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The purpose of this study is to investigate the Ripple Effect of house prices at an inter-suburban level of analysis in the Sydney metropolitan area. By doing this, more practical information of price transmission can be provided to improve residential real estate purchasing decisions of market participants. Equity from residential real estate is a major component of household wealth and is frequently used to improve and upgrade homes. With the ever-increasing prices of real estate in Sydney making more efficient purchasing decisions can grow this wealth quicker allowing a household to obtain financial related goals at a quicker pace.

Using a two-stage sampling technique strings of adjoining suburbs from different Sydney regions were analysed using a combination of price graphs, Engle–Granger and Johansen co-integration techniques and Granger causality tests.

Pairwise co-integration was lacking throughout the various suburb strings, whereas multivariate co-integration was found in the lower priced areas further from the central business district, as these areas also experience less price volatility. The geographical location of suburbs therefore plays an important role in the ability to predict an individual suburb’s price movements. For a prominent Ripple Effect to exist at this level the best conditions would consist of a singular demand centre with restricted geographical space to which this demand can spread. Causal pathways were subsequently mapped for each suburb string identifying price transmission pathways and confirming support that while the standard Ripple Effect does not exist at an inter-suburban level, it is still possible to predict price movements by considering the price behaviour of surrounding suburbs.

This paper adds to the literature by examining the Ripple Effect across different suburbs in Sydney. This is done via an extensive search through the literature and analysing recent real estate data.

This study aims to find out the dynamic and causal long-run and the short-run relationship between energy consumption (electricity usage) and energy production (electricity creation) and also find out the relationship of these two variables based on past values for the SAARC nations (Pakistan, India, Bangladesh, Sri Lanka and Nepal).

Vector auto-regressive (VAR), auto-regressive distributive Lag (ARDL) and Granger causality test have been used in this study to estimate the dynamic and causal relationship between variables.

The unit-root tests were found insignificant at a magnitude but significant at the initial difference. VAR test results were found insignificant, which means co-integration among variables exists, which was tested by ARDL approach. Results suggested that energy consumption has a short-run relationship with energy production, but it was found insignificant in the other way round. The results of this study also suggest that both variables cause each other in the long run.

This study was conducted in a limited environment as we do not have access to energy policies of SAARC countries, and also data access was limited; only five countries’ data was available. This study can help government bodies and policymakers to exchange the electricity across borders to diminish the electricity shortage in the SAARC region, as countries with abandoned resources can produce electricity at a little cost.

Penal data for this study was collected from World Development Indicators from the year 1971 to 2015.

The purpose of this paper is to examine the extent of financial integration occurring in East Asia. Increasing economic integration in East Asia over the last two decades has been evidenced by consistent growth in intra‐regional trade and investment. Greater economic integration in the region, accompanied by financial deregulation and liberalization, has contributed to greater financial integration. This study confirms increasing degree of financial market integration in East Asia by comparing movements of monthly money market rates before and after the Asian financial crisis. Convergence of interest rates across the countries in East Asia is examined by analyzing deviations, correlation coefficients and multivariate co‐integration tests of interest rates.

This study aims to examine the relationship between electricity consumption per capita (ELEC) and real per capita income (Y), as the direction of causation of this relationship remains controversial in the existing literature. It also seeks to explore the relationship between energy consumption per capita (ENC) and real per capita income, over a 34‐year period (between 1975 and 2009).

The study uses Johansen cointegration technique to determine the short‐ and long‐run relationship between the variables. The authors also utilize Granger causality test to determine the causal relationship between the selected variables.

The study provides evidence of bi‐directional causality between the electricity consumption per capita and real per capita income on one hand; and energy consumption per capita and real per capita income on the other hand as the direction of causality has significant policy implications.

This study does not include all dimensions of the energy growth, but is limited to the three variables which the authors consider to be critical to economic development, including energy consumption, electricity consumption and economic growth.

The study uses a sophisticated econometric technique with additional tests of forecasting framework to examine the effect of energy demand on economic growth over a period of the next ten years, i.e. 2010‐2019, in the context of Pakistan. The impulse response describes the reaction of the system as a function of independent variable that parameterizes the dynamic behavior of the system.

The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO₂ emissions, farming areas and the labor force in China.

This analysis applied the autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method and Johansen co-integration test to predict long-term co-integration and relation between variables. Four machine learning methods are used for prediction of the accuracy of climate effect on fruit production.

The Johansen test findings have shown that the fruit crop growth, energy use, CO₂ emissions, harvested land and labor force have a long-term co-integration relation. The outcome of the long-term use of CO₂ emission and rural population has a negative influence on fruit crops. The energy consumption, harvested area, total fruit yield and agriculture labor force have a positive influence on six fruit crops. The long-run relationships reveal that a 1% increase in rural population and CO₂ will decrease fruit crop production by −0.59 and −1.97. The energy consumption, fruit harvested area, total fruit yield and agriculture labor force will increase fruit crop production by 0.17%, 1.52%, 1.80% and 4.33%, respectively. Furthermore, uni-directional causality is correlated with the growth of fruit crops and energy consumption. Also, the results indicate that the bi-directional causality impact varies from CO₂ emissions to agricultural areas to fruit crops.

This study also fills the literature gap in implementing ARDL for agricultural fruits of China, used machine learning methods to examine the impact of climate change and to explore this important issue.

