning that, for example, the value of export and import from China to Taipei equals the value of export and import from Taipei to China). Also there were no data for South Africa from 1995 to 1997 and we decided to replace the empty spaces using data from 1998.

We took data on the structure of international trade from the WTO. The structure of gross value added, measures of trade openness, size, development and tariffs were taken from the World Bank. For financial openness and exchange rate regimes we used the updated versions of databases from Chinn and Ito (2006), and Klein and Shambaugh (2008), respectively. The size of international investment positions was taken from the IMF, distances were from the Distance Calculator webpage (www.distancecalculator.net) and the participation in global value chains – from the OECD.

For each variable we computed an average over the period 1995-2015. Due to the large number of missing observations in data on structural characteristics we removed Algeria, the euro area, Israel and Taiwan from the analysis in the second part of the study.


12

Table 3. Data sources

Country real GDP price level stock price index REER short term interest rate 1 Algeria IMF WEO IMF IFS - BIS IMF IFS 2 Argentina IMF IFS + IMF WEO national MSCI + IMF IFS BIS IMF IFS 3 Australia OECD IMF IFS MSCI BIS IMF IFS 4 Brazil IMF IFS + IMF WEO IMF IFS MSCI BIS IMF IFS 5 Bulgaria IMF IFS + national IMF IFS - BIS IMF IFS 6 Canada IMF IFS + OECD IMF IFS MSCI BIS IMF IFS + OECD 7 Chile OECD + national IMF IFS MSCI BIS IMF IFS + OECD 8 China national IMF IFS MSCI BIS national14 9 Chinese Taipei national national MSCI BIS national

10 Colombia IMF IFS + OECD IMF IFS MSCI BIS IMF IFS 11 Croatia IMF IFS + IMF WEO IMF IFS - BIS IMF IFS + national 12 Czech Republic IMF IFS + IMF WEO IMF IFS MSCI + OECD BIS IMF IFS 13 Denmark IMF IFS IMF IFS MSCI BIS IMF IFS 14 euro area IMF IFS IMF IFS + OECD MSCI BIS IMF IFS + OECD 15 Hong Kong SAR IMF IFS IMF IFS MSCI BIS IMF IFS 16 Hungary IMF IFS IMF IFS MSCI BIS OECD 17 Iceland IMF IFS + IMF WEO IMF IFS OECD + IMF IFS BIS IMF IFS 18 India OECD + IMF WEO IMF IFS MSCI BIS IMF IFS + national 19 Indonesia IMF IFS + OECD IMF IFS MSCI BIS IMF IFS + OECD 20 Israel IMF IFS IMF IFS MSCI BIS OECD 21 Japan IMF IFS + OECD IMF IFS MSCI BIS IMF IFS + OECD 22 Korea IMF IFS IMF IFS MSCI BIS IMF IFS 23 Malaysia IMF IFS + national IMF IFS MSCI BIS IMF IFS 24 Mexico IMF IFS + OECD IMF IFS MSCI + OECD BIS IMF IFS 25 New Zealand IMF IFS IMF IFS MSCI BIS OECD 26 Norway IMF IFS IMF IFS MSCI BIS OECD 27 Peru IMF IFS + national IMF IFS MSCI + national BIS IMF IFS + national 28 Philippines IMF IFS + OECD IMF IFS MSCI BIS IMF IFS 29 Poland IMF IFS + OECD IMF IFS MSCI BIS IMF IFS 30 Romania IMF IFS IMF IFS - BIS IMF IFS 31 Russia IMF IFS IMF IFS MSCI + national BIS IMF IFS + OECD 32 Saudi Arabia IMF IFS + IMF WEO IMF IFS - BIS IMF IFS + national 33 Singapore IMF IFS + national IMF IFS MSCI BIS IMF IFS 34 South Africa IMF IFS + OECD IMF IFS MSCI BIS IMF IFS 35 Sweden IMF IFS IMF IFS MSCI BIS IMF IFS + OECD 36 Switzerland IMF IFS IMF IFS MSCI BIS IMF IFS + OECD 37 Thailand IMF IFS IMF IFS MSCI BIS IMF IFS 38 Turkey IMF IFS IMF IFS MSCI BIS OECD + national 39 United Kingdom IMF IFS IMF IFS MSCI BIS IMF IFS 40 United States IMF IFS IMF IFS MSCI BIS IMF IFS + OECD 41 Venezuela IMF IFS + IMF WEO IMF IFS + IMF WEO national BIS IMF IFS

14 We decided to use Lending Rate of Financial Institutions, 1 year or less from Macrobond for China.

12





10






�[��|�] ��(�)�(��|�),






10






�[��|�] ��(�)�(��|�),







Chapter 5

13

5. Empirical results and discussion

5.1.The output spillover effects from China to Southeast Asia and Oceania

In this section we describe the influence of negative demand shock in China on a set of economies. We report output elasticities for specific countries following a one percent negative GDP shock in China.

Figure 1 presents the negative demand shock in China from the GVAR model, which is equal to 1 p.p. decrease in Chinese GDP on impact, and an average response of the whole global economy and an average response of Southeast Asia and Oceania.

The negative demand shock in China is standardized to 1 p.p. (Fig.1A).The shock is permanent. The presented generalized impulse response functions are bootstrap mean estimates with the associated 90 percent bootstrap error bounds.

The maximum response of the whole global economy to 1 p.p. negative demand shock in China is equal to -0.157 p.p. after 7 quarters. The maximum response of Southeast Asia and Oceania to 1 p.p. negative demand shock in China is -0.262 p.p. after 4 quarters. Thus, the response of neighbouring countries is much stronger and faster than the average response of other economies.

Figure 1. Impulse response functions from the GVAR model

A. Negative demand shock in China B. The response of the global output to real GDP shock in China

C. The response of Southeast Asia and Oceania to real GDP shock in China

Note: bootstrap mean estimates with 90 percent bootstrap error bounds

Concerning Southeast Asia and Oceania, according to the results from the GVAR model, the strongest response of domestic GDP to negative GDP shock in China is observed in Chinese Taipei (-0.505 p.p. after 6 quarters). Next we list the countries from most to least affected: the strongest response of domestic GDP is observed in Singapore (-0.430 p.p. after 4 quarters), Indonesia (-0.383 p.p. after 8 quarters), Thailand (-0.317 p.p. after 3 quarters), Hong Kong (-0.280 p.p. after 3 quarters), Japan (-0.269 p.p. after 9 quarters), Malaysia (-0.259 p.p. after 4 quarters), Philippines (-0.246 p.p. after 5 quarters), and South Korea (-0.188 p.p. after 2 quarters). Hong Kong, Singapore and Taipei have strong financial linkages with China (see Figure 5). It is worth noting that Chinese Taipei is a smaller financial centre than Hong Kong and Singapore, but it has greater trade links with the mainland. This may be the reason for its strong GDP reaction. Indonesia is a major exporter of coal and industrial metals to China. Whereas Thailand, Malaysia, the Philippines and South Korea are major exporters of industrial supplies (includes base metals) and capital goods to China. Japan is the main Chinese export market.

