Statistical Method to predict Agricultural CPI

The growth of any country depends on its economy and economic growth is nothing but an increase in the inflation i.e. adjusted market value of the goods and services produced by an economy over time. Statisticians conventionally measure such inflation using the price indices. They are mainly WPI (Wholesale Price Index and CPI (Consumer Price Index). WPI is now known to be an older method of computation because the main focus has to be on consumer prices.CPI is a measure of consumer prices over a certain period. Changes in the CPI are used to assess price changes associated with the cost of living. It can be calculated for rural, urban areas as well as for both. In CPI rural, the workers and labourers are benefitted as their daily wages can be predicted by this approach. The CPI by state data represents the inflation of each of the states giving a concise view of the country. The data is collected and analysed using a mathematical approach called linear regression in future prediction for rural labours based on previous data.

Efektivitas Kredibilitas Bank Sentral terhadap Inflasi bagi Produsen dan Konsumen di Indonesia

This study is intended to explain the impact of central bank credibility on inflation in Indonesia at the producer and consumer level. In this study, Central Bank Credibility is measured using an index with values between 0 (zero credibility) and 1 (perfect credibility). Generalized Method of Moments (GMM) method is used to analyze the impact of central bank credibility on inflation. Based on the results, central bank credibility can reduce inflation on both producer and consumer price. Central bank credibility is more sensitive towards producer price index compared to Gross Domestic Product (GDP) deflator and wholesale price index while at the consumer level, central bank credibility is more sensitive towards core inflation compared to headline inflation. -------------------------------------- Penelitian ini menjelaskan dampak kredibilitas Bank Sentral terhadap inflasi di Indonesia. Dampak kredibilitas Bank Sentral dianalisis pada tingkat produsen maupun konsumen. Untuk mengukur kredibilitas Bank Sentral, penelitian ini menggunakan indeks kredibilitas bernilai 0 (zero credibility) hingga 1 (perfect credibility). Metode Generalized Method of Moments (GMM) digunakan untuk menganalisis dampak kredibilitas Bank Sentral terhadap inflasi. Berdasarkan hasil empiris, kredibilitas Bank Sentral cenderung lebih memengaruhi inflasi pada Indeks Harga Produsen (IHP) dibandingkan Indeks Harga Perdagangan Besar (IHPB) dan deflator Produk Domestik Bruto (PDB). Kredibilitas Bank Sentral lebih memengaruhi inflasi inti dibandingkan dengan inflasi umum. Dari hasil empiris diketahui bahwa kredibilitas Bank Sentral lebih memengaruhi inflasi pada sisi produsen dibandingkan konsumen.

EXCHANGE RATE PASS — THROUGH TO DOMESTIC PRICES: EVIDENCE FROM PAKISTAN

Exchange Rate Pass — Through is the phenomena that explains to what extent the movements in exchange rate affect macroeconomic variables of any economy. This paper analyses the movements of exchange rate that has affected on wholesale price index, consumer price index, large scale manufacturing, fuel and lightening and the growth of money supply. The data from June 2005 to June 2011 is analyzed by using the econometric framework. In this study, the econometric model, recursive VAR, suggested by McCarthy (2000), is applied in order to measure the movements of exchange rate pass — through to domestic prices by using the impulse response function and variance decomposition. In this study, the results of the impulse response have shown that impact of exchange rate pass through is high on wholesale price index. While the results of the impulse response have shown that the impact of exchange rate pass through is much lower for Consumer Price Index. The result of the variance decomposition has shown that the variance decomposition is indicating that for the CPI variance decomposition is as much as the 5.48 percent. For the WPI the variance decomposition is as much as 10.15 percent and the other variations are explained by the other independent variables.

An Economic Analysis on Historic Inflation Rate in India

India's inflationary experience was a varied basket. There were some years the annual inflation rate reached a maximum of 40%, while other years were negative. Wholesale price index, differs in a wide range as the lowest value of -12.5% for the year 1952-53 and the highest amount of 38.3% for the year 1943-44. The highest value is due to the end of the world war-II. The years of high, inflation is mainly the impact of War, low agricultural production due to drought and oil price hike up in foreign countries. The inflation rate was below 6% for 38 years and above 6% for 40 years out of 78 years beginning from 1939-40 to 2016-2017. Suppose, it is assumed that India's bearable rate of Inflation is equal to or below 6%, then, India appears to be worst in controlling inflation. On the other hand, the rate of inflation above 6% to 15% was for 31years and above 15% for nine years. Therefore, many accept as accurate that India is to be an Inflation driven country. The severe economic setback in the Indian economy is inflation, which hinders the economic power of the people. Most of the people are still suffering due to increase in India. These major study aims to support the Government, and economists should generate better policies for the control the rise.

