Hodrick-Prescott Filtering of Large Emerging Economies and Decoupling Hypothesis

Abstract

The aim of this chapter is to summarize our researches about economic growth in large emerging economies: Russia, India, and China. There were launched back in 2014. Since the 2008 crisis, there has been speculation that large emerging economies have built up sufficient economic capacity and that emerging economies’ economic cycles have become more independent of developed economies. However, our studies have shown that at the time of 2014–2015, the economic growth of large developing economies had too different qualitative characteristics to speak about the synchronization of economic cycles and confirm the decoupling hypothesis. In addition, we predicted a slowdown in China and extremely weak economic growth in Russia as early as 2014. However, the Russian-Ukrainian conflict and unprecedented anti-Russian sanctions artificially led to greater cooperation and communication between large emerging economies. Contrary to expectations, the Russian economy has shown its resilience, and economic ties between China and Russia, and India and Russia have strengthened. The main result of this research is that we have shown that, in addition to the indicator of economic growth, its qualitative characteristics are much more important. Until international cooperation occurs on a qualitative basis of economic growth, the decoupling hypothesis cannot be confirmed.

1. Introduction

Global trade and integration are the main drivers of global economic growth. In the last decade, the global economy has faced a slowdown in economic growth and a decrease in the integration level, but global trade and integration have promoted a better flow of technology that ensures avoid gaps in economic development between developed and developing countries. Despite the fact that in the last 5 years the trend of globalization has changed to the trend of geoeconomic fragmentation, the issue of the effectiveness of creating economic unions has remained relevant, and only the point of view has changed. The presence of synchronization between the economic cycles of the countries of a geoeconomic union is a generally accepted indicator to determine the presence of integration and the efficiency of a geoeconomic union [1, 2, 3, 4, 5].

The large emerging economies: Russia, India, China (hereinafter—the “LEE”) and their geoeconomic alliance is the object of our study. Russia, India, and China are emerging market countries. The role of emerging markets has grown significantly over the past few decades, and they are playing a significant role in the global economy [6]. Restrictive measures in China due to the coronavirus pandemic, as well as the lifting of restrictions, cause surges in resource prices around the world and peak loads in the global logistics system [7]. Kremer et al. [8] note that the decline in medium-term global growth reflects a slower rate of change in the progress of improving living standards in countries such as China and Korea. As a result, since the previous global crisis in 2008, there has been a debate about the separation of the economic development of emerging markets from developed markets, the so-called “decoupling hypothesis” [6, 9, 10, 11]. Before the 2008 crisis, most of the literature supported the decoupling hypothesis [12, 13]. Since the 2008 crisis, there have been many studies showing a reduction or disappearance of its effect [14, 15]. The trade wars between the United States and China also contribute against support for the decoupling hypothesis.

The topic of economic growth in the context of high risks of fragmentation of the global economy has great relevance. A number of researchers support the existence of spillover effects arising from alliances [12, 16], while others find no such evidence [17, 18].

In this paper, we investigated the output gaps of large emerging economies: Russia, India, and China. We chose the output gap as an indicator of the economic growth efficiency of national economies. We used the Hodrik-Preskott filtering method to isolate the output gap. Next, we conducted a spectral analysis to determine the degree of synchronization of economic cycles. A similar approach was taken by Papageorgiou et al. [4], and Frankel and Rose [2], etc., against European Union countries.

In the second part, we investigated the output gaps of LEE, having isolated it by the statistical method of Hodrik-Preskott filtering rather than by calculation one. This method allows us to determine positive and negative gaps between real and potential gross domestic product (GDP). We calculated output gaps in US dollars at 2015 prices, in local currency at constant prices, in US dollars at current prices, and in local currency at current prices. This allowed us to identify the inflationary component and the component related to the exchange rate of national currencies. We applied spectral analysis to determine the degree of synchronization of business cycles.

In the third part, we discussed statistical results of the study, and in the fourth part, we tried to find explanations for these results and make conclusions.

2. From globalization to geoeconomic fragmentation

Global trade and integration are the driving forces of global economic growth, but in the last decade, one of the main problems of the global economy has been a slowdown in economic growth and geoeconomic fragmentation that has replaced the trend of globalization. The main events that have led to increased geoeconomic fragmentation are those related to BREXIT; trade disputes between China and the United States, Russian invasion of Ukraine.