A similar result is found by Inoue et al. (2015), who found that negative Chinese GDP shock impacts commodity exporters, like Indonesia, to the greatest extent, and also export


14

dependent economies, like Japan, Malaysia, Singapore and Thailand. They find a counterintuitive reaction for Philippines.

Within the studied region the weakest reaction is observed in New Zealand (-0.144 p.p. after 6 quarters) and Australia (-0.099 p.p. on impact). It may be a bit surprising, because Australia has about a third of its exports headed for China (see Figure 13).

The output reaction is statistically significant in Chinese Taipei, Hong Kong, Japan and South Korea (see Figure 2).

The obtained results are in line with Cashin et al. (2016), who reported that negative output shock has a large and statistically significant impact on all ASEAN-5 countries (except for the Philippines), with output elasticities ranging between -0.23 and -0.35 percent. Also Bussière et al. (2012) show that Asian countries benefit the most from a positive shock to Chinese imports. They reported that a one-standard-error shock to Chinese imports, which is an increase of 1.9% at the time of the impact, has an economically significant effect on other Asian countries, with real output increase in Korea, Singapore and Thailand by 0.4% after one year, and a lower but still considerable real output increase in Japan and New Zealand by 0.2%.

Figure 2. The output response of the chosen economies to negative GDP shock in China – results from the GVAR model

A. Chinese Taipei B. Hong Kong

C. Japan D. South Korea


Empirical results and discussion

15

5.2.Robustness analysis - the results from BVAR models

In order to check the robustness of our results we estimate a series of BVAR models. Specification of the models is described in Section 2.2. We check whether the difference between the impulse response functions (IRF) obtained from the GVAR model and the IRF obtained from the BVAR model for each country separately is statistically significant. To do so we subtract the IRFs and the confidence bands obtained from the two models. The results are presented in Table 4. It appears that there are considerable differences between maximum responses and maximum response horizons across the economies between the two types of models.

We find out that there are statistically significant differences between the impulse response functions for 8 out of the 37 analysed economies, that is for 22% of analysed economies (8th column of Table 4). However, if we omit the first quarter (the reaction on impact), for the following quarters the difference between the reaction obtained from the GVAR model and the reaction obtained from the BVAR model is statistically different for 4 out of 37 analysed economies, that is for 11% of the analysed economies (9th column of Table 4). This is a good result, that seem to indicate that the results from BVAR models confirm the results from the GVAR model.

Nevertheless the comparison of the two models is a bit problematic and should be analysed with caution. First, the confidence bands are quite wide (see Figure 3). Second, the GDP shock is different within the two models (compare, for example, Figure 1A and Figure 4). The shock is very similar for each of the 37 estimated BVAR models (see Figure 4). But the Chinese GDP shocks in BVAR model are much stronger than the Chinese GDP shock in GVAR model. Although the shocks are equal on impact, we standardise them in the sense that they are equal to 1 p.p. on impact.

For example, for Australia the Chinese GDP shock stabilises at the level of 1.56 p.p. in the BVAR model, whereas the shock stabilises at the level of 1.01 p.p. in the GVAR model. Note that the shocks in both specifications are permanent. The maximum response of domestic GDP in Australia is stronger in the BVAR model (-0.17 p.p.) than in the GVAR model (-0.99 p.p.).

Therefore, the differences between the IRF obtained from the two models do not necessarily stem from a different reaction of domestic GDP, but may be the result of differences in the trajectories of Chinese GDP shock.

Concerning Southeast Asia and Oceania, according to the results from the BVAR models, the strongest response of domestic GDP to negative GDP shock in China is observed in Chinese Taipei (-1.40 p.p.), and next in Singapore (-1.03 p.p.), Thailand (-0.85 p.p.), Malaysia (-0.57 p.p.), Indonesia (-0.48 p.p.), Hong Kong (-0.26 p.p.), and Australia (-0.17 p.p.). According to the results the IRFs from the BVAR model are statistically significant on impact. If we omit the first quarter, the GDP reaction becomes statistically insignificant for Hong Kong, Japan, New Zealand, the Philippines, and South Korea. In Japan the reaction is very weak. In the Philippines we find a counterintuitive positive reaction of domestic GDP to negative Chinese GDP shock.

Thus, both the GVAR and BVAR models show that Chinese Taipei is the most affected economy by negative GDP shock in China. The results for Japan from the GVAR model and


16

from the BVAR model are very different. Both specifications point to very weak and statistically insignificant output reaction in New Zealand.

Lastly, it is important to notice that both the results from the GVAR model and the results from the BVAR model show that the United States are quite severely affected by the slowdown in the Chinese economy. The maximum reaction of domestic GDP in the United States is equal to 0.2 p.p. after 11 quarters according to the GVAR model or 0.36 p.p. after 3 quarters according to the BVAR model.



17

Figure 3. The cumulative output response of the chosen economies to negative GDP shock in China – results from the BVAR models

A. Australia B. Chinese Taipei

C. Indonesia D. Malaysia

E. Singapore F. Thailand


18

Figure 4. The cumulative Chinese real GDP response to Chinese real GDP shock – results from the BVAR models for particular economies

