scholarly journals The Impact of Financial Development on Carbon Emission: Evidence from China

2020 ◽  
Vol 12 (17) ◽  
pp. 6959 ◽  
Author(s):  
Mingyuan Guo ◽  
Yanfang Hu
Keyword(s):  
Financial Development ◽  
Carbon Emissions ◽  
Term Effect ◽  
Short Term ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Autoregressive Distributed Lag ◽  
Lag Model ◽  
The Impact

This paper studies the impact of financial development on carbon emissions in China from 1997 to 2016. First, this paper uses the entropy method to construct a synthetical index to measure the financial development. Meanwhile, a two-dimensional panel framework is introduced to group provinces in the panel analysis. The estimation results of the time series autoregressive distributed lag model show that for China as a whole, there is a weak carbon emissions reduction effect of financial development, whether it is a long-term effect or a short-term effect. The estimation results of the panel autoregressive distributed lag model also support that an increase in financial development suppresses carbon emissions. Although financial development inhibits carbon emissions both in the short run and in the long run, the absolute value of the long-term coefficient of financial development is significantly greater than that of the short-term coefficient.

scholarly journals FINANCIAL MARKET DEVELOPMENT AND CO2 EMISSIONS NEXUS IN NIGERIA: AN APPLICATION OF ARDL APPROACH

2021 ◽  
Vol 6 (1) ◽  
pp. 91
Author(s):  
Kabiru Saidu Musa ◽  
Sulaiman Chindo ◽  
Rabiu Maijama'a
Keyword(s):  
Co2 Emissions ◽  
Financial Development ◽  
Financial Market ◽  
Structural Breaks ◽  
Income Level ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Autoregressive Distributed Lag ◽  
Lag Model ◽  
The Impact

The paper investigated the impact of financial development on CO2 emissions in Nigeria from 1981 to 2019. In the process of investigating the impact, Augmented Dickey-Fuller and Philip Perron, as well as the Zivot-Andrew structural breaks, unit root tests were applied. Their results indicated that financial development, level of income, and CO2 emissions were stationary at the first difference and that of Zivot-Andrew structural breaks indicated a mixture of integration. Cointegration relationship among the variables was established through autoregressive distributed lag model bounds test. The autoregressive distributed lag model long-and-short run models results indicated that financial development and income level significantly negatively impact the CO2 emissions. The suggestion based on these results is that financial development and income level help in financing clean projects in the long-and-short runs. The Granger causality result revealed bidirectional causality from financial development to CO2 emissions, income level to CO2 emissions, and financial development to income level. The variance decomposition analysis indicates that financial development and income level have contributed less to CO2 emissions, and impulse response function results revealed that CO2 emissions respond negatively to shocks in financial development and income level. Therefore, we recommend expanding the Nigerian financial market in financing clean projects for a clean environment alongside checking income generation activities that bring about emissions of CO2, such as burning trees for charcoal production in the forest, among others.Keywords: Financial market development, CO2 emissions, ARDL approachJEL Classification: G20, Q53, C32

COINTEGRATED-BASED FORECAST OF LONG-RUN RELATIONSHIPS

2021 ◽  
Vol 14(63) (1) ◽  
pp. 153-168
Author(s):  
Klara-Dalma Deszke ◽  
Liliana Duguleana
Keyword(s):  
Vector Error Correction ◽  
Final Consumption ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Autoregressive Distributed Lag ◽  
Vector Error ◽  
Long Run ◽  
Lag Model ◽  
Relationship Of

The Vector Error Correction Model (VECM) and the Autoregressive Distributed Lag Model (ARDL) are used to estimate the cointegration in the case of long-run relationship of quarterly GDP and Final Consumption in Romania during the period 1995 – 2019. The actual data of 2020 Q1 and Q2 were used to check the best model’s validity. The static and dynamic approaches of the ARDL model were used to forecast the Final Consumption for Q3 and Q4 of the year 2020. Applying the cointegration model shows the long term relationship of GDP and Final Consumption, but also the effects of other factors, seen in the differences of Final Consumption from its Long-Run evolution, and comprised in the cointegrating terms.

scholarly journals ANALISIS DETERMINANT PROFITABILITAS PADA INDUSTRI PERBANKAN SYARIAH PERIODE 2011-2018 PENDEKATAN AUTO REGRESIVE DISTRIBUTED LAG (ARDL)

2020 ◽  
Vol 7 (6) ◽  
pp. 1102
Author(s):  
Gita Martha Permatasari ◽  
Dian Filianti
Keyword(s):  
Sampling Method ◽  
Banking Industry ◽  
Secondary Data ◽  
Short Term ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Autoregressive Distributed Lag ◽  
Long Run ◽  
Analysis Technique ◽  
Lag Model

