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The purpose of the article is to analyze the relevance of earnings fundamentals in predicting extreme price reversals of an emerging stock market. We collect monthly price data on six sector indices from Bombay Stock Exchange (BSE) of India for the period 2004–2019. The research decomposes industry stock returns into Potential Maximum Gains (PMG) and Potential Maximum Losses (PML) with price extremes at first and then tests price reversal behavior using Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) and vector autoregressive (VAR) models. The study finds symmetry between PMG and PML in the banking, realty, and oil sectors, while the asymmetric reversal behavior is noted in the automobiles and capital goods industries. The presence of industry fundamentals in the models estimating the reversal behavior of share prices enhances their predictive power, which suggests the significance of value strategies in making gains from extreme price variations. The price reversal behavior is sector specific and found inconsistent in emerging market. Hence, the investors cannot overlook the relevance of the industry characteristics and earnings fundamentals while predicting the stock price behavior in emerging markets. [ABSTRACT FROM AUTHOR]

Modeling surface water quality has become crucial in providing better strategies for managing surface water resources, and adequate findings need accurate and geographically dispersed data. Hydrogeological modeling of these data sets is possible using empirically-based models. The other statistical models are also an alternative approach. In this study, a process with maximum probability is considered with the help of machine learning tools (MLT) to have optimized and valid output. The proposed method combines remote sensing and geographic information systems (RS and GIS) and MLT, which are appropriate for the predicament of neither small, large scale, nor long-term simulations. MLT methods such as VAR and ARIMA are developed in the Python programming with Jupyter notebook and tested according to the data in the spatial prediction for surface water quality parameters such as Tr, pH, Ec, TDS, AL, Ca++, NO−3, So, Cl, F−, Fe, and Mg2+ in the Krishna District, Andhra Pradesh, India—lower delta part. The delta with susceptible zones was identified using RS and GIS as those areas are prone to direct exposure to surface water contaminants from aquaculture, agricultural runoff, small- and medium-scale businesses, and household trash. Achieving effective surface water management for this ecosystem is critical for regional water management. The geographical information about the concentrations acquired via the RS and GIS was compared to the statistical modeling findings and verified using real-time measurements. MLT modeling seems more realistic than the experimental setting; data from the previous 20 years (2000–2020) were used for modeling, and the predicted values presented in the paper are predicted for the year 2021. The computed R2 value of ranges between 0.75 and 0.96% is recorded with ARIMA, and VAR posted range between 0.56 and 0.75% with the trained and tested data. The findings show the potential for MLT of geographically dispersed hydrogeological data to be used for pollution-free surface water management. From the surface water management perspective, combining RS and GIS and MLT offers an alternate data analysis approach for obtaining quick results utilizing a less laborious process that produces acceptable results. [ABSTRACT FROM AUTHOR]

It is important to understand the features of an integrated renewable energy power system, especially for deregulated systems. The greatest obstacle to assimilating renewable energy generators with the existing electrical system is their unpredictability. Because wind energy is inconsistent, incorporating it into an established power system necessitates more planning. The effects of wind farm (WF) incorporation with fuel cells and a unified power flow controller (UPFC) on electric losses, voltage profile, generating price, and the economics of the system in a deregulated power market are examined in this paper. An impact analysis of integrating wind farms into controlled and uncontrolled situations is conducted. At two randomly selected locations in India, the real-time statistics of the actual wind speed (AWS) and forecasted wind speed (FWS) were merged for this study. The surplus charge rate and deficit charge rate are intended to evaluate the imbalance cost which is arising from the difference between anticipated and true wind speeds to determine the economics of the system. Customers are always trying to find electricity that is reliable, inexpensive, and efficient due to the reconfiguration of the power system. As a consequence, the security limitations of the system may be surpassed or might function beyond the safety limit, which is undesirable. In the last section, heuristic algorithms, such as sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth-flame optimization algorithms (MFO), are employed to analyze economic risk. In the real-time energy market, it also covers how the fuel cells and UPFC are utilized to rectify the WF integration's deviation. Economic risk evaluation approaches include value-at-risk (VaR) and conditional value-at-risk (CVaR). A modified IEEE 30-bus test system is used throughout the whole project. [ABSTRACT FROM AUTHOR]