A. Australia B. Chinese Taipei

C. Indonesia D. Malaysia

E. Singapore F. Thailand



19

Table 4. Comparison of the results from GVAR and BVAR models

GVAR BVAR

difference

difference without first

quarter max

response

max response horizon

stat. significance

max response

max response horizon

stat. significance

Algieria -0.0004 16 no -0.0007 2 yes no no

Argentina -0.0039 7 no -0.0133 4 yes no no

Australia -0.0010 0 no -0.0017 20 yes no no

Brazil -0.0048 13 yes -0.0060 20 yes no no

Canada -0.0003 6 no -0.0034 5 yes yes no

Chile -0.0005 3 no 0.0020 0 yes no no

Chinese Taipei -0.0050 6 yes -0.0140 20 yes no no

Colombia -0.0014 12 no -0.0007 20 no no no

Croatia -0.0008 17 no -0.0108 20 yes yes yes

Czech Republic -0.0012 5 no -0.0066 20 yes no no

Denmark -0.0011 7 no -0.0002 7 no no no

euro area -0.0013 4 yes -0.0025 20 yes no no

Hong Kong -0.0028 3 yes -0.0026 4 no no no

Hungary -0.0013 11 no 0.0005 0 no no no

Iceland -0.0035 0 yes -0.0059 20 yes no no

India -0.0024 11 yes -0.0001 4 no yes no

Indonesia -0.0038 8 no -0.0048 11 yes no no

Israel -0.0017 12 no -0.0031 0 yes no no

Japan -0.0024 6 yes -0.0001 0 no no no

Korea -0.0019 2 yes -0.0017 20 no no no

Malaysia -0.0026 4 no -0.0057 6 yes no no

Mexico 0.0013 13 no 0.0098 0 yes yes no

New Zealand -0.0011 6 no 0.0000 9 no yes yes

Norway -0.0001 14 no -0.0024 20 no no no

Peru -0.0002 11 no 0.0000 5 yes yes no

Philippines -0.0025 5 no 0.0007 0 no no no

Poland 0.0005 7 no 0.0006 7 no no no

Romania -0.0018 7 no -0.0017 3 no no no

Russia -0.0051 10 yes -0.0157 20 yes yes yes

Saudi Arabia -0.0069 20 yes -0.0023 4 no no no

Singapore -0.0043 4 no -0.0103 20 yes no no

South Africa -0.0005 10 no -0.0042 20 yes no no

Sweden -0.0007 2 no -0.0017 0 yes no no

Switzerland -0.0011 7 no -0.0023 6 yes no no

Thailand -0.0032 3 no -0.0085 7 yes no no

United Kingdom -0.0007 3 no -0.0032 3 yes yes yes

United States -0.0021 11 yes -0.0036 3 yes no no


20

5.3 Factors differentiating responses to a China GDP impulse

Figure (5) shows posterior inclusion probabilities of each structural characteristic being in the model from the baseline BMA analysis (based on results from GVAR, without the weight in trade with China among regressors).15 They are highest for the exchange rate regime (94.5%), GDP in PPP (98.0%), the share of manufacturing in international trade (81.1%), the size of international investment position (78.5%) and the share of manufacturing in GVA (80.0%).

Figure (6) shows the results in a different way. Rows correspond to variables and columns correspond to models. The wider the column, the higher the probability of a given model. Models are ordered so that the first one has the highest probability. If a variable enters a given model, it is in red (a positive impact) or in blue (a negative impact) in a cell. Otherwise – it is in white. When interpreting the results it should be noted that responses are to a negative impulse. The relationship between the strength of the response to a China GDP impulse is negatively related with the exchange rate regime (the less flexible the exchange rate, the stronger the response), GDP in PPP and the size of international investment position, and positively related with the share of manufacturing in international trade.

Figures (7)-(10) show the results from the remaining variants of the BMA analysis (responses from GVAR with the weight in trade with China among regressors, responses from BVARs with or without the weight in trade). Some of them give a high probability of being in the model for participation in GVC with China (a negative relationship), and measures of trade and financial openness (a negative and a positive relationship, respectively).

Averaging over the results above, among the most important factors differentiating responses to a China GDP impulse are: the exchange rate regime, the structure of the economy and its size, with more rigid exchange rates, higher shares of manufacturing in GVA and larger sizes of the economy associated with stronger spillovers.

The economies with floating exchange rates should be more resistant to a decrease in China’s GDP, because if it leads to a decrease in domestic GDP, then domestic currency should depreciate leading to a later increase in domestic GDP.

The share of industry in China’s value added was about 41% in 2015, whereas the average for all other countries in our sample was 31%, similarly the share of manufacturing in China’s value added was about 83% in 2015, whereas the average for all other countries in our sample was 67%. It means that manufacturing is the most important component of China’s trade, thus, the slowdown of Chinese economy transmits more strongly to economies with higher shares of manufacturing in GVA.

It appears that the size of economy correlates with the weight in trade with China. It means that China trades more with larger economies. Therefore larger economies are more affected with negative demand shocks from China.

15Table 9 expands abbreviations in figures (5)-(10).

20

5.3 Factors differentiating responses to a China GDP impulse

Figure (5) shows posterior inclusion probabilities of each structural characteristic being in the model from the baseline BMA analysis (based on results from GVAR, without the weight in trade with China among regressors).15 They are highest for the exchange rate regime (94.5%), GDP in PPP (98.0%), the share of manufacturing in international trade (81.1%), the size of international investment position (78.5%) and the share of manufacturing in GVA (80.0%).

Figure (6) shows the results in a different way. Rows correspond to variables and columns correspond to models. The wider the column, the higher the probability of a given model. Models are ordered so that the first one has the highest probability. If a variable enters a given model, it is in red (a positive impact) or in blue (a negative impact) in a cell. Otherwise – it is in white. When interpreting the results it should be noted that responses are to a negative impulse. The relationship between the strength of the response to a China GDP impulse is negatively related with the exchange rate regime (the less flexible the exchange rate, the stronger the response), GDP in PPP and the size of international investment position, and positively related with the share of manufacturing in international trade.

Figures (7)-(10) show the results from the remaining variants of the BMA analysis (responses from GVAR with the weight in trade with China among regressors, responses from BVARs with or without the weight in trade). Some of them give a high probability of being in the model for participation in GVC with China (a negative relationship), and measures of trade and financial openness (a negative and a positive relationship, respectively).

Averaging over the results above, among the most important factors differentiating responses to a China GDP impulse are: the exchange rate regime, the structure of the economy and its size, with more rigid exchange rates, higher shares of manufacturing in GVA and larger sizes of the economy associated with stronger spillovers.

The economies with floating exchange rates should be more resistant to a decrease in China’s GDP, because if it leads to a decrease in domestic GDP, then domestic currency should depreciate leading to a later increase in domestic GDP.

The share of industry in China’s value added was about 41% in 2015, whereas the average for all other countries in our sample was 31%, similarly the share of manufacturing in China’s value added was about 83% in 2015, whereas the average for all other countries in our sample was 67%. It means that manufacturing is the most important component of China’s trade, thus, the slowdown of Chinese economy transmits more strongly to economies with higher shares of manufacturing in GVA.

It appears that the size of economy correlates with the weight in trade with China. It means that China trades more with larger economies. Therefore larger economies are more affected with negative demand shocks from China.

15Table 9 expands abbreviations in figures (5)-(10).

12





12







21

Figure 5. Posterior inclusion probabilities – baseline

Note: Abbreviations are explained in Table 9.

