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The aims of the paper are to investigate IT software and service export function for India. First, cointegration tests have been used to investigate the long-run equilibrium relationship of the given variables. Second, long-run coefficients and associated error correction mechanism are estimated.

Annual time series data on IT software and service exports, human capital, exchange rate, investment in IT, external demand and openness index have been used for the present study during the period 1980–2017. The data are collected from the National Association of Software and Service Companies (NASSCOM), Planning Commission of India, University Grants Commission (UGC) of India, real effective exchange rate (REER) database and World Bank development indicators. Auto regressive distributed lag (ARDL) model is used to analyze both short-run and long-run dynamic behaviour of economic variables with appropriate asymptotic inferences.

Results of the analysis show the stable long-run equilibrium relationship among the given variables. It is found that external demand, exchange rate, human capital and openness index have a substantial long-run impact on the IT software and service exports. We also found that the coefficient of error correction term is negative and significant at 1% of the level of significance, which confirms the existence of stable long-run relationship which means adjustment will take place when there is a short-run deviation to its long-run equilibrium after a shock.

There may be other determinants of software and service exports apart from those considered by the present study. Due to the non-availability of data, the study considers only important determinants that determine the software and service exports in India. The IT exports are an emerging and dynamic field of economic activity and the rate of change is so rapid that the relevance of individual factors may change over time. The study period is also limited to available data.

The paper has implications for achieving sustainability in IT software and service exports growth. It is recommended that policies directed at improving the performance of IT software and service exports should largely consider the long-run behaviour of these variables.

This paper focuses on originality in the analysis of the relationship among the given variables including IT software and service exports, human capital, exchange rate, investment in IT, external demand and openness index in India. All the work has been done in original by the authors, and the work used has been acknowledged properly.

The purpose of this paper is to investigate both long-run and short-run dynamics among the software and services export, investment in information technology (IT) and GDP in India and to investigate the direction of the relationship among the given three macro-economic variables.

The time series data have been taken to investigate the long-run relationship exists among the variables. Annual data were collected from the NASSCOM Annual Reports, Planning Commission of India and Reserve Bank of India during the period 1980–2016. Cointegration and vector error correction model have been used for analyzing the causal relationship among investment in IT, software exports and GDP in India.

Cointegration results confirm that software and services export, investment in IT and GDP are cointegrated, implying that there exists the long-run equilibrium relationship among the given three macro-economic variables. Similarly, vector error correction mechanism Granger causality results hold that there is uni-directional long-run causality running from software and services export and investment in IT to GDP, implying that software and services export is an important determinant of economic growth in India.

The limitations of the paper are generalization of the results and proxy variable for IT investments.

The paper has implications for the expansion of market concentration, diversification of software and service exports, and investments in R&D for increasing competitiveness of the industry in the global market.

This paper focuses on originality in the analysis of the relationship among the given variables software exports, investment in the IT sector and GDP in India. All the work has been done in original by the authors and the work used have been acknowledged properly.

The study aims to empirically examine the relationship between monetary policy and economic growth, as well as to explore the long-run and the short-run effect of monetary policy on the economic growth of a developing country (Bangladesh) and a developed country (the United Kingdom).

Depending on data availability, the study employed secondary data covering the period of 1980–2019. The augmented Dickey–Fuller test and the Phillips–Perron test were used for the stationarity test. Further, the F-bounds test was run to justify the long-run relationship between monetary policy and economic growth. Thereafter, long-run coefficients were revealed from the auto-regressive distributed lag (ARDL) model and short-run coefficients from the error correction model. Furthermore, the vector error correction model (VECM) Granger causality approach was employed to demonstrate the causality of studied variables. Lastly, different diagnostics tests ensured the robustness of the models.

F-bounds test outcomes suggest that monetary policy has a long-run relationship with economic growth in both countries. Long-run coefficients revealed that money supply has a positive long-run impact on economic growth in both countries. Unlike the UK, the exchange rate exhibits an adverse effect on the economic growth of Bangladesh. The bank rate seems to promote economic growth for the UK. Findings also depict that increase in lending interest rates hurts the economic growth for both countries. Besides, the short-run coefficients portray random effects at different lags in both cases. Lastly, causality among studied variables is revealed using the VECM Granger causality approach.

The novelty of this study lies in consideration of both developing and developed countries in the same study.

This study aims to establish the dynamic relationship between international crude oil prices and Indian stock prices represented by the Bombay Stock Exchange (BSE) energy index.