Trends in global investment have shifted to reshoring and friendshoring, resulting in a slowdown in technology transfer between developed and developing countries [19, 20, 21]. Under such conditions, China’s economic growth will depend on the state of international trade [7]. At the same time, China accounts for a quarter of exports from Asia, and the removal of COVID restrictions in China causes positive spillover effects for countries with strong trade ties with it [22]. At the same time, the decline in medium-term global growth reflects a slowdown in the rate of improvement in living standards in countries such as China and Korea [8].

At the same time, while there are quite a lot of studies of the impact of China’s economic situation on the global economy, in Russia there is a huge gap, which is further exacerbated by the closeness of Russian statistics.

After the introduction of anti-Russian sanctions, in particular the European hydrocarbon embargo, the economic and trade interaction between large developing economies has increased significantly. India and China account for 70% of Russian oil exports. Russia has increased oil supplies to India by 22 times. Russia completely redirected supplies of petroleum products from the European market to India and China. In 2022, Russia supplied China with a third of its total oil exports. Approximately 29% of the Russian budget is formed through energy trade with India and China. In fact, this speaks of the highest degree of integration of large emerging economies. However, we assume that such integration has no economic basis and is of an administrative nature. This has consequences in terms of the instability of such relations because such integration can also be stopped by administrative means. It is impossible to talk about high-quality and inclusive economic growth based on such integration.

3. Methods

3.1 Decoupling hypothesis

Prior to the 2008 crisis, there were many studies showing that emerging markets had sustained economic growth, had built up sufficient economic and technological capacity, and that their economic cycles had become more independent of those of developed countries—the decoupling hypothesis [23]. Since the 2008 crisis, however, developed countries have changed their monetary policy substantially and have become more focused on stimulating their productive forces. The trade wars between the United States and China, which began in 2018, allow us to talk about direct restrictions in the exchange of technology between the United States and China.

At the same time, China and other major developing countries are facing a slowdown in economic growth. As a result of these processes, emerging markets are facing capital outflows and devaluation of national currencies.

It is also important to distinguish between the issue of the synchronicity of economic cycles and the fragmentation of the economies of the global economy. From our perspective, the existence of the synchronicity of economic cycles is objective in nature. The synchronicity of economic cycles confirms the interaction of the economic systems of national economies and confirms the decoupling hypothesis. In addition, the fragmentation of economies can be caused by various geopolitical events, such as sanctions and various prohibitive measures that impede global trade and technology exchange. Now we see a trade war between the United States and China, anti-Russian sanctions, and an almost complete termination of free trade between Russia and the United States, Russia, and Europe. It is obviously a powerful geoeconomic fragmentation of the LEE. Nevertheless, whether their business cycles are synchronized remains a big question.

3.2 Hodriсk-Preskott filtering method

Hodriсk-Preskott filtering is a simple and straightforward method to extract the trend and the cyclic component of a time series. We considered the GDP data as a time series and, due to isolation of the cyclical component, determined the difference between actual and potential GDP or output gap, in other words.

By “trend,” we mean a certain steady, systematic change over a long period. However, no matter how long the series is, we can never be sure that the trend is not just a part of a slow fluctuation. After having separated the trend from seasonal fluctuations, the remainder of the series is a function of cyclical fluctuations. Seasonal fluctuations are the easiest to detect, isolate, and study.

When defining a trend, we understand that any movement observed over a sufficiently long period can be smoothed. It means that, at least locally, the component corresponding to the trend can be expressed by a polynomial of time.

Thus, in our case, GDP is a trend and fluctuations around this trend [24, 25]:

$$y_t=y_t^g+y_t^c,$$E1

where $y_t^g$ is a trend or structural component of the time series and $y_t^{с}$ is the cyclical component of the time series.

We imposed a minimization condition on the cyclic component to obtain a smoothed series:

$$\sum _{t=0}^{\infty }{\left(y_t^c\right)}^2+\lambda \sum _{t=0}^{\infty }{\left[\left(y_{t+1}^g-y_t^g\right)-\left(y_t^g-y_{t-1}^g\right)\right]}^2\to \mathit{min}$$E2

where $\lambda$ is a Lagrange multiplier. For annual data, $\lambda =100$.

After elimination of the trend, we investigated the reminder of the series by spectral analysis methods.

3.3 Spectral analysis

To investigate the reminders of the time series, we applied spectral analysis techniques to check for correlations between time series members and to determine the period of major fluctuations in the reminders of the time series [26].

