Long-run Parameters Research Articles

Dynamic interaction between financial development and natural resources: Evaluating the ‘Resource curse’ hypothesis

The role of natural resources in promoting economic and financial activities is important for attaining country's economic growth. The study used different natural resource rents, domestic investment, trade openness, per capita income and their resulting impact on financial development in order to assess ‘financial resource curse’ hypothesis in Pakistan by using a consistent time series data from 1975 to 2017. The study employed ARDL-Bounds testing approach that is fairly worked under different order of integrated variables and displayed short- and long-run parameter estimates. Further, the study used VAR decomposition analysis to generate Impulse Response Function (IRF) and Variance Decomposition Analysis (VDA) to assessed forecasted variance and error shocks over a next 10 year time period. The results show that, in the short-run, initial level of forest rents and oil rents supported the ‘natural resource abundance’ hypothesis, as both the rents substantially increases country's financial development, however, in the long-run, there is a negative relationship of coal rents, forest rents, natural gas rents, and oil rents with domestic credit to private sector (DCPS), which confirmed the ‘natural curse hypothesis’ in a country. The domestic investment in the form of gross fixed capital formation (GFCF) largely supported the financial activities with all given resource rents in the models, while country's per capita income unable to signify its positive impact on DCPS under the resource curse environment during the study time period. The other results show that coal rents and oil rents decreases broad money supply whereas natural gas rents decreases market capitalization to validate ‘financial resource curse’ hypothesis in a country. The results of IRF and VDA approach confirmed the viability of both the competing natural resource theories (i.e., financial resource curse and financial resource blessing hypothesis) under financial development in the next 10 year time period.

Do agriculture-based economies mitigate CO2emissions?

PurposeThis study aims to fill the gap in income-environment literature by adding agricultural contribution to the nexus. The authors investigate the short-run and long-run impact of agricultural contribution, renewable energy consumption, real income, trade liberalisation and urbanisation on carbon emissions for a balanced panel of five South Asian Association for Regional Cooperation (SAARC) countries spanning the period 1990-2013.Design/methodology/approachPedroni and Kao cointegration techniques have been used to test the existence of long-run relationship between the variables. The directions of causal relationships have been verified using Granger causality tests. Further, the long-run parameters of the baseline equation have been estimated by using the fully modified ordinary least squares, the technique developed by Pedroni, (2001a) for heterogeneous cointegrated panels.FindingsThe result reveals that agricultural contribution and renewable energy consumption improve environmental quality in the long run, while urbanisation and per capita real income degrade it. The study did not find any evidence of “pollution heaven hypothesis” in the selected countries. The Granger causality tests confirm bidirectional causality between carbon emissions and income and between carbon emissions and urbanisation. In addition, there is unidirectional causality running from agricultural contribution to renewable energy consumption.Originality/valueThis is the only study to investigate the role of agriculture sector in carbon mitigation from a panel of South Asian economies. To the best of the authors’ knowledge, it is also the first study to test the applicability of “pollution heaven hypothesis” for SAARC countries.

Industrial growth and emissions of CO2 in Ghana: The role of financial development and fossil fuel consumption

The rapid rise in greenhouse gas emissions have become a global concern catching the attention of policy makers and researchers all over the world. Fossil fuel combustion has been named as the major source of greenhouse gas emissions, meanwhile, studies focusing on fossil fuel impact on CO2 emissions are rare for developing countries including Ghana. This study employed the ARDL procedure with structural breaks and the Bayer–Hanck joint cointegration approach to examine the validity of the EKC hypothesis in the dynamic linkage between industrial growth and emissions of carbon dioxide (CO2) in Ghana, capturing the role of fossil fuel consumption and financial development. The variables are found to be cointegrated and both the short-run and the long-run parameters showed evidence of a U-shaped relationship between industrial growth and CO2 emissions which was further confirmed by the Lind and Mehlum U-test. The short-run causality revealed a uni-directional causality running from fossil fuel consumption to emissions of CO2. For policy purposes, the study advocates for efficient and low carbon emission technologies.