Purpose: Rising crude oil prices are likely to have an asymmetric and nonlinear negative impact on GDP growth. The purpose of this paper is to ask the following questions: Does the effect of a crude price shock depend on the position of crude price cycle, i.e. is the effect of price shock larger/smaller in periods of already elevated crude price? And, does the effect of crude price shock depend on the position of the economy in the business cycle, i.e. does the crude price shock affect growth differentially in periods of low/high growth? Design/methodology/approach: The authors use a local linear projection (LLP) model to examine the asymmetric impact of crude price on GDP growth in an environment of high crude price. Next, a quantile regression model is used to account for differential impact on growth around high and low growth periods. Findings: Results from the LLP model show that when oil price is above $70, each additional percentage point of increase in oil price results in a 20 basis point (bps) drop in quarterly GDP growth rate on average. The impact is felt between the third and sixth quarters. When oil prices rise above $80, the impact is similar, with a sharper drop in growth (30 bps). The exercise with quantile regression shows that the impact of an increase in crude prices on growth is almost double at lowest quantiles of growth compared with the median. Originality/value: There is a growing literature that evaluates the impact of oil price in developing economies. However, nonlinearities in crude price-GDP growth dynamics have not received enough attention, especially during phases of elevated crude price or a growth downcycle. The authors believe that accounting for such effects is especially relevant in the present economic scenario of high oil prices because of geopolitical crises and a period of vulnerable growth because of supply chain issues arising out of the pandemic. Using recent data from oil-importing emerging market economies such as India, this paper fills a crucial gap in the literature. [ABSTRACT FROM AUTHOR]

This study aims at developing econometric models to manage the price risk of Dry and Wet Cocoa beans with the help of ARIMA (Autoregressive Integrated Moving Average) and VAR (Vector Auto Regressive). The monthly price of Cocoa beans is collected for the period starting from April 2009 to March 2020 from the office of CAMPCO Limited, Mangalore, and the ICE Cocoa futures price from the website of investing.com. The augmented dickey fuller test is used to test the stationarity of the series. The ACF and PACF correlograms are used to identify the tentative ARIMA model. Akaike information criterion (AIC) and Schwarz criterion (SBIC), Sigma square, and adjusted R2 are used to decide on the optional AR and MA terms for the models. Durbin–Watson statistics and correlograms of the residuals are used to decide on the model's goodness of fit. Identified optimal models were ARIMA (1, 1, 0) for the Dry Cocoa beans price series and ARIMA (1, 1, 2) for the Wet Cocoa beans price series. The multivariate VAR (1) model found that the US and London Cocoa futures prices traded on the ICE platform will influence the price of Dry Cocoa in India. This study will be helpful to forecast the price of Cocoa beans to manage the price risk, precisely for Cocoa traders, Chocolate manufacturers, Cocoa growers, and the government for planning and decision-making purposes. [ABSTRACT FROM AUTHOR]

In the present study a novel attempt has been made to study the co integrated movement of food grains production and agricultural inputs in India. The data pertaining to food grains production and pattern of consumption of agricultural inputs like total cropped area, fertilizer consumption, pesticide consumption in India for the period 1950-1951 to 2019-2020 collected from official sources have been analysed. Coefficient of determination (R2) has been used to evaluate the goodness-of-fit of linear, quadratic and cubic trend functions of the variables under study. Vector auto regression could be extensively used to examine the co integrated movement of each variable using the lagged values of itself as well as other variables. Findings of the study revealed that VAR models resulted in significantly high values of adjusted R2 ranging from 0.95 - 0.99 for prediction models with respect to different variables showing the potential of the VAR approach to quantify the co integrated movement of the variables. [ABSTRACT FROM AUTHOR]

Purpose: The study tries to find pattern in the bilateral trade and impact of macro happenings like GFC, Chinese meltdown, Galwan conflict, COVID-19 on it over the period of 1995 to 2020. Design/Methodology/Approach: The study has two dimensions. The first one analyses the monthly export and import figures between India and China product wise (based on HS Code at two -digit level) from Jan2016 to Jan 2021 whereas second one focus on annual data of Indo-China Export and Import along with their annual GDP for 26 years starting from 1994 - 1995 to 2019-2020. Bilateral trades are analysed by using four tools namely-Bilateral Trade Dependence Index (BTDI); Trade Intensity Index (TII); Herfindahl Hirschmann Market Concentration Index (HHI); and Index of Export market penetration (IEMP). The study has also used Time series analysis to find the relationship between total bilateral trade and GDP of respective countries using Johansen Cointegration Test, Granger Causality Test, and VAR model. Findings: The annual growth rate of import and export for India with China suggest the short-term impact of macro happenings. Research Limitations: The study has several limitations with respect to availability of very recent data, availability of cost components of trade items in respective countries etc. Managerial Implications: Policy makers for India are suggested to work towards import substitution via various programs like Make-in-India with priority of domestic productions of HS Code 85, 84, 29 which are increasing the trade deficit continuously. Originality/Value: This study is an original effort to highlight the dynamic bilateral trade relationships between India and China in last twenty-five years. [ABSTRACT FROM AUTHOR]