Suppose there is autocorrelation for any pair of values:

$${\rho }_j=\frac{\mathit{cov}\left(u_t,u_{t-j}\right)}{{\sigma }^2},$$E3

where ${\rho }_j$ is the correlation between members of the time series after filtering by j,$\mathit{cov}\left(u_t,u_{t-j}\right)$ is the covariance between members of the time series after filtering by j,${\sigma }^2$ is the dispersion of the rest of the time series.

The total sum of coefficients ${\rho }_0,{\rho }_1,{\rho }_2,\dots$ is called the correlogram of the series. By determining the correlation of the other series gradually, without the components of the main trend, we can build a correlogram that allows us to graphically trace the interdependence between the members of the time series.

Then during studying of various kinds of periodic processes (we mean processes repeated over a certain period, including economic processes), it is best to decompose periodic functions describing these processes into trigonometric series. The simplest periodic functions are trigonometric functions $sinx$ and $cosx$. The period $T$ of these functions is $2\pi$.

The simplest periodic process is a simple harmonic oscillation described by a function:

$$y=A\ast \mathit{sin}\left(\mathit{at}+{\phi }_0\right)$$E4

$$t\ge 0$$E5

where $A$ is the amplitude of the oscillation,

$\omega$ is the frequency,

${\phi }_0$ is the phase offset.

This kind of function (and its graph) is called a simple harmonic. The fundamental period of the function is $T=\frac{2\pi }{\omega }$, which means that one full oscillation occurs in the span of time$\frac{2\pi }{\omega }$. The denominator $\omega$ shows how many oscillations will occur within the time unit $2\pi$.

Complex harmonic oscillation, which occurs as a result of applying a finite (or infinite) number of simple harmonics, is also described by the functions $\mathit{\text{sinx}}$ and $\mathit{\text{cosx}}$. This way, a constant periodic function can be expressed with this system of Eqs. (6)–(8):

$$A=\frac{2}{\pi }\sum _{t=1}^n{u}_t,\mathit{cos}\frac{2\mathit{\pi t}}{\lambda },\lambda =\frac{2\pi }{\alpha }$$E6

$$B=\frac{2}{\pi }\sum _{t=1}^n{u}_t,\mathit{sin}\frac{2\mathit{\pi t}}{\lambda }$$E7

$$S^2=A^2+B^2=\frac{4}{n}{\sigma }^2\varpi \left(\lambda \right)$$E8

where $u_t$ is a member of the time series after removing the trend:

$\lambda$ is the wavelength;

$S^2$ is the intensity of the oscillations.

The graph of $S^2$ is dependence on the wavelength $\lambda$ is called a periodogram.

4. Result

4.1 Hodriсk-Preskott filtering application

We applied the Hodriсk-Preskott filtering method to isolate the cyclic component of GDP. We took GDP data in US dollars at 2015 prices, in local currencies at constant prices, and in current prices in US dollars and local currencies. Thus, using different GDP data, we can not only identify the cyclical component but also analyze the contribution of local currency inflation and exchange rate to the result. In this case, it is worth noting that the exchange rate of the Chinese currency is not floating, as well as the rate of the Russian ruble is significantly affected by methods of administrative regulation such as “budget rule” and central bank interventions. Both the Russian and Chinese currencies are far from the free-floating exchange rate model.

Figure 1 shows the result of output gaps: Russia, India, and China calculated from GDP data in US dollars in 2015 prices. We believe that this calculation is cleared of the parameter of local currency inflation and the exchange rate and can be taken as a base. In addition, we logarithmized the original time series in order to bring it to an additive model. Figure 1 shows that the cyclical component of economic growth looks different in all three countries.

Figure 1.

Output gap.

For India, the constant random fluctuations around the GDP trend line are normal and reflect that the output gap is alternately positive and negative. It means that the economy works intensely and with a shortage of resources for a while, and then there is a period of recovery.

The output gap of all three countries in the base case is characterized by a period of positive values from 1999 to 2005. It is a period of rapid growth of emerging markets in the global economy. It means that economies have used their resources excessively and are now legitimately facing a slowdown in economic growth. The same situation was observed in Japan in the 1970s and 1980s, when rapid growth was replaced by the so-called lost decade. Today, we see a slowdown of the Chinese economy and almost zero economic growth in Russia.

However, in Russia, if we consider the basic version of the output gap calculation and the calculation in current prices in national currency, the picture is significantly different, and we see a huge negative effect associated with hyperinflation of the ruble and the instability of the national currency in the period from 1991 to 1999 (Figure 2) When analyzing economic cycles, we need to consider that emerging markets have no solid economic basis for market self-regulation. In addition, oftentimes in countries such as China and Russia, economic regulation is performed by administrative methods, and the values of economic growth rates do not depend not only on the development of market mechanisms and the state of the economic environment but also on the level of government spending. This is why Russia has shown resistance to sanctions pressure, because after the 2008 crisis, Russia has been steadily replacing market mechanisms of regulation with administrative ones, which, when formally calculated, produce positive results, but the quality of such economic growth is left out of the picture.

