Search results

The aim of the study is to evaluate role of trade war between the United States and China on oil price. For this purpose, global oil price and US trade balance with China are selected as variables. In addition to this issue, monthly data of these variables for the periods between 1990 and 2019 are taken into consideration. In the evaluation process, both Engle–Granger cointegration and Toda–Yamamoto causality analysis are considered. The results of Engle–Granger cointegration analysis indicates that there is a ­relationship between trade war and oil prices. Nevertheless, according to the results of Toda–Yamamoto causality analysis, it is identified that trade war does not cause oil prices. While looking at these results, it is determined that trade war between the United States and China has an influence on the oil price changes. However, it is also understood that it is not the main factor of this volatility. Thus, it is recommended that in order to identify the main indicator of the oil price volatility, some different factors should also be taken into consideration.

The purpose of this paper is to examine asymmetric co-integration effects between nutrition and economic growth for annual South African data from the period 1961-2013.

The authors deviate from the conventional assumption of linear co-integration and pragmatically incorporate asymmetric effects in the framework through a fusion of the momentum threshold autoregressive and threshold error correction (MTAR-TEC) model approaches, which essentially combines the adjustment asymmetry model of Enders and Silkos (2001); with causality analysis as introduced by Granger (1969); all encompassed by/within the threshold autoregressive (TAR) framework, a la Hansen (2000).

The findings obtained from the study uncover a number of interesting phenomena for the South Africa economy. First, in coherence with previous studies conducted for developing economies, the authors establish a positive relationship between nutrition and economic growth with an estimated income elasticity of nutritional intake of 0.15. Second, the authors find bi-direction causality between nutrition and economic growth with a stronger causal effect running from nutrition to economic growth. Lastly, the authors find that in the face of equilibrium shocks to the variables, policymakers are slow to responding to deviations of the variables from their co-integrated long run steady state equilibrium.

In the study, the authors make a novel contribution to the literature by exploring asymmetric modelling in the correlation between nutrition intake and economic growth for the exclusive case of South Africa.

This paper tests for longrun relationships among the national equity markets of the G'7 countries using the Engle‐Granger two‐step procedure. Results indicate that cointegration is the norm among these seven equity markets in the post‐Bretton Woods period. Further, market adjustments to system equilibria are accelerating as one moves toward the present implying that markets are becoming more integrated.

The purpose of this paper is to provide an extensive review of the monetary model of exchange rate determination which is the main theoretical framework on analyzing exchange rate behavior over the last 40 years. Furthermore, we test the flexible price monetarist variant and the sticky price Keynesian variant of the monetary model. We conduct our analysis employing a sample of 14 advanced economies using annual data spanning the period 1880–2012.

The theoretical background of the paper relies on the monetary model to the exchange rate determination. We provide a thorough econometric analysis using a battery of unit root and cointegration testing techniques. We test the price-flexible monetarist version and the sticky-price version of the model using annual data from 1880 to 2012 for a group of industrialized countries.

We provide strong evidence of the existence of a nonlinear relationship between exchange rates and fundamentals. Therefore, we model the time-varying nature of this relationship by allowing for Markov regime switches for the exchange rate regimes. Modeling exchange rates within this context can be motivated by the fact that the change in regime should be considered as a random event and not predictable. These results show that linearity is rejected in favor of an MS-VECM specification which forms statistically an adequate representation of the data. Two regimes are implied by the model; the one of the estimated regimes describes the monetary model whereas the other matches in most cases the constant coefficient model with wrong signs. Furthermore it is shown that depending on the nominal exchange rate regime in operation, the adjustment to the long run implied by the monetary model of the exchange rate determination came either from the exchange rate or from the monetary fundamentals. Moreover, based on a Regime Classification Measure, we showed that our chosen Markov-switching specification performed well in distinguishing between the two regimes for all cases. Finally, it is shown that fundamentals are not only significant within each regime but are also significant for the switches between the two regimes.

The results are of interest to practitioners and policy makers since understanding the evolution and determination of exchange rates is of crucial importance. Furthermore, our results are linked to forecasting performance of exchange rate models.

The present analysis extends previous analyses on exchange rate determination and it provides further support in favor of the monetary model as a long-run framework to understand the evolution of exchange rates.

This paper aims to empirically examine the relationship between macroeconomic factors and housing prices in Malaysia from 1991 to 2016.

