The present study uses secondary data available from different published sources. It uses monthly data of WPI series,Index of Industrial Production (IIP), International Crude Oil
15
Price (ICOP), Real Effective Exchange Rate (REER), Call Money Rate (CMR) as short run domestic interest rate and monetary aggregates (M3) from January 2000 to April 2016 with base year of 2004-05. The study also uses CPI combine data but as it is available only from 2010, we have back casted the CPI data by establishing a relationship
between these two series for the common sample period. The data are collected from secondary sources such as Hand Book of Statistics and Data Base of Indian Economy published by Reserve Bank of India (RBI), Central Statistical Organisation (CSO) of the Government of India, International Financial Statistics (IFS) published by International Monetary Fund and World Development Indicators (WDI) published by World Bank. The ICOP data is not generating by any international or national Govt. Organization. Hence, ICOP data are collected from the private sources like Bloomberg (www.bloomberg.com).
To empirically analyse the study, we use some simple statistical tools and some econometric techniques. All the measures of core inflation are broadly divided into two categories, i.e., statistical approach and model based approach. Our first objective is to analyse the existing measures of core inflation suitable for India and the dynamic relationship between headline and core inflation. To analyse the statistical approach, we are using the simple statistical tools like mean, standard deviation, covariance etc. and for model based approach we are using the econometrics techniques like Structural Vector Auto Regression (SVAR). SVAR was developed by Sims (1986) and Bernanke (1986).The standard VAR was developed by Sims in 1980, but it was criticised on the ground that it does not use any economic theory; it only helps in recovering the structural innovations from residuals by using Cholesky decomposition. To overcome from these problems, Structural VAR was introduced. The major difference between standard VAR and Structural VAR is the use of economic theory. Structural VAR can be identified from reduced form of VAR model. It is helpful to separate out the economically unrelated influences in price index and also useful for forecasting purpose. After getting all the series of core inflation we try to find out the existing dynamic relation headline inflation and core inflation. We use Vector Error Correction (VECM) Model to identify this dynamic relation.
16
Our second objective is to find out the domestic and imported core inflation for India. Volatility in food prices are not the only reason which creates short run or temporary fluctuations in the price index. Sometimes price of various commodities directly or indirectly get affected by the changes in the world economy. This objective aims to find out, which are the commodities that attract inflation from the world economy. To analyse this, first we separated goods of price index into two categories, i.e., tradable and non-tradable goods. Tradable goods are mostly associated with world economy and fluctuations in the world economy leads to affect the domestic economy through theses goods; and non-tradable goods purely represent the fluctuations in the domestic economy. In the first objective the study use WPI to represent the inflation but WPI does not include non-tradable goods or service sectors in the index. Therefore, we use CPI instead of WPI to represent the inflation of the Indian economy. First, we use factor analysis to categorise CPI index into two different sectors, i.e., tradable and non- tradable goods; and then we try to find out how fluctuations in the world economy transmitted to domestic economy through tradable goods by using Vector Auto- regression (VAR) model.
The third objective of the study is to find out the role of asset price in Indian inflation. Our main motives of the study are to identify all the possible sources that contribute to Indian inflation. According to Alchain and Klein (1972), instead of looking into only current consumption basket for measuring inflation, we must focus on the current cost of expected life time consumption as people lives in two periods. So, we also need to consider the future expectations of individuals. We are considering individuals’ investment in future or their behaviour towards future expectations of prices, and we must consider asset prices as indicator of these things, because asset prices can better represent the future movement of prices. Also, we can identify aspiring credit bubbles and take necessary monetary policy actions according to that. To identify the role of asset price in Indian inflation, first we added asset price in to the commodity basket by assigning weightage to it. We use Neo-Edgeworthian Index to assign weightage to the asset prices in commodity baskets. Finally, after assigning weightage we use Kalman
17
Filter to estimate the forecasted inflation from both headline series of inflation and also from the inflation series including asset prices.
Final and last objective of this study is to find out whether targeting core inflation really improving the macroeconomic performance of India or not. The ultimate objective of every country is to achieve economic growth and a stable economy. After identifying all the possible factors that contributing to Indian inflation, the study aims to find out whether adopting inflation targeting framework improves macroeconomic performance of the country or not. It is believed that price stability is the pre-condition for sustained economic development from every aspect. In this objective we are examining how all the measures of core inflation we get from above mentioned three objectives contribute to macroeconomic performance of the economy. To empirically analyse this, we use Vector Error Correction Model (VECM). We have also applied Impulse Response Function (IRF) and Variance Decomposition (VD) to know how macroeconomic variables response to any changes in core inflation.