Figure 6. Graphical results of BMA analysis – baseline

Note: Abbreviations are explained in Table 9.

0102030405060708090

100


Conclusions

22

Conclusions

In this study we develop the GVAR model to investigate how a slowdown in Chinese economy may affect other economies. We use a novel data set that spans from1995Q1 to 2017Q4 and covers 59 economies. We apply time-varying trade weights. Also, we estimate a set of BVAR models to check the robustness of the results.

We decided to concentrate on Southeast Asia and Oceania, because this region seems to be the most dependent from China. Indeed, our results show that the maximum response of the whole global economy to 1 p.p. negative demand shock in China is equal to -0.157 p.p. after 7 quarters, while the maximum response of Southeast Asia and Oceania is -0.262 p.p. after 4 quarters. Thus, the response of neighbouring countries is much stronger and faster than the average response of other economies.

According to our results from both the GVAR and the BVAR models Chinese Taipei is the most affected economy by negative GDP shock in China. Also Indonesia, Singapore, Hong Kong, Thailand, Malaysia are strongly affected. Australia seems to be also affected but to a lesser degree. We find a weak GDP response to China GDP shock for New Zealand and the Philippines. The response of GDP in South Korea and Japan is quite significant, but only according to the results from the GVAR model.

Thus, our results suggest that there exist strong economic linkages between the Asian economies. Busserie et al. (2012), for example, also confirm the presence of a strong Asian business cycle and an increased vertical specialisation in international trade among Asian economies.

The results from the BVAR models seem to confirm the results from the GVAR model. We find out that there are statistically significant differences between the impulse response functions in 22% of analysed economies, but if we omit the first quarter the differences appear only in 11% of the analysed economies. But there are considerable differences between maximum responses and maximum response horizons across the economies between the two types of models.

In the final step of our analysis, we used Bayesian model averaging in order to check how structural characteristics may explain the differences in the response of the analysed economies to a negative Chinese GDP shock. We found that spillovers are stronger to economies with less flexible exchange rates, a higher share of manufacturing in gross value added and to economies which are larger.


References

23

References

1. Abdullahi D. A., Rui H. (2019), Impacts of China's crash on Asia-Pacific financial integration: Volatility interdependence, information transmission and market co-movement, Economic Modelling, 79(C), 28-46.

2. Backé P., Feldkircher M., Slacík T. (2013), Economic spillovers from the euro area to the CESEE region via the financial channel: a GVAR approach, Focus on European Economic Integration, Oesterreichische Nationalbank, 4, 50–64.

3. Blagrave P.,Vesperoni E.(2018), The implications of China’s slowdown for international trade, Journal of Asian Economics, 56(C), 36-47.

4. Bussière M., Chudik A., Sestieri G. (2012), Modelling global trade flows: results from a GVAR model, Globalization and Monetary Policy Institute Working Paper 119, Federal Reserve Bank of Dallas.

5. Canova F. (2007), Methods for Applied Macroeconomic Research, Princeton University Press.

6. Cashin P., Mohaddes K., Raissi M. (2016), China's slowdown and global financial market volatility; is world growth losing out?, IMF Working Papers 16/63, International Monetary Fund.

7. Chinn M.D., Ito H. (2006), What matters for financial development? Capital controls, institutions, and interactions, Journal of Development Economics, 81(1), 163-192.

8. Chmielewski T., Kapuściński M., Kocięcki A., Łyziak T., Przystupa J., Stanisławska E., Wróbel E. (2016), Monetary policy transmission mechanism in Poland. What do we know in 2015?, NBP Working Papers 286.

9. Dees S., Di Mauro F., Pesaran M. H., Smith L. V. (2007a), Exploring the international linkages of the euro area: a global VAR analysis, Journal of Applied Econometrics, 22(1), 1–38.

10. Dees S., Holly S., Pesaran M. H., Smith L. V. (2007b), Long Run Macroeconomic Relations in the Global Economy, Economics: The Open-Access, Open-Assessment E-Journal, 1(5), 1-29.

11. Demchuk O., Łyziak T., Przystupa J., Sznajderska A., Wróbel E. (2012), Monetary policy transmission mechanism in Poland. What do we know in 2011?, NBP Working Paper 116.

12. Dieppe A., Gilhooly R., Han J., Korhonen I., Lodge D. (2018), The transition of China to sustainable growth – implications for the global economy and the euro area, Occasional Paper Series 206, European Central Bank.

13. Dieppe A., van Roye B., Legrand R. (2016), The BEAR toolbox, Working Paper Series 1934, European Central Bank.

14. Georgiadis G. (2015), Examining asymmetries in the transmission of monetary policy in the euro area: Evidence from a mixed cross-section global VAR model, European Economic Review, 75, 195-215.

15. Georgiadis G. (2016), Determinants of global spillovers from US monetary policy, Journal of International Money and Finance, 67(C), 41-61.

16. Havranek T., Rusnak M. (2013), Transmission Lags of Monetary Policy: A Meta-Analysis, International Journal of Central Banking, 9(4), 39-76.

17. Hummels D., Ishii J., Yi K.M. (2001), The Nature and Growth of Vertical Specialization in World Trade, Journal of International Economics, 54, 75-96.


24

18. Inoue T., Kaya D., Ohshige H. (2015), The impact of China’s slowdown on the Asia Pacific region : an application of the GVAR model, Policy Research Working Paper Series 7442, The World Bank.

19. Kapuściński M., Łyziak T., Przystupa J., Stanisławska E., Sznajderska A., Wróbel E. (2014), Monetary policy transmission mechanism in Poland. What do we know in 2013?, NBP Working Papers 180.

20. Kapuściński M., Kowalczyk H., Kocięcki A., Łyziak T., Przystupa J., Stanisławska E., Sznajderska A., Wróbel E. (2016), Monetary policy transmission mechanism in Poland. What do we know in 2015?, NBP Working Papers 249.

21. Klein M.W., Shambaugh J. C., (2008), The Dynamics of Exchange Rate Regimes: Fixes, Floats, and Flips, Journal of International Economics, 75(1), 70-92.

22. Koop G., Pesaran M. H., Potter S. M. (1996), Impulse response analysis in nonlinear multivariate models, Journal of Econometrics, 74(1) 119-147.

23. Osorio C., Unsal D. F. (2013), Inflation dynamics in Asia: Causes, changes, and spillovers from China, Journal of Asian Economics, Elsevier, 24(C), 26-40.

24. Peersman G., Smets F. (2001), The monetary transmission mechanism in the euro area: more evidence from VAR analysis, Working Paper Series 91, European Central Bank.