There are growing consensuses regarding the efficiency and efficacy in the use of fiscal policy in stabilization, redistribution and also in the conduct of monetary policy. Despite inadequate revenues in the fiscal system to match the aggregate spending of the Government, the issue of deficit financing is not meaningfully addressed. A well-designed fiscal stimulus measures are prerequisite for those economies, including India, where fiscal space is perceived to be limited. This study is a modest attempt to capture the dynamic links between the Government deficits and interest rates through Hsiao based Granger causality. The evidences based on quarterly as well as on annual data clearly demonstrate the one-way causality from fiscal deficit to interest rate. Other possibilities of causality are ruled out as per the estimates produced. [ABSTRACT FROM AUTHOR]

In this paper, an aggregate financial stress index for India is constructed by taking monthly data from different segments of the financial market like money market, bond market, equity market, foreign exchange market, and the banking sector, for the period March 2007 to December 2016. The interrelationship and feedback effect between financial stress, economic growth and price stability are tested by using correlation and an unrestricted VAR model. The impulse response analysis shows that financial stress leads to a decline in growth after a lag period and a higher growth rate for a longer period of time increases stress in the financial system. The variance decomposition result indicates that the contribution of FSI to the variation of other variables are not much high, whereas other variables can affect FSI to some extent. In the Short-run price stability increases financial stress but in the long run, the result is the opposite. [ABSTRACT FROM AUTHOR]

• "cys2/MFK*/*REY" motif with short length were restricted to cerebral and severe malaria. • Higher expression level of var group A was associated with malaria severity. • Peptide VAR P5 (DIGDIVRGKDLY) appeared to be a good marker for severity. Plasmodium falciparum erythrocyte membrane protein is encoded by a highly variable multicopy var gene family known to play a key role in malaria pathogenicity. Therefore, we investigated sequence variation, expression profile and immune response of the Duffy binding-like domain (DBLα) region of the var gene. Blood samples were collected from patients with cerebral, severe and mild malaria in Chhattisgarh, India, a region with endemic malaria. Polymerase chain reaction amplicons were cloned and sequenced to determine sequence variation. The expression level was analyzed targeting the upstream region of var gene using the Delta-Delta-Ct method. Immunoglobulin G (IgG) level was determined against the 6 synthetic peptides of the DBLα region. The study identified that group 1 and group 5 sequences (cysteine/position of limited variability (cys/PoLV) classification) along with cys2/cys4 and MFK*/REY motifs and short amino acid length were significantly associated with malaria severity. The specific PoLV (MFKS, LREA, PTNL) were restricted to cerebral malaria. The expression level of var group A was higher than var groups B and C, demonstrating its prognostic characteristic. All peptides showed high-quality IgG response, while VAR P5 appeared to be a good marker for severity. The present study illustrates the presence of specific sequences of DBLα tags involved in severe malaria that could be targeted in future interventions for malaria control and elimination. [ABSTRACT FROM AUTHOR]