Inference in heavy-tailed vector error correction models

This paper first studies the full rank least squares estimator (FLSE) of the heavy-tailed vector error correction (VEC) models. It is shown that the rate of convergence of the FLSE related to the long-run parameters is n (sample size) and its limiting distribution is a stochastic integral in terms of two stable random processes when the tail index α∈(0,2). Furthermore, we show that the rate of convergence of the FLSE related to the short-term parameters is n1∕αL̃(n) and its limiting distribution is a functional of two stable processes when α∈(1,2), where L̃(n) is a slowly varying function. However, when α∈(0,1), we show that the rate of convergence of the FLSE related to the short-term parameters is n and its limiting distribution not only depends on the stationary component itself but also depends on the unit root component. Based on the FLSE, we then study the limiting behavior of the reduced rank LSE (RLSE). The results related to the short-term parameters of both FLSE and RLSE are significantly different from those of heavy-tailed time series in the literature, and it may provide new insights in the area for future research. Simulation study is carried out to demonstrate the performance of both estimators. A real example with application to 3-month Treasury Bill rate, 1-year Treasury Bill rate and Federal Fund rate is given.

Renewable and fossil energy, terrorism, economic growth, and trade: Evidence from France

To the best of our knowledge, there is no econometric study on the causal relationship between renewable or fossil energies and terrorism, nor on terrorism in France. This paper tries to fill this gap and considers other variables as economic growth and trade openness. The autoregressive distributed lag approach and Granger causality are used for annual data on France covering the period 1980–2015. The Wald test confirms the existence of long-run relationships between the considered variables. Granger causality reveals long-run bidirectional causalities between all the variables. Interestingly, in the short-run, there are unidirectional causalities running from renewable energy to terrorism, and from terrorism to trade openness, and there is bidirectional causality between terrorism and economic growth. Long-run parameter estimates show that more renewable energy consumption and more trade increase both terrorism and economic growth. However, economic growth reduces terrorism in the long-run and appears as a powerful tool to combat terrorism in France. In addition, France is recommended to continue encouraging the use of renewable energies, and at the same time, to make more diplomatic efforts to resolve political and military conflicts in the world, and particularly in the Middle East.

Quasi-maximum likelihood estimation for cointegrated continuous-time linear state space models observed at low frequencies

In this paper, we investigate quasi-maximum likelihood (QML) estimation for the parameters of a cointegrated solution of a continuous-time linear state space model observed at discrete time points. The class of cointegrated solutions of continuous-time linear state space models is equivalent to the class of cointegrated continuous-time ARMA (MCARMA) processes. As a start, some pseudo-innovations are constructed to be able to define a QML-function. Moreover, the parameter vector is divided appropriately in long-run and short-run parameters using a representation for cointegrated solutions of continuous-time linear state space models as a sum of a Levy process plus a stationary solution of a linear state space model. Then, we establish the consistency of our estimator in three steps. First, we show the consistency for the QML estimator of the long-run parameters. In the next step, we calculate its consistency rate. Finally, we use these results to prove the consistency for the QML estimator of the short-run parameters. After all, we derive the limiting distributions of the estimators. The long-run parameters are asymptotically mixed normally distributed, whereas the short-run parameters are asymptotically normally distributed. The performance of the QML estimator is demonstrated by a simulation study.

Estimating the tourism induced environmental Kuznets curve in France

France has retained its position as the world’s favourite tourism destination. Accounting for around 83 million visitors and 4.2% of the total employment, the tourism sector in France contributes to nearly 9% of the total GDP. However, as reported by the World Tourism Organization, road and air travel related to tourism activities account for approximately 10% of the total CO2 emissions in the atmosphere, thereby contributing to severe air pollution. Thus, initiatives are necessary to prevent environmental damage that could have a corresponding detrimental effect on the tourism industry itself and slow down the country’s economic growth. This study utilises a vector error correction model (VECM) to investigate the relationship between polluting emissions, GDP levels, and tourism flows in France to test the validity of the environmental Kuznets curve (EKC) hypothesis for the period 1995–2014. Additionally, based on a joint analysis of long-run parameters and causality links, appropriate policy strategies are proposed to promote robust and sustainable long-run economic growth.