The Indian Inflation 2006–2016: An Econometric Investigation

Recently several changes have been adopted in the conduct of monetary policy in India, like tracking CPI (Consumer Price Index), targeting inflation and so on. However, certain curious features of inflation may have some implications on the effectiveness of such measures. This article tries to explore the nature of inflation during the last decade. There are certain views about the nature of Indian inflation from the structuralist perspective. This article contributes to the literature by empirically testing those propositions and coming out with some significant policy implications. The article is based on monthly data from January 2006 to March 2016. By employing econometric techniques like cointegration and vector autoregression (VAR), the article tries to explain the movements of different components of WPI (Wholesale Price Index) and CPI inflation, both core and headline inflation and how they are related to macroeconomic policy variables. The empirical analyses focus on finding out the existence of co-movements among the inflation and macroeconomic variables, explaining the role of components like food and fuel price in driving CPI and WPI. The results have some important policy implications. First, the movements of WPI and CPI and their headline and core counterparts are not explained by same set of variables. Second, food inflation is not explained by agricultural output pointing to the insufficient increase in supply in agriculture. Third, the determinants of CPI headline and core inflation are not same. So, both of them should be tracked while formulating policies. The relationship among the components of inflation point to the possibility of some adjustment in demand from one set of goods to another, implying adjustments in terms of relative prices which needs further exploration. JEL: E31, E52, C32

Macroeconomic Dynamics of Foreign Direct Investment in India: An Empirical Analysis

For the past few years, Foreign Direct Investment (FDI) has become the indicator for Economic Growth, especially in emerging economies. This paper empirically investigates the determinants of FDI flows in India by employing the Auto Regressive Distributed Lag (ARDL) model. The result confirm the existence of a long run equilibrium between the FDI and five explanatory variables, namely exchange rate, Wholesale Price Index, Index of Industrial Production, Trade openness and dummy variable (financial crisis). India’s Wholesale Price Index, Exchange Rate volatility and Index of Industrial Production have positively influence the flow of FDI in India and Trade Openness is negatively significant for the flow of FDI in India. The coefficient of the Error Correction Term (ECT) is highly significant with expected sign, which confirm the result of bound test for co-integration. The cumulative sum of recursive residual (CUSUM) test is used for measuring the stability of the model.

P-star model for India: a nonlinear approach

Inflation in India has been a major cause for concern in the recent past (2008–2012). This study examines the Indian wholesale price index inflation from 1951 to 2012 using P-star (or P*) models after accounting for the nonlinearities in the data by establishing the presence of a nonlinear long-run equilibrium. The paper establishes the presence of a threshold vector error correction model (TVECM) between prices and their long-run equilibrium with three optimal regimes to explain the short-run and long-run dynamics based on an error correcting transition term. Based on these results, the study classifies the various regimes that Indian inflation goes through based on historical economic events. The P* models (price gap, output gap and velocity gap models) were implemented regime-wise. The price gap models (output gap and income velocity gap determine inflation) were found to be optimal in the first and second regimes and consistent with theory. The velocity gap model (which has monetarist foundations) was found to be optimal in the third regime.

Impact of Macroeconomic Variables on Small, Mid and Large Cap Stocks: A Comparative Study of India Using VAR Approach

The study examines and compares the relationships between Indian stock market indices (BSE Small, Mid and Large Cap) and five macroeconomic variables (Index of Industrial Production, Wholesale Price Index, Money Supply M3, Exchange Rate and Call Money Rate) over the period April 2006 to March 2017. The study applies Augmented-Dickey Fuller test to test the data stationarity. The analysis reveals that data is neither found to be stationary at level nor co-integrated. Hence, the study applies unrestricted Vector Autoregression (VAR) model to establish the short-run relationships. It is observed that macroeconomic variables significantly impact stock prices depending upon the type of index. As per the Granger Causality test, the study found unidirectional relationship from Exchange Rate to BSE Small Cap; unidirectional relationship from Exchange Rate to BSE Mid Cap and BSE Mid Cap towards IIP; bidirectional relationship between BSE Large Cap and Exchange Rate whereas unidirectional relationship from BSE Large Cap to IIP and from Money Supply M3 towards BSE Large Cap.