This study explored how emerging economy banks are rebalancing their interest income and non‐interest income to ensure stability. We set our study in India during the period 2005–2017. Interestingly, we observe that time–series correlation of interest income growth and non‐interest income growth for public sector banks as well as for private banks is on the negative side. We applied panel vector auto regression and generalised method of moments methodology. This study found that when bank interest income falls, they try to increase their non‐interest income to offset their losses to a certain extent, and the trend is increasing. Public sector banks are overall substituting non‐interest income for a reduction in the margin, and there is an increasing trend for this substitution. Based on the size of banks, it has been found that change in non‐interest income in the subsequent year due to change in interest income in the previous year is there for large banks, whereas no such significant change has been found in case of small banks. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to study the effect of the 2008 global financial crisis on housing market dynamics in an emerging economy like India using quarterly data (Q4 2008–2009 to Q1 2018–2019). The study explores the extent of linkages between housing prices, monetary policy and financial stability by explaining the nature of the shocks to the housing sector and the degree of impact of those shocks; the possibility of adverse feedback loop which is beyond the natural levels; and the usefulness of explicit and direct role of monetary policy for the housing market stability, which was the loudest demand immediately after the crisis. Design/methodology/approach: The paper follows a three-step methodology: data transformations, a variable selection process "general-to-specific modelling" with the help of OxMetrics 6 Package, and vector autoregressive modelling with the help of EViews 10. F-test was used to describe the short-term relationships between the variables. Impulse response and variance decomposition were used to explain the type of relationship (negative or positive) and the period of the relationships, respectively. Findings: The study finds that the housing sector is sensitive to the monetary policy shocks, whereas the contribution of the housing market shocks to the fluctuations in other market variables is not substantial, though not negligible. As far as the nature of the shocks is concerned, the observed dynamics in the real house prices are diverging from their fundamental levels. The housing market shocks are more or less static; it rules out the chances for a self-reinforcing feedback loop with the existing setup. Research limitations/implications: The study concludes that the observed dynamics in the real house prices are diverging from their fundamental levels. Given the limitation, the researchers could extend this study by decomposing the part of the risk to the sector contributed by the other drivers, which may be inherent imperfections in housing markets, weak and unreliable wealth effect, and the presence of behavioural biases. Practical implications: The present study finds countercyclical measures to be more useful for this sector as compared to the forward-looking monetary policy reforms in this sector. The central bank in India should continue to refrain from responding directly to the housing sector fluctuations. Investors can enjoy investing in the housing sector without any fear of the crisis as of now. The effect of speculation is small but not negligible, which enjoins the investors and the policy-makers to remain watchful. Interest rate, money supply and inflation lead (Granger-cause) the housing prices. This information is relevant for spending and investment decisions. Social implications: The study feels that banks should avoid using monetary policy to balance the house prices. This will be beneficial both for the economy and the society, as any change in monetary policy to especially curb out surging housing prices may adversely affect the output, and finally, may lead to the deflation. The fear of deflation may cause devastating economic, financial and social effects. Originality/value: The study contributes to the literature by shedding some new insights about the interrelationship between macroeconomic variables, housing prices and financial stability in the aftermath of the 2008–2009 financial crisis. Such types of studies are absent from emerging markets, particularly from India. [ABSTRACT FROM AUTHOR]

In prior literature it was conjectured that the Indian stock market responses on domestic macroeconomic surprises are expected to be significantly influenced by global surprises. In this paper we empirically established that hypothesis. We used both the Event Analysis and VAR model. We found that global surprises consistently dominate Indian stock market and the influence of domestic macroeconomic surprises on it is relatively less. The understanding of stock market dynamics against domestic macroeconomic surprises and global factors can provide assistance to the policy makers for augmenting policy effectiveness and the corporate finance professionals for enhancing decision making. [ABSTRACT FROM AUTHOR]

Flow of information and volatility coming from stock markets of other countries have significant impact on the stock market of a country. Volatility is even higher in the case the countries enjoy good economic conditions among themselves. The present manuscript aims to probe into the spread of impacts over a large range of returns and volatility in four major equity markets of Asia viz. India, China, Hongkong and Japan for a period of 18 years ranging from 2000 to 2017. The study uses VAR based GARCH model to determine the volatility spillover among the chosen countries for the period under assessment. The empirical outcomes of the study present that all selected markets have responded to their own lag of conditional volatility along with news shocks. The impact of conditional variance is higher in comparison to shocks which is an indication that markets fundamentals are stronger than corrections or shocks. The results of cross country spillover show that volatility of Shanghai Stock Exchange of China and shocks from Japan and Hongkong markets assert a significant effect over volatility of Indian equity market. Volatility of stock markets of Japan and China is not affected by the cross market volatility and shocks spillover from India. In contrast, volatility of Hongkong market is affected by shocks and volatility of Indian equity markets. Findings of the research have meaningful insights for the Governments and regulators, academicians, researchers, investors and fund managers in framing investment strategies in the chosen markets. [ABSTRACT FROM AUTHOR]