Factor substitution and labor market friction in the United States: 1948–2010

ABSTRACTWe re-estimate the capital-labor elasticity of substitution and the biased factor-augmenting technological progress using a system approach for the aggregate U.S. economy from 1948 to 2010. Due to (i) the significant impacts of labor market dynamics on economic growth and (ii) the fundamental tension between the short-run data that are available and the long-run parameter that is required in the estimation process, we incorporate labor market friction into a supply-side system to re-estimate these important growth parameters and to explore their sensitivity to the incorporation of labor market friction. Our estimation obtains a significantly smaller-than-unity elasticity of substitution. This result is consistent with labor input measured along the extensive and intensive margin, and in both competitive and imperfect labor markets. Technological progress tends to be purely labor-augmenting, with the average growth rate around 2% per year. These findings are robust to alternatively constructed data sets and different estimation strategies.

Inflation Expectations in Turkey: Determinants and Roles in Missing Inflation Targets

Abstract This paper aims at specifying the determinants of 12-month ahead and 24-month ahead inflation expectations in Turkey by using monthly data from April 2006 to December 2016. Put differently, this paper tries to shed light on how inflation expectations respond to changes in past inflation rate, inflation target, output gap, USD/TL exchange rate, oil price, and EMBI in Turkey. To this end, the paper first conducts unit root tests in order to detect the order of integration of the variables. Then, the paper employs the autoregressive distributed lag approach to examine whether there is a cointegration relationship among variables and to estimate long-run parameters. According to the findings, 12-month ahead expected inflation rate is positively related to past inflation rate, inflation target, output gap, USD/TL exchange rate, and oil price and is negatively related to EMBI. Besides, 24-month ahead expected inflation rate is positively related to past inflation rate and USD/TL exchange rate and is negatively related to inflation target and EMBI. Upon its findings, the paper makes some inferences about the success of inflation targeting strategy in Turkey.

Exchange rate nonlinearities in India’s exports to the USA

Exchange rate nonlinearities in India’s exports to the USA

Econometric testing of uncovered interest rate parity in Serbia

This paper provides econometric evidence of the interest parity puzzle in Serbia over the period 2005-2016. Econometric findings are derived from the following techniques: long-run parameter estimation based on the autoregressive distributed lag model, impulse response function computed from the bivariate vector autoregressive model, and estimation of the two-regime Markov switching parameter model. Our results indicate that a positive interest differential corrected for country risk leads to significant dinar appreciation against the euro. The intensity of this impact is different across sub-periods of low exchange rate variability and high variability. Exchange rate movements are found to appreciate more strongly during lower variability episodes. Preliminary econometric investigation of four other European emerging economies documents similar findings only for Romania. Our results suggest that there is a huge incentive for shortterm carry trades in Serbia, regardless of substantial risks.

SOME NEW ESTIMATES OF SHADOW ECONOMY FOR 80 COUNTRIES USING POOLED MEAN GROUP ESTIMATOR

The purpose of this study is to estimates the size of the shadow economy for 80 countries from nine regions spanning the period 1975-2012 based on Tanzi-type currency demand approach (CDA). This study contributes to the literature in three distinct ways. First, we augment CDA regression with a macroeconomic uncertainty index (MUI). Second, the construction of the uncertainty index is based on the dynamic factor model (DFM). Third, the pooled mean group (PMG) estimator allows in capturing the heterogeneity across countries in the short-run dynamics but imposing restrictions in the long-run parameters. The results confirm the existence of the longrun equilibrium relationship among the variables examined. All coefficients show expected signs along with statistical significance. More importantly, the macroeconomic uncertainty index variable show positive relationship, suggesting that public tend to hold more currency in an uncertain macroeconomic environment. In addition, we observe that developing regions (ranging from 19.9% to 37.3%) exhibit relatively large size of the shadow economy. On the contrary, developed regions have a considerable smaller estimate (ranging from 13.7% to 19.0%) of the size of shadow economy. On average, the world estimate of the shadow economy as a percentage of GDP is about 23.1%. Keywords: Shadow Economy; Currency Demand; Macroeconomic Uncertainty; Pooled Mean Group.