This study aims to determine the influence of the Macro Economy, namely GDP and Inflation and Bank Characteristics, namely CAR, FDR, NPF, BOPO and Size on the Profitability of the Sharia Banking Industry in Indonesia in the 2011 - 2018 Period. The data used are secondary data, namely quarterly data obtained from the official website of Bank Indonesia (www.bi.go.id), Badan Pusat Statistik (www.bps.go.id), and Statistik Perbankan Syariah reports published by OJK (www.ojk.go.id). The sampling method used was purposive sampling method. The analysis technique uses the ARDL (Autoregressive Distributed Lag) model with statistical tools EViews 9. The results of the study show that in the short term of the GDP, Inflation, BOPO, Size variables, they have a significant effect on the profitability of the Sharia Banking Industry and the CAR, FDR, NPF variables have no significant effect on profitability of Islamic Banking Industry. Meanwhile in the long run of the GDP, BOPO, Size variables, they have a significant effect on the profitability of the Sharia Banking Industry and the Inflation, CAR, FDR, NPF variable does not significantly influence the profitability of the Sharia Banking Industry.Keywords: Profitability, Sharia Bank, ARDL

scholarly journals On the Predictability of China Macro Indicator with Carbon Emissions Trading

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1271
Author(s):  
Qian Chen ◽  
Xiang Gao ◽  
Shan Xie ◽  
Li Sun ◽  
Shuairu Tian ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Underlying Mechanism ◽  
Mixed Data ◽  
Data Sampling ◽  
Carbon Emissions Trading ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Autoregressive Distributed Lag ◽  
Mixed Data Sampling ◽  
Lag Model

Accurate and timely macro forecasting requires new and powerful predictors. Carbon emissions data with high trading frequency and short releasing lag could play such a role under the framework of mixed data sampling regression techniques. This paper explores the China case in this regard. We find that our multiple autoregressive distributed lag model with mixed data sampling method setup outperforms either the auto-regressive or autoregressive distributed lag benchmark in both in-sample and out-of-sample nowcasting for not only the monthly changes of the purchasing managers’ index in China but also the Chinese quarterly GDP growth. Moreover, it is demonstrated that such capability operates better in nowcasting than h-step ahead forecasting, and remains prominent even after we account for commonly-used macroeconomic predictive factors. The underlying mechanism lies in the critical connection between the demand for carbon emission in excess of the expected quota and the production expansion decision of manufacturers.

Modelling labour demand in Poland

2020 ◽  
Vol 65 (11) ◽  
pp. 7-23
Author(s):  
Kamila Radlińska ◽  
Krzysztof Jaros ◽  
Agnieszka Jakubowska ◽  
Anna Rosa
Keyword(s):  
Value Added ◽  
Labour Demand ◽  
Explanatory Variables ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Autoregressive Distributed Lag ◽  
Gross Value Added ◽  
Lag Model ◽  
Production Approach

The aim of the paper is to construct a long-term model of labour demand in Poland, in which the explanatory variables are the average gross salary and gross value added. Additionally, the authors attempt to detect labour hoarding. The study adopted the production approach, which used autoregressive distributed lag model with an ARDL-ECM error correction mechanism. The model parametres were estimated on the basis of quarterly data on the average number of persons employed, the average monthly gross salary and gross value added, all of which related to the period from the first quarter of 2002 to the fourth quarter of 2018. The data used in the study came from Statistics Poland publications. The proposed approach estimated the actual demand for labour. In the analysed period, a long-term relationship between the average employment, the average monthly gross salary and gross value added was observed. Employment was decreasing as the average salary was growing, and its increase was connected with the production growth. Moreover, short-term deviations of the value of the actual employment from the value of employment estimated by the model were observed on the labour market, which indicates labour hoarding could have been taking place. However, due to an insufficient number of observations, the occurrence of this phenomenon could not be fully confirmed.

scholarly journals Estimating Supply Response of Some Strategic Crops in Egypt Using ARDL Model

2019 ◽  
pp. 1-22
Author(s):  
Moataz Eliw ◽  
Abbas Mottawea ◽  
Ahmed El-Shafei
Keyword(s):  
Supply Response ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Autoregressive Distributed Lag ◽  
Dependent Variables ◽  
Farm Price ◽  
Short Run ◽  
Lag Model ◽  
Net Revenue

This paper estimated the areas of main cereal crops in Egypt (Wheat, Maize, Rice) supply response of farm price, area harvested and net revenue by using Autoregressive Distributed Lag Model (ARDL) methodology to define the integral relationship between the dependent variable and independent variables, both in the long and short-run, in addition to determining the magnitude of the impacts of all dependent variables on the dependent variable, Main findings indicate that farmgate price has a statistically significant impact on wheat, maize and rice cultivated areas. Impact of yield on wheat cultivated area proved insignificant, while proved statistically significant on maize and rice cultivated areas. Impact of net revenue on wheat and maize cultivated areas were significant but was insignificant in case of rice. Applying ARDL bounds test revealed a long-term relationship between all variables in the model for wheat, but not for maize and rice, the study used the data during the period (2000-2017).

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