Using Johansen’s cointegration test, vector error correction (VEC) model, impulse response function and variance decomposition test the study tries to ascertain the short-term and long-term dynamic association between the oil price shock and the movement of stock price and Granger causality test is applied to find out the nature of causality.

Considering vector autoregression estimation, the present study analyzes the relationship between the variables and tries to make a valid conclusion. The result of the co-integration test exhibits the presence of a long-term association between these two macro-economic variables during the period under study. Also, in the short-run VEC Granger causality result reveals that the movement of international crude oil price significantly influences the Indian stock price.

To get a more robust result the study can be further extended by taking a longer time period with data of shorter time-frequency such as daily or weekly and further by using more sophisticated econometric and statistical tools. Further, the study can be extended to firm-level investigation considering the forward trading concentration with the Indian oil basket.

In today’s globalized era, forecasting of share price movement helps investors in predicting the market and invest accordingly. Through this liquidity of the markets enhance and markets become more active in the global arena.

This study represents fresh findings in the changing time period the linkage between crude oil prices and stock prices which are of value to the academicians, researchers, policymakers, investors, market regulators, etc.

This study aims to examine the short- and long-term equilibrium relationship between All share price index (ASPI), macroeconomic variables and the economic crisis in Sri Lanka.

Monthly time series data for inflation (CPI), industrial production (IP), an exchange rate (EX), an interest rate (TB), short-term interest rate (CD) and economic crisis were used from 2010 to 2021. The ADF test, the bound testing approach, the CUSUM test and the CUSUMQ test were used in this study.

The findings show a long-run stable relationship between stock price, macroeconomic variables and political crisis (i.e., CPI, IP, ER, TB, CD and economic crisis). The results of the Johansen cointegration test suggest that there is at least one cointegrating equation, indicating that there is a long-run equilibrium relationship between macroeconomic variables and stock prices in Sri Lanka.

The vector error correction estimates show that the coefficient of the error correction term is significant with a negative sign, indicating that a long-run dynamic relationship exists between macroeconomic variables and stock prices. In the short term, economic crisis has had a big effect on stock prices suggesting that Sri Lanka’s domestic financial markets are linked to the stability of the country.

This research establishes the links between stock returns, macroeconomic variables and economic crisis. So far, research has been unable to establish the empirical nature of such links. The authors believe that this paper fills that gap.

By using the annual time series data from 1986 to 2008 in South Korea (hereafter Korea) we will examine the possibility that the difference in trade liberalization is causal to the differentials in growth and measure the sensitivity of growth to changes in trade liberalization. For the estimation, we will use both alternative measures of trade liberalization; imports for the total factor cost of national income for trade openness (hereafter openness) and the sum of exports and imports for the total factor cost of national income for trade globalization (hereafter globalization). The regression results suggest that both openness and globalization make a substantial contribution towards Korea’s economic growth. In a comparison between these two indicators of trade liberalization, openness is more sensitive to growth. A negative and statistically significant error correction term implies that the null hypothesis of no co-integration is rejected when one period lag is used. The existence of co-integration means that openness and globalization policies can be targeted in order to bring about a desired long-run effect as well as a short run effect on growth.

The author analyzes households' inflation expectations data for India, collected quarterly by the RBI for more than a decade. The contribution of this paper lies in two folds. First, this study examines the relationship between relatively recent inflation expectations survey of households (IESH) and the actual inflation for India. Secondly, the author employs a structural VAR with the time period 2006 Q2 to 2020 Q2 on inflation expectation survey data of India. A short-term non-recursive restriction is imposed in the model in order to capture the simultaneous co-dependence causal effect of inflation expectation and realized inflation.

This paper studies the dynamic behavior of inflation expectations survey data in two folds. First, the author analyzes the time series property of the survey data. The author begins with testing the stationarity property of the series, followed by the casual relationship between the expected and actual inflation. The author further examines the short-run and long-run behavior of the IESH with actual inflation. Employing autoregressive distributed lag and Johansen co-integration, the author tested if a long-run relationship exists between the variables. In the second approach, the author investigates the determinants of inflation expectations by employing a non-recursive SVAR model.

The preliminary explanatory test reveals that inflation expectation is a policy variable and should be used in monetary policy as an instrument variable. The model identifies the price puzzle for India. The author finds that the response of inflation to a monetary policy shock is neutral. The results also indicate that the expectations of the general public are self-fulfilling.

