Description of Data

All data have been collected from the database of International Financial Statistics (IFS). Depending on the analytical purpose, two types of dataset on Consumer Price Index (CPI) are exploited. The first is the monthly data for Time domain analysis and the second is the quarterly data for Frequency Domain Analysis. The time domain analysis aims to model the volatile nature of inflation which requires high frequency data. Given the record of inflation, monthly frequency of data is the best alternative. The frequency domain analysis examines inflation volatility from the cyclical components and at different frequencies of fluctuations. The literature (e.g. Baxter & King, 1999) suggests that quarterly data is more suitable for the extraction of cyclical components and frequency decomposition as it mitigates noise from the data but retains the basic pattern in the movement of the concerned variable.

In addition to categorising the use of data according to methodological purpose, data on CPI are assembled into two layers. The first is for group level data and the second is for individual country level data following the classification of IFS. The motivation behind considering the data for the group as well as for individual countries is to check the robustness of the findings obtained from the empirical analysis. The group of advanced

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economies comprises thirty three countries, including Euro area, G7 countries, new industrialised Asian countries and advanced countries other than G7 & Euro area. The group of emerging and developing economies consists of one hundred forty nine countries, including Central and Eastern Europe, Commonwealth of independent states, Developing Asia, Sub-Saharan Africa, Middle East and North Africa and Latin America and the Caribbean countries15. Such group level data are particularly useful for obtaining

an initial overview of the scenario at aggregate level. It is possible to identify the distinguishing feature of significant volatility in inflation for developing countries than for the advanced group. Nevertheless, analysis has been extended to get conformity of the key stylised fact of inflation from individual country level data. For this purpose, two samples of advanced and developing countries have been constructed. The sample countries, whether they belong to advanced or developing group, are chosen in a way that the homogeneity of each group can be maintained. Besides, it is considered whether these sample countries can be well representative for their respective groups.

In case of advanced group, countries like US and UK are well known developed countries in the world. Along with them, the several EU countries are chosen which are homogenous in terms of country specific traits. Further, given the fact that these countries are under similar type of monetary policy rule, it would be convenient to control for the heterogeneity of policy specific shocks. In case of developing economies group, first, the countries are classified into four broad categories geographically, viz., Latin America, Sub-Saharan Africa, Middle-East and North Africa and East, South-East

15 _{See IFS website for further details.}

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and South Asia. Latin American countries have a history of hyperinflation. Since the aspect of hyperinflation is not addressed in this thesis, this group of countries is not considered. Sub-Saharan Africa and Middle East and North African countries are subject to political instability and social unrest which make their economic structure quite different (and sometime treated as the „outlier‟) in the entire group of developing nations. By contrast, East, South-East and South Asia reflect some similarity in their pattern of economic development with respect to growth, market structure, liberalization and public policies. At the same time, these countries are well representative in terms of inflation volatility for the group. The range of coefficient of variation of inflation is 0.42 to 0.56 for these four categories of developing countries and South East Asian region lies in the range with 0.48. Finally, in comparison with other regions, very little work has been done on inflation volatility for South East Asian nations. In sum, all these factors provide motivation to choose the sample of countries from East, South-East and South Asian region.

Table 2.2A: Sample of Countries for Time Domain Analysis

Country ID Advanced Developing

1 Austria Bangladesh 2 Belgium Cambodia 3 Canada China 4 Denmark India 5 Finland Indonesia 6 France Malaysia 7 Germany Myanmar 8 Italy Nepal 9 Japan Pakistan 10 Norway Philippines 11 Switzerland Srilanka 12 UK Thailand 13 US Vietnam

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Table 2.2B: Sample of Countries for Frequency Domain Analysis

Country ID Advanced Developing

1 Austria Bangladesh 2 Australia Cambodia 3 Belgium China 4 Canada Fiji 5 Denmark India 6 Finland Indonesia 7 France Malaysia 8 Germany Myanmar 9 Italy Nepal 10 Japan Pakistan 11 Norway Philippines

12 New Zealand Papua New Guinea

13 Switzerland Srilanka

14 UK Thailand

15 US Vietnam

The empirical analysis considers the sample period of 1968 to 2011 as this is the maximum time span for which the inflation series are available for both the advanced and developing countries. For the time domain analysis, monthly data on CPI are collected for the sample period of 1968M01 to 2011M09. From the dataset of CPI, inflation series are calculated as the logarithmic difference of price indices between two consecutive time periods. The group level data is used to implement the first method of ARCH Effect test while the country-wise data for individual sample countries are utilised for GARCH estimation and estimation of long run volatility. Following the country classification of IFS, sample of advanced and developing countries are chosen. In Table: 2.2A, the sample countries are listed. Each group of economies contains thirteen countries in the sample. For the frequency domain analysis, quarterly data on CPI are gathered for the sample period of 1968 Q1 to 2011 Q2. In Table 2.2B, the

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countries selected in the sample are listed16. Once again, inflation has been computed as

the logarithmic difference of price indices between two consecutive time periods. Considering the inflation series for analytical group and individual country, the method of frequency filter is applied to dissect the innate volatility at different frequencies of the underlying process. For the country level study, the sample is almost same as it is for time domain analysis, but with a little difference.