Energy consumption, CO2 emissions and the economic growth nexus in Bangladesh: cointegration and dynamic causality analysis

The paper investigates the existence of dynamic causality between the energy consumption, environmental pollutions and economic growth using cointegration analysis for Bangladesh. First, we tested whether any long run relationship exist using Johansen bi-variate cointegration model which is complemented with auto-regressive distributed lag model introduced by Pesaron for the results robustness. Then, we tested for the short run and the long causality relationship by estimating bi-variate vector error correction modeling framework. The estimation results indicate that a unidirectional causality run from energy consumption to economic growth both in the short and the long run; a bi-directional causality from electricity consumption to economic growth in long run but no causal relationship exists in the short run. A uni-directional causality run from CO2 emissions to energy consumption in the long run but it is opposite in the short run. CO2 granger cause to economic growth both in the short and in the long run, which is conflicting to the familiar environmental Kuznets curve hypothesis. Our results are different from existing analysis for electricity consumption and economic growth, however. The result of dynamic linkage between energy consumption and economic growth significantly reject the ‘neo-classical’ assumption that energy use is neutral to economic growth. Hence clearly an important policy implication, energy can be considered as a limiting factor to the economic growth in Bangladesh and conservation of energy may harm economic spurs. Therefore, it is a challenge for the policy makers to formulate sustainable energy consumption policy to support smooth energy supply for sustainable economic growth

Nonlinear Cointegration and Nonlinear Error Correction: Record Counting Cointegration Tests

In this article we propose a record counting cointegration (RCC) test that is robust to nonlinearities and certain types of structural breaks. The RCC test is based on the synchronicity property of the jumps (new records) of cointegrated series, counting the number of jumps that simultaneously occur in both series. We obtain the rate of convergence of the RCC statistics under the null and alternative hypothesis. Since the asymptotic distribution of RCC under the null hypothesis of a unit root depends on the short-run dependence of the cointegrated series, we propose a small sample correction and show by Monte Carlo simulation techniques their excellent small sample behaviour. Finally, we apply our new cointegration test statistic to several financial and macroeconomic time series that have certain structural breaks and nonlinearities.Publicad

An Automated Approach Towards Sparse Single-Equation Cointegration Modelling

In this paper we propose the Single-equation Penalized Error Correction Selector (SPECS) as an automated estimation procedure for dynamic single-equation models with a large number of potentially (co)integrated variables. By extending the classical single-equation error correction model, SPECS enables the researcher to model large cointegrated datasets without necessitating any form of pre-testing for the order of integration or cointegrating rank. Under an asymptotic regime in which both the number of parameters and time series observations jointly diverge to infinity, we show that SPECS is able to consistently estimate an appropriate linear combination of the cointegrating vectors that may occur in the underlying DGP. In addition, SPECS is shown to enable the correct recovery of sparsity patterns in the parameter space and to posses the same limiting distribution as the OLS oracle procedure. A simulation study shows strong selective capabilities, as well as superior predictive performance in the context of nowcasting compared to high-dimensional models that ignore cointegration. An empirical application to nowcasting Dutch unemployment rates using Google Trends confirms the strong practical performance of our procedure

Electronic trading systems and intraday non-linear dynamics : an examination of the FTSE 100 cash and futures returns

This paper focuses on dynamic interactions of equity prices among theoretically related assets. We explore the existence of intraday non-linearities in the FTSE 100 cash and futures indices. We test whether the introduction of the electronic trading systems in the London Stock Exchange in 1997 and in the London International Financial Futures and Options Exchange (LIFFE) in 1999 has eliminated the non-linear dynamic relationship in the FTSE 100 markets. We show that the introduction of the electronic trading systems in the FTSE 100 markets has increased the efficiency of the markets by enhancing the price discovery process, namely by facilitating the increase of the speed of adjustment of the futures and cash prices to departures of the mispricing error from its non-arbitrage band. Nevertheless, we conclude that the automation of the markets has not completely eliminated the non-linear properties of the FTSE 100 cash and futures return series. JEL Classification: G12, G14, G1

Asymmetric and non linear adjustment in the revenue expenditure models

The purpose of this paper is to empirically analyse the revenue-expenditure models of public finance by considering the possibility of non-linear and asymmetric adjustment. A long-run relationship between general government expenditure and revenues is identified for Italy. Following system-wide shocks, the estimated relationship adjusts slowly to equilibrium, mainly due to complex administrative procedures that add to the sluggishness of tax collection and undermine the effective monitoring of public spending. Exogeneity of public expenditure implies that taxes rather than spending, carry the burden of short-run adjustment to correct budgetary disequilibria. Allowing for non-linear adjustment and the possibility of multiple equilibria, our findings show evidence of asymmetric adjustment around a unique equilibrium. In particular, we find that when government expenditure is too high, adjustment of taxes takes places at a faster rate than when it is too low. Further, there is evidence of a faster adjustment when deviations from the equilibrium level get larger, pointing to a Leviathan-style, revenue-maximiser government

Non-linear error correction, asymmetric adjustment and cointegration

This article links the intertemporal choice model with the non-linear error correction (NEC) model. It has three main components. First, it outlines a model of non-linear error correction, in which the linear error correction term ?Xt (the vector time series Xt is cointegrated, is the cointegrating vector) is replaced by the non-linear term g(?Xt), where g(.) is a non-linear function. Second, several types of asymmetries and the existence of multiple equilibria are discussed. The implications for the NEC model of trending targets are also explained. Third, it is shown that non-linear error correction is present in a trivariate series of UK employment, wage and capital stock.Publicad

Recent Developments in Cointegration

It is well known that inference on the cointegrating relations in a vector autoregression (CVAR) is difficult in the presence of a near unit root. The test for a given cointegration vector can have rejection probabilities under the null, which vary from the nominal size to more than 90%. This paper formulates a CVAR model allowing for multiple near unit roots and analyses the asymptotic properties of the Gaussian maximum likelihood estimator. Then two critical value adjustments suggested by McCloskey (2017) for the test on the cointegrating relations are implemented for the model with a single near unit root, and it is found by simulation that they eliminate the serious size distortions, with a reasonable power for moderate values of the near unit root parameter. The findings are illustrated with an analysis of a number of different bivariate DGPs

Information-Theoretic Analysis of Serial Dependence and Cointegration

This paper is devoted to presenting wider characterizations of memory and cointegration in time series, in terms of information-theoretic statistics such as the entropy and the mutual information between pairs of variables. We suggest a nonparametric and nonlinear methodology for data analysis and for testing the hypotheses of long memory and the existence of a cointegrating relationship in a nonlinear context. This new framework represents a natural extension of the linear-memory concepts based on correlations. Finally, we show that our testing devices seem promising for exploratory analysis with nonlinearly cointegrated time series.Publicad