25. Pesaran M. H., Schuermann T., Weiner S. (2004), Modeling regional interdependencies using a global error-correcting macroeconometric model, Journal of Business & Economic Statistics, 22, 129–162.

26. Pesaran M. H., Shin Y. (1998), Generalized impulse response analysis in linear multivariate models, Economics Letters, 58(1), 17-29.

27. Raftery A. E., Painter I. S., Volinsky C. T.(2005), BMA: An R package for Bayesian Model Averaging, R News, 5/2.

28. Smith L., Galesi A. (2014), GVAR Toolbox 2.0., University of Cambridge: Judge Business School.

29. Sznajderska A. (2019) The role of China in the world economy: evidence from a global VAR model, Applied Economics, 51, 15, 1574-1587.


Appendix

Appendix

25

Appendix

Varia

ble

Stat

istic

Criti

cal V

alue

Alge

riaAr

gent

ina

Aust

ralia

Braz

ilCa

nada

Chile

Chin

aCh

ines

e Ta

ipei

Colo

mbi

aCr

oatia

Czec

h Re

publ

icDe

nmar

keu

ro a

rea

Hong

Kon

g SA

RHu

ngar

yIc

elan

dIn

dia

Indo

nesi

aIs

rael

Japa

nKo

rea

Mal

aysi

aM

exic

oNe

w Z

eala

ndNo

rway

Peru

Phili

ppin

esPo

land

Rom

ania

Russ

iaSa

udi A

rabi

aSi

ngap

ore

Sout

h Af

rica

Swed

enSw

itzer

land

Thai

land

Unite

d Ki

ngdo

mUn

ited

Stat

es

yAD

F-3

.45

-0.5

6305

-2.0

2626

-2.1

4632

0.13

8586

-1.9

8606

-2.1

2238

-1.1

8316

-2.3

2435

-2.2

486

-1.4

2542

-2.2

0063

-2.2

3438

-2.1

8847

-2.7

3636

-1.4

8887

-1.7

4949

-1.9

7945

-2.2

5527

-2.6

2295

-3.0

7139

-2.1

8634

-3.8

4153

-1.6

7592

-1.6

6425

-3.2

5315

-1.7

6482

-1.8

5018

-2.5

314

-1.9

7509

-1.1

0124

-2.5

7668

-2.5

4108

-1.0

4857

-2.0

7035

-2.7

7041

-2.4

4147

-2.1

7073

-2.4

1915

yW

S-3

.24

-1.0

017

-2.2

7424

0.12

1781

-0.9

0601

-1.5

8285

-2.0

6708

-1.5

8907

-2.0

5806

-1.3

7453

-0.5

975

-2.4

5512

-1.2

4634

-1.7

8221

-2.7

9334

-1.6

579

-1.6

0462

-1.5

2267

-1.6

2848

-2.6

0118

-2.9

5365

-2.0

1961

-4.0

2447

-1.9

5494

-1.7

111

-0.1

4353

-0.9

0416

-1.4

6791

-1.2

0272

-1.5

8139

-1.2

6933

-1.9

6607

-2.7

6898

-1.4

623

-2.0

0414

-3.0

0163

-1.9

9187

-1.7

949

-0.8

4855

DyAD

F-2

.89

-1.6

0831

-5.3

7323

-6.0

6097

-3.1

7369

-4.9

6792

-5.4

6225

-2.3

806

-6.1

2263

-4.4

3764

-5.0

0561

-3.9

7673

-5.6

7412

-3.9

16-4

.558

37-4

.407

73-7

.135

83-5

.518

71-4

.930

39-4

.957

17-6

.191

26-5

.659

32-5

.433

41-6

.840

58-6

.642

97-8

.222

01-5

.336

76-5

.314

93-7

.490

37-3

.401

24-4

.560

85-7

.928

85-6

.252

84-4

.150

32-4

.917

35-4

.516

67-6

.092

24-4

.827

14-4

.029

52Dy

WS

-2.5

5-1

.672

19-5

.347

2-6

.007

22-3

.334

25-5

.032

52-5

.447

03-2

.531

05-6

.222

17-4

.617

84-5

.160

47-4

.168

2-5

.834

26-4

.107

21-4

.637

86-4

.510

69-7

.308

61-5

.687

4-5

.113

66-4

.928

58-6

.171

38-5

.812

29-5

.407

13-6

.684

66-6

.796

18-8

.162

51-5

.496

61-5

.493

74-7

.397

77-3

.584

49-4

.746

3-8

.098

17-6

.116

47-4

.308

7-5

.208

56-4

.698

85-6

.274

75-5

.041

7-4

.219

66p

AD

F-2

.89

0.09

852.

5678

14-0

.452

28-0

.676

95-0

.746

18-1

.375

480.

0550

21-0

.712

34-3

.530

1-2

.357

74-3

.033

64-2

.580

19-1

.527

43-0

.144

3-4

.227

48-0

.480

73-0

.085

39-2

.722

07-4

.022

78-1

.178

08-3

.140

26-0

.572

86-1

.166

59-0

.580

66-0

.186

74-1

.983

04-2

.407

45-3

.008

99-4

.959

23-3

.201

770.

2435

68-0

.040

73-0

.422

-0.2

4165

-1.7

9369

-1.8

5083

0.65

0803

-1.4

3838

p

WS

-2.5

51.

5272

671.

2677

71.

6014

981.

4676

581.

8820

271.

6756

581.

0282

231.

7337

451.

4448

430.

5889

241.

3437

590.

9904

141.

1377

82-0

.317

711.

7778

580.

2482

21.

6923

641.

3647

041.

8070

59-1

.477

851.

8480

452.

2141

941.

5976

451.

2330

682.

5963

51.

5187

011.

1951

061.

5732

70.

7019

322.

1888

6-0

.327

760.

2725

61.

3516

740.

8018

280.

3813

811.

3842

791.

2451

141.

6612

79Dp

ADF

-2.8

9-5

.290

41-4

.396

9-5

.280

21-5

.637

08-7

.311

28-4

.900

53-3

.664

07-7

.366

44-2

.846

07-3

.096

2-3

.010

07-4

.278

04-4

.191

37-3

.383

36-3

.612

15-3

.607

85-5

.449

49-5

.356

85-3

.945

18-5

.378

8-5

.062

71-6

.636

6-4

.833

35-5

.137

15-7

.046

61-3

.274

88-4

.500

15-3

.134

15-2

.842

4-5

.103

3-2

.501

17-3

.744

87-4

.678

26-4

.843

7-5

.103

04-5

.101

48-4

.451

16-6

.208

69Dp

WS

-2.5

5-2

.893

22-4

.487

78-5

.075

47-2

.987

63-7

.504

34-4

.296

5-2

.607

52-7

.463

47-0

.140

59-3

.105

76-2

.967

81-4

.418

33-4

.322

19-3

.013

760.