Urbanization and industrialization impact of CO2 emissions in China

This study investigates the causal linkage among CO2 emissions per capita, energy intensity, real GDP per capita, industrialization (share of industrial value added in GDP), urbanization (share of urban population in total population), and share of renewable energy consumption in China over the period from 1970 to 2015. We employ autoregressive distributed lag (ARDL) technology to test the co-integration and short- and long-run estimates, and apply the vector error correction model (VECM) to analyze the directional causality among the time series data. The estimates of long-run parameters indicate that 1% augments of energy intensity, real GDP, industrialization, and urbanization increase CO2 emissions by 1.1%, 0.6%, 0.3%, and 1.0%, respectively. Long-run feedback Granger causalities exist among emissions, real GDP, and industrialization. Thus, our main policy suggestions are as follows: (i) to encourage green and sustainable urbanization, as it increases economic growth but not at the expense of environmental degradation; (ii) to strategically adjust and optimize the industrial structure; (iii) to improve the efficiency of energy use and technological innovation; and (iv) to increase the proportion of renewable energy in total energy consumption.

Economic growth and CO2 emissions: an investigation with smooth transition autoregressive distributed lag models for the 1800-2014 period in the USA.

The study aims to combine the autoregressive distributed lag (ARDL) cointegration framework with smooth transition autoregressive (STAR)-type nonlinear econometric models for causal inference. Further, the proposed STAR distributed lag (STARDL) models offer new insights in terms of modeling nonlinearity in the long- and short-run relations between analyzed variables. The STARDL method allows modeling and testing nonlinearity in the short-run and long-run parameters or both in the short- and long-run relations. To this aim, the relation between CO2 emissions and economic growth rates in the USA is investigated for the 1800-2014 period, which is one of the largest data sets available. The proposed hybrid models are the logistic, exponential, and second-order logistic smooth transition autoregressive distributed lag (LSTARDL, ESTARDL, and LSTAR2DL) models combine the STAR framework with nonlinear ARDL-type cointegration to augment the linear ARDL approach with smooth transitional nonlinearity. The proposed models provide a new approach to the relevant econometrics and environmental economics literature. Our results indicated the presence of asymmetric long-run and short-run relations between the analyzed variables that are from the GDP towards CO2 emissions. By the use of newly proposed STARDL models, the results are in favor of important differences in terms of the response of CO2 emissions in regimes 1 and 2 for the estimated LSTAR2DL and LSTARDL models.

Dynamics of electricity consumption, oil price and economic growth: Global perspective

This study uses the data from 157 countries from 1960 to 2014 to analyze the relationship between economic growth, electricity consumption, oil prices, capital, and labor. The economic growth of developing countries with industrial infrastructure has a more significant association with electricity consumption than oil prices. We use oil prices and electricity consumption jointly to study highly predictive observations for economic growth. The data are categorized by income, OECD and regional levels. The panel cointegration, long-run parameter estimation, and Pool Mean Group tests are used to analyze the cointegration and short-run and long-run relationships between the variables. The empirical results indicate the presence of cointegration between the variables. The presence of feedback effects between electricity consumption and economic growth, oil prices and economic growth is valid. These findings confirm that in spite of the oil prices, developing countries rely heavily on electricity consumption for economic growth. In the short run, growth and feedback effects suggest that more vigorous electricity policies should be implemented to attain sustainable economic growth for the long-term.