This study was based on annual macroeconomic data from 1991 to 2016. For the model that was constructed, house prices were treated as the dependent variable and other variables, namely, the interest rate, the gross domestic product (GDP) and the consumer price index (CPI) as explanatory or regression variables. All these data come from the annual publication of the World Bank’s statistical report. In the meantime, data on house prices have been obtained through the publication of the National Property Information Centre in the form of an index. In the study, the bounds testing approach for cointegration was used because the Engel Granger cointegration tests can be used only if all the series are stationary at first difference. And it is against this background the author could not use it because our variables are a combination of series integrated of orders I (0) and I (1).

The results of this study revealed a positive equilibrium relationship in the long run between interest rate, CPI and housing prices and negative for the GDP. In addition, in the short run, housing prices were shown to have a positive relationship with GDP and CPI, but no relationship with the interest rate, based on the assumption that “all other things being equal”.

The data on economic factors (GDP, RI and CPI) used in this study are secondary and not from the Malaysian Department of Statistics and Economic Studies. Furthermore, the result of this research only reflects the Malaysian reality, and therefore, cannot be generalised to the entire housing market worldwide.

The result of this study can be used for housing valuation by Malaysian property market players, investors and especially policymakers.

This study’s findings can help Malaysian policymakers to limit the high price fluctuation in the housing industry and help Malaysian citizens to buy their own homes at a reasonable price.

The contributions of this study are structured around two points. The first is the contribution to the body of knowledge, where the results of the study will contribute to the growing number of literature in the housing sector. Moreover, the second contribution of this document is the strength of its recommendation to policymakers. This is because it analyses only the main macroeconomic determinants (IR, GDP and CPI) that are essential to better influence the housing sector as quickly as possible, as opposed to those that use many variables that could lead to possible specification errors.

This paper explores the evidence of a long-run co-movement between aggregate unemployment insurance spending and the labor force participation rate in the USA. The unemployment insurance (UI) program tends to expand during an economic downturn and contract during an expansion. UI may incentivize unemployment and may also facilitate better matching in the labor market. Statistical evidence of the presence of a co-movement will thus shed new light on their dynamics.

This research applies time-series econometric approach using monthly data from 1959:1 to 2020:3 to test threshold cointegration and estimate a threshold vector error-correction (TVEC) model. The estimates from the TVEC model investigating the nature of short-run dynamics.

The Enders and Siklos (2001) test find evidence of threshold cointegration between the two indicating the presence of long-run co-movement. The estimates from the TVEC model investigating the nature of short-run dynamics find evidence that the growth in aggregate UI spending and the growth in labor force participation rate adjust simultaneously to maintain the long-run co-movement above the threshold in the short run. The author also observes the same short-run dynamics for the growth in aggregate UI spending and the growth in the labor force participation rate for females.

This model is bi-variate by construction and does not address causality.

The author argues that the UI program positively impacts the female labor market outcomes, for example, better matching. This finding may explain the upward trend in the labor force participation rate for females in the USA.

The research findings may justify the transfer programs for minority and immigrants.

This is first research that analyzes the UI programs impact on the labor force participation using a macroeconometric approach. To the best of the author's knowledge, this is the first study in this genre.

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.

This study estimates a Vector Error Correction Model (VECM) that incorporates the linkages among the agriculture, industry, construction, transport, storage and communication and service sectors for Bangladesh by using historical data from 1979 to 2009. Two cointegrating vectors confirm that all the different sectors moved together over the sample period, and therefore that their growth rates are interdependent. The long-run relationships of the industrial, construction, transport, storage and communication and service sectors to the agricultural sector were established, and the results show that the industrial and construction sectors contribute positively to the agricultural sector, the growing service sector contributes negatively and the transport, storage and communication sector shows mixed results. In addition, weak exogeneity for the agricultural sector is rejected and this underlines the fact that the agricultural sector should be considered by policymakers in any analysis of inter sector growth.

Sequel to the results of the preceding chapter that depicted positive associations of credit with the indicators of growth and development, the present chapter aims at investigating the interrelationships of credit with GDP and HDI separately in a bivariate framework for the selected countries for the period 1990–2019. For this purpose, this chapter first develops a theoretical model in line with the Barro (1991) model where bank credit is introduced as a good institutional component of endogenous growth. Then, it goes for a time series exercise to establish the long-run relations and short-run dynamics for the pairs of variables, credit-GDP and credit-HDI, to justify the linkages between the financial sector and the real sector. The study arrives at mixed results across the countries. In many cases, credit has been identified to be strongly related to income and development indicators in the long run through cointegrated stable relationships. Furthermore, credit makes a causal influence on GDP and HDI in some developed countries whereas GDP becomes a causal factor to credit in some developing countries. It is thus recommended for further aggravation of the two sectors’ linkages under the patronisations of the governments and the monetary authorities of the countries to have high growth of income and development so that a part of the sustainable development goal can be achieved through the financial sector.