6855

82-3

.678

16-5

.385

76-5

.572

33-3

.973

84-5

.487

72-5

.041

48-6

.793

163.

8357

18-5

.320

34-7

.235

6-2

.554

35-4

.226

88-0

.117

49-3

.025

84-3

.288

36-2

.622

65-3

.936

55-4

.856

21-4

.988

38-5

.158

58-4

.862

48-4

.601

72-6

.386

63s

ADF

-2.8

90.

0131

04-1

.188

78-1

.299

59-1

.514

28-0

.368

04-0

.759

35-1

.784

08-0

.815

-1.1

725

-1.5

6932

-0.8

4737

-2.6

321

-0.5

0826

-2.2

7994

-0.5

0826

-0.4

8108

-2.0

3729

-1.8

146

-0.7

6681

-0.9

2199

-1.7

7272

0.23

0701

-1.0

9801

-0.6

7341

-0.4

3835

-1.7

7494

-1.9

319

-1.1

3037

-0.1

7258

-1.4

497

-1.9

8057

-0.6

4032

-1.3

173

-1.0

5712

s W

S-2

.55

0.37

9242

1.09

1109

0.38

7863

0.70

2615

-0.1

0558

-1.1

5689

-2.0

2303

-0.3

7095

-0.6

0665

-0.6

6779

-0.2

0912

0.96

0474

-0.2

7894

-2.3

9764

-0.2

7894

-0.4

7197

-0.0

4685

-2.0

4365

-1.0

0304

-1.2

5452

0.96

4628

1.40

0071

-0.1

4508

-0.1

8755

-0.9

069

-0.0

9762

-0.8

2941

-1.2

1864

1.12

6884

0.19

5668

0.55

7214

-0.9

5899

1.07

7303

1.18

6055

DsAD

F-2

.89

-6.6

2605

-6.0

404

-6.6

878

-6.8

4568

-5.7

4954

-5.9

9187

-7.1

054

-5.3

6357

-5.8

5117

-5.8

2504

-6.3

1593

-3.9

332

-6.6

6297

-3.5

8237

-6.6

6297

-6.5

3629

-5.7

601

-6.0

3378

-6.1

6184

-5.2

941

-6.2

7274

-5.8

2419

-6.2

9015

-6.4

7704

-6.1

1065

-3.9

3285

-5.9

9211

-5.8

0818

-7.0

9499

-5.5

4825

-5.3

6932

-5.7

7242

-5.3

7239

-5.8

9565

DsW

S-2

.55

-6.7

9067

-6.1

0502

-6.8

7144

-7.0

0219

-5.8

8097

-6.1

7619

-7.0

3048

-5.4

4455

-5.8

17-6

.003

29-6

.439

5-3

.735

9-6

.795

52-3

.779

23-6

.795

52-6

.700

61-5

.773

2-6

.217

42-6

.347

09-5

.463

92-6

.153

08-5

.948

49-6

.454

09-6

.560

96-6

.245

41-3

.653

45-6

.158

43-5

.991

34-7

.284

81-5

.673

22-5

.472

29-5

.947

85-5

.344

77-5

.921

57r

ADF

-2.8

9-0

.924

96-1

.339

95-1

.922

67-2

.066

26-1

.567

62-2

.626

44-1

.603

36-1

.583

16-1

.948

56-1

.494

4-1

.716

18-2

.581

02-2

.285

94-1

.171

07-1

.839

49-1

.493

26-3

.443

92-2

.766

29-1

.462

15-1

.629

97-2

.943

46-2

.456

63-2

.420

62-2

.011

87-1

.869

59-2

.357

86-1

.282

97-2

.987

69-2

.081

24-1

.981

4-1

.483

03-1

.090

8-2

.459

28-2

.003

11-2

.194

88-2

.720

97-1

.563

22-2

.012

18r

WS

-2.5

5-1

.012

7-1

.129

71-1

.802

58-2

.223

9-1

.740

63-2

.842

8-0

.485

34-0

.436

03-2

.182

95-1

.578

23-0

.077

81-2

.681

37-2

.074

76-1

.607

38-0

.816

13-1

.811

87-3

.566

23-2

.737

79-1

.782

17-1

.071

07-2

.972

86-1

.987

57-0

.683

3-2

.247

1-2

.116

91-2

.527

86-1

.480

67-1

.824

1-1

.303

76-1

.786

71-1

.764

11-1

.481

93-2

.120

28-1

.093

47-2

.056

64-2

.834

05-1

.776

05-2

.069

72Dr

ADF

-2.8

9-7

.878

14-6

.871

93-6

.587

14-6

.640

34-6

.582

26-7

.632

52-6

.888

49-6

.374

82-7

.148

97-7

.548

17-6

.826

26-5

.935

03-5

.417

26-5

.709

06-8

.787

69-5

.283

32-5

.032

61-6

.602

02-6

.610

01-4

.466

49-7

.359

47-6

.476

73-4

.376

79-5

.916

07-6

.466

03-6

.153

93-6

.924

76-7

.244

21-5

.978

38-6

.587

8-6

.400

7-4

.600

24-6

.310

23-5

.988

86-6

.644

36-7

.129

08-5

.418

52-6

.197

62Dr

WS

-2.5

5-7

.371

52-7

.061

16-6

.766

69-6

.828

75-6

.739

82-7

.610

52-7

.074

49-6

.377

7-7

.306

91-7

.723

56-7

.056

78-5

.949

81-5

.594

74-5

.885

71-8

.682

55-5

.464

22-4

.697

9-6

.831

81-6

.796

91-4

.645

13-7

.544

36-6

.645

52-4

.489

79-6

.089

04-6

.649

61-6

.341

67-7

.106

96-7

.423

44-5

.951

84-6

.042

8-6

.520

38-4

.777

28-6

.494

39-5

.842

26-6

.799

86-7

.352

41-5

.556

19-6

.246

96i

ADF

-2.8

9-2

.933

94-2

.943

13-1

.605

37-4

.221

45-2

.132

4-1

.497

36-4

.271

95-1

.618

66-2

.335

65-3

.670

2-1

.279

25-2

.409

01-2

.462

11-1

.705

58-3

.631

69-2

.465

85-4

.575

2-3

.407

61-1

.557

51-2

.098

65-1

.870

23-2

.211

05-5

.042

-1.5

9636

-2.2

5573

-2.2

5715

-1.3

2991

-2.0

9339

-1.1

3037

-4.3

3642

-1.7

4022

-2.4

2891

-2.0

4962

-2.9

6827

-2.1

3134

-2.8

6619

-1.2

5416

-2.2

4472

i W

S-2

.55

-0.1

1843

-3.1

5384

-0.9

7933

0.25

6774

-1.4

4027

-1.0

8907

-0.9

0666

-0.5

7756

-1.3

6021

-0.8

6615

-0.7

7861

-1.4

108

-1.3

4762

-1.3

2901

0.95

0042

-2.6

8835

-3.4

578

-3.6

3503

0.53

3674

1.53

3902

-0.6

2316

-2.2

7817

1.29