IESH has only commenced from September 2005, hence is relatively new as compared to other survey in developed countries. Being a new data set so far, the author could not locate any study devoted in analyzing the behavior of the data with other macroeconomic variables.

This paper aims to explain real exchange rate fluctuations by means of a model including both standard fundamentals and two alternative measures of inflation expectations for five inflation targeting countries (the UK, Canada, Australia, New Zealand and Sweden) over the period January 1993–July 2019.

Both a benchmark linear autoregressive distributed lag (ARDL) model and a nonlinear autoregressive distributed lag (NARDL) specification are considered.

The results suggest that the nonlinear framework is more appropriate to capture the behaviour of real exchange rates given the presence of asymmetries both in the long and short run. In particular, the speed of adjustment towards the purchasing power parity (PPP) implied long-run equilibrium is three times faster in a nonlinear framework, which provides much stronger evidence in support of PPP. Moreover, inflation expectations play an important role, with survey-based ones having a more sizable effect than market-based ones.

The focus on linearities and the estimation of a NARDL model, which is shown to outperform the linear ARDL model both within sample and out of sample, is an important contribution to the existing literature which has rarely applied this type of framework; the choice of an appropriate econometric method also makes the policy implications of the analysis more reliable; in particular, monetary authorities should aim to achieve a high degree of credibility to manage them and thus currency fluctuations effectively; the inflation targeting framework might be especially appropriate for this purpose.

The purpose of this study is to investigate the role of natural resources in economic growth by taking evidence from Pakistan.

Total five variables are used in this study, i.e. GDP, population density, water renewable resources, deforestation and the emissions of CO₂, based on time series data from 1972 to 2016. The annual data is collected from World Development Indicators, Food and Agriculture Organization and Pakistan Economic Survey. Vector error correction model technique is applied to find out the long-run results.

Results depict that all variables have a negative and significant relationship over the long run at 5% level of significance. It is observed that 1% increase in population accordingly will degrade GDP by 0.334496%. Correspondingly, 1% increase of water renewable resources will degrade GDP by 0.450647%. Findings are aligning with the study of. Moreover, 1% increase in deforestation will diminish GDP by 0.127821%. If we increase 1% of CO₂, GDP will be reduced by 0.802420%.

Results depict that all variables have a negative and significant relationship over the long run at 5% level of significance. It is observed that 1% increase in population accordingly will degrade GDP by 0.334496%. Correspondingly, 1% increase of water renewable resources will degrade GDP by 0.450647%. Findings are aligning with the study of. Moreover, 1% increase in deforestation will diminish GDP by 0.127821%. If we increase 1% of CO₂, GDP will be reduced by 0.802420%.

Family planning may be our last hope. Viable and fruitful family planning ought to be introduced. Status of ladies should be brought up in the society by providing education and employment opportunities. Time of marriage ought to be brought up to 25 years in case of males and 23 in case of females; this can help in decreasing the number of births. Having a large population will not automatically translate into economic prosperity. Investment in well-being, education, sound economic policies and good governance will bring about accelerated economic growth.

In recent years, the issue of worldwide water shortage has attracted increasing consideration within scholarly community, non-administrative organizations and the media. Water shortage is a significant and ever-increasing danger to the environment, human well-being, advancement, energy security and the worldwide food supply. This work will introduce real issues and requirements relating to water, environmental changes and their impact on economic growth of Pakistan.

The purpose of the study is to find out the impact of gold and oil prices on the stock market.

This study uses the data on gold prices, stock exchange and oil prices for the period 1991–2016. This study applied descriptive statistics, augmented Dickey–Fuller test, correlation and autoregressive distributed lag test.

The data analysis results showed that gold and oil prices have a significant impact on the stock market.

Following empirical evidence of this study, the authors recommend that investors should invest in gold because the main reason is that hike in inflation reduces the real value of money, and people seek to invest in alternative investment avenues like gold to preserve the value of their assets and earn additional returns. This suggests that investment in gold can be used as a tool to decline inflation pressure to a sustainable level. This study was restricted to use small sample data owing to the availability of data from 1991 to 2017 and could not use structural break unit root tests with two structural break and structural break cointegration approach, as these tests require high-frequency data set.

This study provides information to the investors who want to get the benefit of diversification by investing in gold, oil and stock market. In the current era, gold prices and oil prices are fluctuating day by day, and investors think that stock returns may or may not be affected by these fluctuations. This study is unique because it focusses on current issues and takes the current data in this research to help investment institutions or portfolio managers.