This study analyzes the sensitivity of a series of Indian stock indices for the astonishing component of monetary and macroeconomic policy with the data set from 1 April 2004 to 31 July 2016. The immediate impact is assessed with event analysis, and the dynamic effect is analyzed with the Vector Autoregression (VAR) model. The result of the event analysis indicates that the monetary policy surprise significantly affects the stock market and is more prominent than that of other macroeconomic surprises. Unlike the event study, the VAR analysis found that the other macroeconomic surprise also affects stock return. The study also highlights the industry effect and size effect, which is coherent with the predictions of the CAPM (Capital Asset Pricing Model) model. While many studies have been conducted on the monetary policy surprise in the developed economy, there are relatively few studies on macroeconomic surprises. Some studies conducted in India have analyzed the impact of monetary policy surprises on stock price; however, to the best of our knowledge, none of the studies has examined the simultaneous effect of both macroeconomic and monetary policy surprise. The study is relevant because the responses differ across sectors and vary with firm sizes. Thus, the study can effectively be used as a hedging instrument. Furthermore, the stock market acts as a vital channel for policy transmission and a critical decision driver for corporate finance. The understanding of firm and stock market dynamics against macroeconomic surprises can help policymakers in enhancing policy effectiveness and corporate finance professionals in improving decision-making. [ABSTRACT FROM AUTHOR]

Monetary policy to be effective, besides an accurate assessment of the economy and precise timing, a clear understanding of monetary transmission mechanism as well as of the channels through which monetary policy affects output and prices is important. Further, what has duly received considerable attention in recent days is the disaggregated effect of monetary policy on output at sectoral level, i.e., whether different sectors respond symmetrically to monetary policy shocks. Extending this line of enquiry, the paper analyzes the effect of monetary policy shocks on the aggregate as well as on the sectoral output of Indian economy using reduced form vector auto regression (VAR) model. In order to understand the direction of response variables (aggregate and disaggregated output) to standard shocks (monetary policy), we employ impulse response functions. We find that the impact of a monetary policy shock at the sectoral level is heterogeneous. Sectors such as mining and quarrying, manufacturing, construction and trade, hotel, transport and communications seem to decline more sharply than aggregate output in response to a monetary tightening. We also augment the basic VAR by including three channels-credit channel, exchange rate channel and asset price channel of the monetary pol icy, and analyze the sector specific importance of each of the channel. The channels through which monetary policy is transmitted to the real economy are found to be different for every sector. In most of the cases, multiple channels are responsible for the changes in the aggregate and sectoral output to the monetary policy shock. These results clearly indicate the need for a sector specific monetary policy in India. [ABSTRACT FROM AUTHOR]

At present stock return is significantly related to other global stock markets. The present paper empirically investigates the short run and long run equilibrium relationship between the stock market of India, Japan Hong Kong, Singapore, Malaysia, China, and Australia monthly data during January 1995 to December 2013. Researcher employs correlation test, multivariate cointegration framework, Vector Auto Regressive error-correction model and Granger causality test with reference to financial up evils in Asia and world viz., Asian crisis (1997/98), financial crisis (2008) Inflation conditions, Natural disasters, financial up evils etc. of long run relationship. Results find that the Indian stock market return is significantly cointegrated with long run and short run situations/ causalities in Asian Stock returns. [ABSTRACT FROM AUTHOR]

India is witnessing tremendous growth in dairy industry. The milk production has increased from 20 million tonnes in 1961 to 132 million tonnes in 2012-13. India has been retaining its number one position in milk production for many years. Dairy Industry in India is growing at the rate of 10% per annum. Considering this, it is essential to know the future production to improve and sustain the growth and development of sector. The objective of the study is to find out most suitable forecasting method for milk production for sustainable future production and policy implications. The data used in study is secondary data, collected from FAOSTAT (1961 to 2012) and NDDB (1991 to 2012). Stationarity of data was checked with Autocorrelation Function (ACF) and Partial autocorrelation function (PACF), after confirming the stationarity, Autoregressive Integrated Moving Average (ARIMA) and Vector Autoregression (VAR) models were used. Akaike Information Criteria (AIC), Schwartz Bayesian Criteria (SBC), Mean Absolute Percentage Error (MAPE), R square and RMSE were used to test reliability of model. The results indicate that ARIMA (1, 1, 1) is more suitable method with the use of SPSS software package for forecasting of milk. Milk production is expected to be 160 million tonnes by 2017. [ABSTRACT FROM AUTHOR]