Figure 2.

Russian output gap in constant USD in 2015 year prices and in local currency.

However, we see an increase in the efficiency of the Russian economy in 2008, 2015, and 2020. In 2008, administrative regulation of the Russian economy was strengthened as a response to the global economic crisis, in 2015 as a response to the first wave of sanctions, and in 2020 in connection with the COVID pandemic. Such dynamics indicates the presence of a short-term effect from the replacement of market mechanisms of regulation with administrative ones. Then, when analyzing economic cycles and studying the degree of integration of countries, it is necessary to take into account the basis of this integration, whether integration is connected exclusively with administrative regulation.

Additionally, we calculated the impact of dollar inflation and inflation of the local currency, as shown in Figures 3 and 4, and found that the devaluation of the national currency gives a positive, but very short-term effect.

Figure 3.

Currency course effect.

Figure 4.

Local currency inflation effect.

The third diagram shows the line reflecting the impact of inflation on economic growth for Russia in the period from 1991 to 1999, a period of sharp transition from administrative methods of regulation in Russia to the formation of a market economy. It is time when Russia faced huge negative effects associated with the devaluation of the ruble and hyperinflation. The excessive governmentalization of the Russian economy and the reliance on administrative methods of regulation today in the future may lead to the fact that the abrupt lifting of sanctions, for example, will be more disastrous than the sanctions themselves. Therefore, we can conclude that the dynamics of the output gap between countries is different, as well as the qualitative characteristics of economic growth, so we cannot confirm the synchronization of business cycles.

4.2 Spectral analysis

Next, after excluding the trend, we conducted an additional study of the remaining time series. Figure 5 shows correlograms of the remaining time series of LEE countries’ GDP.

Figure 5.

Correlograms of the remaining time series of LEE countries’ GDP.

The highest dependence among the members of these series is: for India 8 and 9, and for Russia and China the diagram looks flat. Therefore, the graphs have different shape and nature, and there is not much correlation between them. As a result, the most intense fluctuations in these remaining GDP time series are likely to be in the years when the elements of the time series have the highest correlation. To confirm this, we have shown periodograms of these LEE countries’ GDP in Figure 6.

Figure 6.

Periodograms of the trend-less time series of lee countries’ GDP.

The periodograms show that in Russia, fluctuations around the basic trend occur at intervals of 1.5–2 years. It correlates with fluctuations in economic activity that is associated with capital markets. India and China reaffirm the conclusions we made in the previous section.

In general, the results show that the synchronization in the economic cycles of the countries is minimal, and we can find no confirmation of the decoupling hypothesis.

5. Discussion

Our study showed that LEE countries have different output gap dynamics, which does not allow us to confirm any significant level of synchronization in the economic cycles of LEE countries and, consequently, does not allow us to support the hypothesis of separation.

Despite it, our study agrees well with the existing research paradigm on the topic. Before the 2009 financial crisis, there was an environment of rapid growth in developing economies, especially Asian economies [6, 9, 10]. Most researchers have found in their research evidence of decoupling hypothesis and spillover effects [12, 13, 16]. However, the 2009 global crisis changed the trend [11] and studies began to show more and more evidence of the decoupling hypothesis and spillover effects after the 2009 crisis. Our study also indirectly supports the results of other researchers who noted a significant decrease in the decoupling effect after the 2009 crisis [14, 15].

We have also found that when considering the issue of economic growth, it is important to understand by what methods this economic growth is stimulated. It is impossible to talk about full-fledged economic integration and efficiency if only the methods of administrative regulation form the basis of economic growth.

6. Conclusion

We have conducted a study of the synchronization level of LEE countries’ economic cycles. At the same time, two methods were used: Hodriсk-Preskott filtering and spectral analysis. Thanks to the Hodrick-Preskott filtering we have found the multidirectional nature of the output gap dynamics of large emerging economies, which allowed us to conclude about the lack of synchronization of economic cycles and about the impossibility of supporting the decoupling hypothesis at present. Similar results were obtained by spectral analysis. Thus, the various alliances of major emerging economies currently have more geopolitical grounds than economic ones and are a manifestation of geoeconomic fragmentation rather than integration.