4027

-0.9

9564

-2.3

4347

-2.0

9563

-0.9

8816

-0.2

6435

-1.2

153

2.68

7955

-1.2

999

-2.3

8283

-2.1

5502

-1.0

7977

-1.2

0415

-2.3

5585

-1.3

737

-1.9

5248

DiAD

F-2

.89

-4.9

1898

-8.6

0476

-5.5

6435

-5.6

2196

-4.6

5951

-7.4

0416

-4.6

2428

-5.5

0936

-5.9

4304

-6.5

7106

-5.8

3303

-5.1

9296

-4.5

2578

-7.4

1879

-5.0

5783

-3.7

1081

-8.5

7602

-4.3

6089

-7.6

527

-8.5

9926

-4.4

0754

-5.7

2458

-5.5

1492

-6.8

1704

-5.6

5779

-9.2

1987

-7.3

022

-4.4

7055

-13.

4097

-11.

1855

-6.6

0088

-5.2

0093

-5.9

6941

-5.3

7966

-6.3

0847

-5.5

5461

-5.4

716

-3.3

9118

DiW

S-2

.55

-5.0

7683

-8.7

528

-5.7

4344

-4.2

3189

-4.4

3172

-7.5

9569

-4.6

4989

-5.6

9565

-5.7

0314

-5.6

0849

-5.9

891

-5.3

6608

-4.7

0972

-7.6

0875

-4.7

561

-3.9

4613

-8.4

2789

-4.5

4699

-7.5

4682

-0.3

2344

-4.4

2615

-5.8

3558

-2.9

7248

-7.0

0481

-5.8

4238

-9.4

1578

-7.4

6182

-4.5

9223

-13.

6184

-0.5

2319

-6.7

1666

-5.3

8286

-5.9

9954

-5.1

9538

-5.9

8454

-5.4

0783

-5.1

95-3

.590

79

Tabl

e 5.

Uni

t Roo

t Tes

ts fo

r the

Dom

estic

Var

iabl

es a

t the

5%

Sig

nific

ance

Leve

l

Note

: ADF

- A

ugm

ente

d D

icke

y-Fu

ller a

nd W

S - W

eigh

ted-

Sym

met

ric a

ugm

ente

d D

icke

y-Fu

ller


Country

Algeria

Argenti

naAus

tralia

Brazil

Canada

Chile

China

Chinese

Taipei

Colomb

iaCro

atiaCze

ch Repu

blicDen

mark

euro are

aHon

g Kong

Hungary

Iceland

India

Indonesi

aIsra

elJap

anKor

eaMa

laysia

Mexico

New Zea

landNor

wayPer

uPhil

ippines

Poland

Romania

Russia

Saudi A

rabia

Singapo

reSou

th Africa

Sweden

Switzer

landTha

ilandUni

ted Kin

gdomUn

ited Sta

tes# en

dogeno

us varia

bles4

55

55

55

55

45

55

55

55

55

55

55

55

55

54

54

55

55

55

5# fo

reign (s

tar) var

iables

33

33

33

33

33

33

33

33

33

33

33

33

33

33

33

33

33

33

33

r=0126

.11962

97.9466

156.864

6202.8

353190

.20952

01.2037

198.022

156.830

6357.7

162151

.3129

231.139

204.060

7229.6

903210

.525307

.80652

20.6179

149.114

3159.5

346221

.81511

35.2019

220.786

1184.8

024396

.23851

50.5279

141.063

259.518

3193.2

418391

.36362

69.5869

441.491

3146.8

803177

.79392

16.9671

171.565

1185.9

868201

.40041

55.8255

179.613

r=172.5

243153

.74649

8.58011

107.767

9113.4

221117

.66231

10.5116

93.9540

6141.4

73884.4

45571

16.9844

115.133

1134.8

075115

.51991

04.7392

121.120

396.5

48798.6

03331

06.4131

85.8397

7145.9

96199.0

41721

03.2684

85.2180

884.70

667151

.12671

25.4921

172.759

469.9

344195

.96824

6.25959

88.3712

1137.1

651100

.13341

27.0712

113.294

580.92

692106

.7977

r=241.7

70879

7.53315

54.0949

744.80

50948.8

1454

69.2241

55.1414

255.50

69775.7

27225

1.12187

48.2899

561.69

24671.5

68970.4

58675

1.64525

74.8501

859.71

94850.5

03495

2.42621

53.6117

76.0043

51.9805

155.57

70749.6

93424

9.94531

68.5481

968.32

32787.5

5609

39.5227

117.132

624.76

61247.8

97717

3.70986

45.3087

480.67

02666.4

29244

3.74712

59.8967

2r=3

18.2131

458.52

58224.3

00342

4.15322

26.3841

638.98

75629.7

92652

7.54733

35.411

20.3680

322.87

46135.0

0775

36.5363

31.5636

527.54

70836.7

53023

5.93085

21.7118

28.5182

727.70

67942.9

30322

4.07398

26.6521

423.98

57321.6

43263

5.32509

36.3994

142.45

82816.9

60055

5.32562

5.28726

517.83

23728.3

64332

4.53483

41.0844

334.72

02124.2

41572

2.89987

r=424.4

55887

.828242

8.98886

510.85

83714.3

76451

1.02284

7.20250

915.55

7665.47

7671

12.1599

14.6859

810.10

0967.87

80021

2.88813

15.0531

58.932

48312.6

34311.3

54481

4.18489

10.2964

28.367

67710.3

1651

6.25624

14.0580

18.044

97218.7

1764

9.14663

27.52

2827

4.51725

9.56174

910.86

6758.91

18136

.916221

10.7649

6

Country

Algeria

Argenti

naAus

tralia

Brazil

Canada

Chile

China

Chines

e Taipei

Colomb

iaCro

atiaCze

ch Repu

blicDen

mark

euro are

aHon

g Kong

Hun

garyIcel

andInd

iaInd

onesia

Israel

Japan

Korea

Malays

iaMe

xicoNew

Zealan

dNor

wayPer

uPhil

ippines

Poland

Romania

Russia

Saudi A

rabia

Singapo

reSou

th Africa

Sweden

Switzer

landTha

ilandUni

ted Kin

gdomUn

ited Sta

tes# en

dogeno

us varia

bles4

55

55

55

55

45

55

55

55

55

55

55

55

55

54

54

55

55

55

5# fo

reign (s

tar) var

iables

33

33

33

33

33

33

33

33

33

33

33

33

33

33

33

33

33

33

33

r=091.