Financial sector reform measures, which were initiated in 1991, have provided some degree of maturity and integration of different segments of India's financial markets. The purpose of this paper is to articulate the impact of financial sector reform measures on integration of various segments of financial markets in India.

The paper surveys various methodologies for measurement of financial integration and uses the recently developed technique of co‐integration in a VAR framework to assess the extent of integration of various segments of India's financial markets.

The paper concludes that the financial market integration is inconclusive in India. Only a few segments of money market, Gilt market and foreign exchange market are integrated. Interest rate parity does not hold in India's case, which indicates poor evidence in support of international integration of domestic financial markets. Similarly, the analysis of the relationship between domestic saving and domestic investment does not support international integration. The study of co‐integration of Nasdaq and Bombay sensitive index (BSE), also revealed absence of international integration.

Owing to non‐availability of time series data, the paper could not consider the mutual fund market, pension market and various derivatives markets in the overall process of assessment of financial integration. However, the impact on the findings is minimal, as these markets are not so far developed in India.

The findings have significant practical implications particularly in the formulation of policies on management and interventions in the money market, foreign exchange and equity markets and in the overall formulation of monetary policy for the economy.

This paper presents a quite comprehensive research study on financial integration in India and is original, particularly in the area of application of the co‐integration technique for assessment of financial integration.

This paper seeks to examine the validity of Wagner's Law using annual data (1957‐2006) for the US state‐local government (SLG) real expenditure and eight of its sub‐categories.

The co‐integration tests of Johansen and the bounds testing approach to co‐integration proposed by Pesaran et al. were carried out to determine whether a long‐term equilibrium relationship existed between real per capita GDP (pcgdp) and the expenditure variables scaled by real GDP. The income elasticity coefficients of the expenditure variables were then estimated. The direction of causality was tested in the context of error‐correction models (ECM) and the Toda‐Yamamoto approach, which allows for estimating level relationships without pre‐testing for unit roots.

Most SLG expenditure variables were found to be non‐stationary and income‐elastic. However, with the exception of total expenditure (te), insurance trust benefits (ins) and social services and income maintenance (ssim), no other non‐stationary expenditure variable was co‐integrated with pcgdp and error‐corrected over time. The ECM results suggested that te, ins and ssim were driven by pcgdp, consistent with a Wagnerian causal ordering. The Toda‐Yamamoto approach, however, indicated that in these and a few other cases the causal effect was bidirectional.

This paper provides a fairly comprehensive test of Wagner's Law at the US sub‐national government level with an emphasis on the concepts of co‐integration and (long‐run) causality in the income‐expenditure nexus. Its findings underscore the importance of using disaggregated expenditure measures to test Wagner's Law, as they suggest that some, but not all, rapidly growing and non‐stationary expenditure sub‐categories were decoupled from pcgdp in the long run.

The purpose of this paper is to present a method of forecasting the development of the market shares of competitors in a pursuit race.

The statistical concept of co‐integration is applied to a state space model in order to operationalize the pursuit race in competition. Two time series served as data input for the designed model. They document the development of the monthly market shares of German savings banks and German cooperative banks with respect to the end‐of‐month volume of non‐bank deposits.

The analysis of the competition between the two bank groups proves that the development of their market shares is co‐integrated in the shape of a long lasting pursuit race. Therefore, the forecasts of the changes in market shares are very accurate and reliable.

With the help of such a statistical analysis, a competitor is able to monitor the development of competition effectively to forecast the changes that are to be expected. Thus, the method described here helps the firm to react in time to the movements of its competitors.

This paper is an important contribution to gain insight in the quantitative dynamics of competition according to co‐integration by the parameters of state space model.

The purpose of this paper is to study impacts of changes in crude oil price, money supply, fiscal deficit and effective exchange rate on India’s economic growth (expressing all variables in real term).

First, a simple macroeconomic model is formulated to this effect. Next, linear autoregressive distributed lag procedure and vector error-correction model are applied for growth empirics. Annual data are used from 1977 through 2015.

Rises in real crude oil price and monetized real fiscal deficits have negative short-run and long-run effects on real economic growth. Increase in real money supply and real effective exchange rate appreciation helps promote real economic growth in both short run and long run. In all cases, there is evidence of net interactive positive feedback effects among the variables in the short run. Real effective exchange rate appreciation dampens exports, but it is helpful to imports of capital goods and crude oil that contribute to economic growth. So, the net effect on the economy may be conjecturally positive.

To the best of the authors’ knowledge, this paper is unique because of the formulation of macro-economic model pertaining to the topic and its subsequent empirical verification. Moreover, this paper seems more comprehensive than some other studies, cited in the literature review.

In financial analysis, forecasting often involves regressing one time series variable on another. However, to ensure that the models are correctly specified, one needs to first test for stationarity, co‐integration and causality. In testing for causality, the variables should be stationary. If non‐stationary, one can estimate the model in difference form, unless the variables are co‐integrated. This article determines whether cash flow and earnings variables are stationary, and which variable causes the other, using econometric analysis. In most cases, cash flow variables are found to cause earnings variables. This is so when the models are estimated in levels. However, when estimated in first differences, the causal relationship tends to be reversed such that earnings cause cash flows. Further study is recommended, whereby panel data could be used to improve the power of the tests.