Liberalization of financial markets in emerging economies usually leads to an increase in the foreign ownership of the host country's stocks. Heavy investment of these foreign investors may have deep impact on host country's stock market. The motivation for this study is to understand the investment pattern of foreign institutional investors (FIIs) and their trading behavior, and its impact on the stock market. The present study is an attempt to evaluate the investment pattern of FIIs' investment and its relation to the stock market return in the Indian stock market. Daily data from January 2003 to June 2014 has been collected and analyzed with vector auto regression and multivariate regression models. The results of the study suggest the prevalence of unintentional herding among FIIs that creates short term volatility in the Indian stock market. FIIs' herding tendency is more intense on the buy-side rather than the sell side. Although the impact of FIIs' herding creates short-term volatility, it may still impede the price efficiency of the stock market. [ABSTRACT FROM AUTHOR]

The main objective of this study is to find out causal relationship between FDI and GDP, presence or absence of long run equilibrium relationship between FDI and GDP during post reform period. The required data of FDI and GDP is collected from RBI database for 64 quarters commencing from Q2 of 1998 to Q2 of 2014 and GDP is used as proxy for economic growth. The result of Johansen cointegration test indicates long run equilibrium relationship between FDI and GDP. Granger causality test indicates unidirectional relationship running from GDP to FDI, so it is concluded that GDP Granger causes to FDI and inflow of FDI in India is economic growth induced. The findings suggest government must devise strategies that will lead to economic growth as inflow of FDI follows to economic growth. [ABSTRACT FROM AUTHOR]

This study examines the integration of Indian stock markets with other world markets. There has been a phenomenal growth in Indian stock markets since 2003; however the Indian markets have weathered the Global economic crisis and the European Debt crisis during the recent past. The majority of Investments in Indian stock markets have been done by Foreign Institutional Investors particularly from US. However; the volatility in Indian stock markets was felt during the European debt crisis. Hence it is felt important to understand the cointegration of Indian equity markets with US and European equity markets and also integration with other major Asian Markets. This study employed Vector Autoregressive (VAR) as suggested by Johansen and Vector Error Correction Model (VECM) to study the long run and short run dynamics between the different markets. [ABSTRACT FROM AUTHOR]

The article presents a study to understand the behavior of money velocity for different money stock measures in India for the period 1982 -- 2012 along with the important factors by which they get influenced. The first part checks the random walk hypothesis for money velocity fluctuations. The results show that the velocities do not exhibit random walk behavior which proved to be useful for analysis of money velocity dynamics. The causality tests show that the recent developments in electronic transactions are yet to have an impact on money velocity. The last part analyzes the impact of external sectors which includes the trade, the debt inflows, remittances from abroad, investments in India and the interventions from Reserve Bank of India on money velocity suitably in a VAR (1) framework and responses of the variables in future to shocks in the other variables at present have been analyzed using impulse response functions. [ABSTRACT FROM AUTHOR]

The approach paper to the Twelfth Five Year Plan focuses on promoting the tourism sector and considers it as a harbinger of inclusive growth. As per the WTTC, 2011, the direct and indirect contribution of the Indian tourism industry to the total Gross Domestic Product (GDP) and employment is found to be 15.8 per cent and 18.4 per cent, respectively. Hence, the objective of this paper is to analyse the role of the tourism industry in promoting employment and economic growth. In view of the use of the VAR model, the GDP, Foreign Tourist Arrivals (FTAs), Foreign Exchange Earnings (FEEs) and Employment have been taken as the variables. The endeavour was to investigate the relationship between tourism, economic growth and employment. While the annual data for India for the period 1971-2010 has been used, the results reveal that there is a long-run relationship between tourism, on one hand, and economic growth and employment, on the other. The Granger Causality Test indicates that there is a unidirectional impact from FEE and FTA to the GDP and from the GDP to employment. [ABSTRACT FROM AUTHOR]

Abstract: This article looks at the preconditions that an emerging economy needs to fulfill, before it can adopt inflation targeting as a monetary policy regime. The study is conducted using the Indian economy as a case study. We conduct sector-wise analysis of the Indian economy to evaluate the independence of India's monetary policy from fiscal, external, structural and financial perspectives. Dominance from any of these sectors may divert monetary policy from the objective of maintaining price stability in the economy. Our analysis suggests that among the four dominance issues, the issue of 'structural dominance' is the most acute for India. Supply shocks, hitting the economy due to structural bottlenecks, pose a major threat to the independent conduct of monetary policy. This study concludes that inflation band targeting with a wide target range would be a feasible monetary policy option for India. [ABSTRACT FROM AUTHOR]