A convenient method for the estimation of ARDL parameters and test statistics: USA trade balance and real effective exchange rate relation

This paper suggests an alternative yet effective way of obtaining long-run parameters of ARDL estimators as well as their test statistics. We contribute by providing an approach that is simple to implement, user-friendly and portable in which we can simultaneously investigate not only the long-run relationship but also the short-run dynamics. Additionally, our approach can be used without the need to buy new software, and it may be indispensable in the face of tighter expenditure budgets and the increasing cost of education for students. Finally, we affirm our approach by demonstrating through bivariate and multivariate examples using the trade balance of the United States.

Price modelling in the Canadian fish supply chain with forecasts and simulations of the producer price of fish

ABSTRACTThis research article reports the results of two time-series models defined on the producer price of fish in Canada. An ARMAX model is specified to provide a parsimonious characterization of producer prices and used to simulate price effects from exchange rate shocks. Results show the importance of exogenous shocks, outside the control of fisheries management on welfare/income of Canadian fishermen. An error-correction model is specified to link the price in the first-hand market to the price in the processing market. This model measures both the short- and long-run parameters in defining price, provides estimates on the length of time to regain the equilibrium from price shocks and to forecast producer price response. Interestingly, results show that process price can be considered weakly exogenous with respect to both the short and long run and is the price leader in determining producer price.

The role of renewable energy and agriculture in reducing CO2 emissions: Evidence for North Africa countries

This paper uses panel cointegration techniques and Granger causality tests to investigate the dynamic causal links between per capita renewable energy consumption, agricultural value added (AVA), carbon dioxide (CO2) emissions, and real gross domestic product (GDP) for a panel of five North Africa countries spanning the period 1980–2011. In the short-run, Granger causality tests show the existence of bidirectional causality between CO2 emissions and agriculture; a unidirectional causality running from agriculture to GDP, from GDP to renewable energy consumption, and from renewable energy consumption to agriculture. In the long-run, there is bidirectional causality between agriculture and CO2 emissions; a unidirectional causality running from renewable energy to agriculture and to emissions, and from output to agriculture and to emissions. Long-run parameter estimates show that an increase in GDP or in renewable energy consumption (including combustible and waste) increases CO2 emissions, whereas an increase in agricultural value added reduces CO2 emissions. As policy recommendation, North African authorities should encourage renewable energy consumption, and especially clean renewable energy such as solar or wind, as this improves agricultural production and help to combat global warming.

Wagner versus Keynes: the causal nexus between Government Expenditures and Economic Growth: An Empirical study of Burkina Faso

Abstract The spending patterns of governments in the world especially developing economies have changed significantly over the last several decades. The main objective of this paper is analysing the relationship between government expenditures and growth in Burkina Faso’s economy. The study focuses on testing the various versions of Wagner’s hypothesis using the Burkina Faso data between 1960-2015 by an Autoregressive-Distributed Lag (ARDL) model. Cointegration tests, the long-run parameters and causality tests found valid Keynesian and Wagnerian relationship, but results are sensitive to the variable definition; the use of relative and absolute measures, local and international currency leads to a different conclusion.

How does monetary policy and private sector credit interact in a developing economy?

This paper evaluates the nature of relationship between monetary policy and private sector credit as well as the impact of structural break on the relationship by applying a battery of econometric tests on Nigeria data. The cointegrating regression results reveal evidence of long-run relationship between monetary policy and credit to private sector. Estimates from the long-run parameter stability tests support cointegration in the presence of structural breaks. Similarly, error correction model results show that changes in credit have positive and significant short-term effects on changes in monetary policy. The results of innovation accounting also provide support to show that innovations in credit have impact on monetary policy. Granger causality analysis exhibits unidirectional causality from credit to monetary policy. The key implication of the findings is that credit to the private sector is an effective channel for monetary policy transmission in Nigeria. It is recommended therefore that monetary authorities in developing countries should consider credit as a major channel for implementing monetary policies.