81122

.96122

.96122

.96122

.96122

.96122

.96122

.96122

.9691.

81122

.96122

.96122

.96122

.96122

.96122

.96122

.96122

.96122

.96122

.96122

.96122

.96122

.96122

.96122

.96122

.96122

.96122

.9691.

81122

.9691.

81122

.96122

.96122

.96122

.96122

.96122

.96122

.96r=1

64.54

91.81

91.81

91.81

91.81

91.81

91.81

91.81

91.81

64.54

91.81

91.81

91.81

91.81

91.81

91.81

91.81

91.81

91.81

91.81

91.81

91.81

91.81

91.81

91.81

91.81

91.81

91.81

64.54

91.81

64.54

91.81

91.81

91.81

91.81

91.81

91.81

91.81

r=241.

0364.

5464.

5464.

5464.

5464.

5464.

5464.

5464.

5441.

0364.

5464.

5464.

5464.

5464.

5464.

5464.

5464.

5464.

5464.

5464.

5464.

5464.

5464.

5464.

5464.

5464.

5464.

5441.

0364.

5441.

0364.

5464.

5464.

5464.

5464.

5464.

5464.

54r=3

20.98

41.03

41.03

41.03

41.03

41.03

41.03

41.03

41.03

20.98

41.03

41.03

41.03

41.03

41.03

41.03

41.03

41.03

41.03

41.03

41.03

41.03

41.03

41.03

41.03

41.03

41.03

41.03

20.98

41.03

20.98

41.03

41.03

41.03

41.03

41.03

41.03

41.03

r=420.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

9820.

98

Tabl

e 6.

Det

aile

d Co

inte

grat

ion

Resu

lts fo

r the

Tra

ce S

tatis

tic a

t the

5%

Sig

nific

ance

Leve

l

Tabl

e 7.

Crit

ical V

alue

s for

Tra

ce S

tatis

tic a

t the

5%

Sig

nific

ance

Leve

l (M

acKi

nnon

, Hau

g, M

ichel

is, 1

999)


Appendix

27

Table 8. Lag orders and number of cointegrating relations for country specific VARX*(p,q) models in the baseline model

country VARX* order number of coint.

relations p q

Algeria 1 1 2 Argentina 1 1 1 Australia 2 1 2 Brazil 1 1 2 Canada 1 1 2 Chile 1 1 3 China 1 1 2 Chinese Taipei 1 1 2 Colombia 1 1 3 Croatia 1 1 3 Czech Republic 1 1 2 Denmark 1 1 2 euro area 1 1 3 Hong Kong 1 1 3 Hungary 1 1 2 Iceland 1 1 3 India 1 1 2 Indonesia 2 1 2 Israel 1 1 2 Japan 1 1 1 Korea 1 1 4 Malaysia 1 1 2 Mexico 1 1 2 New Zealand 1 1 1 Norway 1 1 1 Peru 1 1 3 Philippines 1 1 3 Poland 1 1 3 Romania 1 2 2 Russia 1 1 4 Saudi Arabia 1 1 1 Singapore 1 1 1 South Africa 1 1 3 Sweden 1 1 2 Switzerland 1 1 3 Thailand 1 1 3 United Kingdom 1 1 1 United States 1 1 2


28

Table 9. Expansions of abbreviations in figures (5)-(10)

Abbreviation Variable agr_int_tra Share of agriculture in international trade agr_gva Share of agriculture in GVA chinn_ito Chinn-Ito index exc_rat_reg_kspeg Exchange rate regime fue_min_int_tra Share of fuels and mining products in international trade gdp_per_cap_ppp_con GDP per capita, PPP, constant prices gvc_chi Participation in GVC with China gvc_wor Participation in GVC, total ind_gva Share of industry in GVA man_int_tra Share of manufacturing in international trade man_gva Share of manufacturing in GVA ser_gva Share of services in GVA tariff_wei_mea Tariffs, weighted mean Weight Weight in trade with China exp_imp_gdp Sum of exports and imports in relation to GDP iip_ass_lia_gdp Sum of IIP assets and liabilities in relation to GDP distance Distance between capital of respective economies and

Beijing gdp_ppp_con GDP, PPP, constant prices

Figure 7. Posterior inclusion probabilities – alternative

0102030405060708090

100

GVAR, with weight in trade with China

BVAR, without weight in trade with China

BVAR, with weight in trade with China


Appendix

29

Figure 8. Graphical results of BMA analysis – alternative 1 (GVAR, with weight in trade with China)

Figure 9. Graphical results of BMA analysis – alternative 2 (BVAR, without weight in trade with China)


30

Figure 10. Graphical results of BMA analysis – alternative 3 (BVAR, with weight in trade with China)


Appendix

31

Figure 11. Counterparties resident in China. Total assets and liabilities for all sectors (USD millions) located in BIS reporting country in 2018.

Figure 12. The sum of exports and imports from China (USD millions)

Source: own calculations based on BIS locational statistics (see Backé et al. 2013)

Source: own calculations based on Direction of Trade Statistics, IMF; sum of goods, value of exports, FOB and goods, value of imports, CIF

Figure 13. Exposure to China. Percentage of exports to China in 2017.

Figure 14. Real GDP growth rate in China

Source: own calculations based on Direction of Trade Statistics, IMF; sum of goods, value of exports, FOB and goods, value of imports, CIF

Source: own calculations based on China GDP Total (at Constant Price, 2015), CNY series from Macrobond.

0

150000

300000

450000

600000

750000

Hon

g K

ong

SAR

euro

are

aU

nite

d St

ates

Uni

ted

Kin

gdom

Japa

nCh

ines

e Ta

ipei

Kor

eaA

ustra

liaCa

nada

Switz

erla

ndD

enm

ark

Swed

enPh

ilipp

ines

Braz

ilCh

ileM

exic

o

www.nbp.pl

the spillover effects of chinese economy on southeast asia ... · the chinese economy is maturing...

Documents