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The research aims to analysis the impact of some economic policy variables on the value of agricultural resultant in Iraq for the period (2004-2020) using quarterly time series. The independent variables were used (foreign exchange window FC, narrow money supply M1, equilibrium exchange rate CE, interest rate imposed on agricultural loans R, value of agricultural imports M, while the value of agricultural output Y was adopted as the dependent variable. The researcher used the modern methodology to analyze the chains The temporal data from unit root test, Johansson co-integration test and using error correction vector model (VECM), shock measurement using IRF and DC variance components analysis with the introduction of all available standard tests to judge the quality of the model and its free from standard problems, the results indicated that all The variables had significant trace on the value of agricultural resultant, with the exception of the foreign currency sale window variable, where the results showed that it had no significant trace on the value of agricultural resultant because its impact was mention on the value of agricultural resultant, as well as the error correction value of the parameter C1 was negative, significant and smaller than The one, which amounted to (-0.10), mention that the speed is somewhat slow to the equilibrium state of adjusting the system. T is so normal in this model, that is because it excludes the general trend of the variables, and the focus here is on the value of F, which appeared significant 7.69, which mention the significance of the model as a whole. The researcher recommended the necessity of having a strategy to confront the development of the agricultural sector in Iraq by creating a revolution that focuses on the technical and technical aspects and works on the strategy of introducing modern means of production to the agricultural sector. The market mechanism, in addition to working to reduce agricultural imports, which was the largest proportion of dumping the Iraqi market with foreign products, as well research recommends increasing agricultural investments by reducing interest rates imposed on agricultural loans. [ABSTRACT FROM AUTHOR]

Stock is proof of ownership of a company where the buyer pays money to the company through the stock exchange. The company issues shares when the company decides to conduct corporate funding, which attracts investors to invest and make a profit. The company's business development greatly influences the stock price. If the company's management does not run well, it will affect stock price changes, so the stock price is stochastic. The Wiener process is one of the mathematical models that can be used to model stock prices. In addition to the wiener process, Monte Carlo simulations can also be used for random number generation in the calculation of changes in stock prices. The Monte Carlo simulation method assumes that the returns are normally distributed and requires repeated experiments through the generation of random numbers so that random values are obtained at a specific frequency probability. The study's results obtained the value of volatility or the magnitude of fluctuations in the stock price of PT. BRI starting from January 03, 2022, to January 27, 2023, of 2.267801175 and the comparison of historical stock prices with an average result of 30 simulations with Monte Carlo shows a good movement pattern by following the movement pattern of actual stock price data with a MAPE (Mean Absolute Percentage Error) value of 4.781%. [ABSTRACT FROM AUTHOR]

Research on the relationship between air quality, economic development, population growth, and energy consumption has been conducted in newly industrialized countries (NICs). It is necessary to analyze further the role of this variable in ASEAN countries in reducing environmental degradation. This study aims to analyze the interaction patterns of the causal relationship between air quality, economic growth, urbanization, and international commitments. The study was conducted in 5 central ASEAN countries from 1990 to 2014. Data were analyzed using the Panel Vector Error Correction Model (Panel VECM). The findings show a long-run relationship between urbanization and air quality variables. The bidirectional panel causality exists on urbanization and a unidirectional causality panel on air quality to economic growth. Mitigation strategies are needed to overcome the degradation in air quality. [ABSTRACT FROM AUTHOR]

Considering the increasing trend of greenhouse gas emission globally in the last few decades which contributes to environmental degradation and global warming, this study intends to measure and analyze the short- and long-run effects of road infrastructure, energy consumption, and economic growth on CO2 emission in Indonesia. This study focuses on Indonesia, considering the country as one of developing economies that ranked as the sixth-largest contributor to global CO2 emissions. Utilizing annual secondary data and analyzing it using an Autoregressive Distributed Lag (ARDL) model, this study documented the short-run effect of energy consumption on an increase in CO2 emissions. Meanwhile, in the long run, the increase in CO2 emissions in the country was attributed to the rise in road infrastructure, energy consumption, and economic growth. The economic growth-emissions relationship is relevant to the EKC hypothesis. These findings suggest an urgent need to introduce an energy-efficient transportation system, searching for new sources of renewable energy, implement green economic development measures, and increase public awareness on the importance of preserving the environment as a strategic effort to reduce CO2 emissions in Indonesia. [ABSTRACT FROM AUTHOR]

Economic growth is significant for a country to raise its standard of living. It is found that economic growth can be contributed by several factors such as capital, labour and total factor productivity (TFP). Therefore, this study aims to analyse the contribution of education and labour skills to economic growth in Malaysia. For this purpose, the analysis of the study is to use the Autoregressive Distributed Lag (ARDL) approach. The focus of the study is in Malaysia based on data for the period 1982‒2017 obtained from the Department of Statistics Malaysia and the International Labour Organisation. The results show that only capital and labour variables with tertiary education can contribute to economic growth in Malaysia in the long run. Nevertheless, the findings of the study in the short-term show that the variables of capital, labour with tertiary education, secondary education and the ratio of skilled labour can contribute to economic growth. Therefore, the country needs to allocate greater expenditure to the education and training sector as education and labour skills have a positive impact. [ABSTRACT FROM AUTHOR]

Empirical analysis is conducted to examine the nexus of energy consumption, energy intensity and carbon dioxide (CO2) emissions using the global panel dataset of 1990 to 2014. The main objectives are to compare the nexus between oil-importing versus oil-exporting countries, to examine the nexus between short versus long-run counterparts, and to reveal the asymmetric effects of oil price changes in the nexus examined. The main contribution of this study is to analyze the asymmetric effects of oil price changes using a nonlinear autoregressive distributed lags (NARDL) model. A few variables are included, which are energy consumption, energy intensity, total and sectoral CO2 emissions, and world crude oil price, in which the oil price acts as a control variable. The results show that CO2 emission affects energy consumption positively for both groups of countries, with a higher impact in oil-importing countries. Besides, a rise in CO2 emissions shrinks the energy intensity of oil-exporting countries but raises the energy intensity of oil-importing countries. The oil price has asymmetric effects on energy consumption, with larger effect from oil price increases. Oil price has a limited influence on energy intensity, while the higher oil price causes higher emissions. The oil-dependency factor matters, with oil-importing countries experiencing larger impacts from oil price changes. It is suggested that policy decision/ planning should co-exist with the environmental concern, to sustain the environmental quality and economic growth. [ABSTRACT FROM AUTHOR]

A strong correlation between economic growth, energy consumption and carbon dioxide (CO2) emissions has made the increasing level of CO2 emissions becomes a perpetual problem worldwide. Therefore, the efficacy of current energy and environment related policies needs to be evaluated. In this regard, finding a reliable model to accurately forecast CO2 emissions is of importance, particularly for a developing country like Indonesia. By involving an unbalanced panel data of 77 countries covering the period of 1966 to 2019, this paper proposes a model which relies on the machine learning method to forecast CO2 emissions in Indonesia and to predict the feasibility for decoupling CO2 emissions from economic growth. This study finds the beneficial impacts of new and renewable energy on reducing CO2 emissions. However, the peak of CO2 emissions in Indonesia was not predicted. Hence, decarbonization of Indonesia's economy is not likely to be achieved in the near future. [ABSTRACT FROM AUTHOR]

Daily data of companies based on the January 1, 1991 to December 31period Bombay Stock Exchange (BSE) -200 index 2012 was analysed using various methodologies. In this paper we discuss aboutRandom Walk, Unit Root Test, Granger Causality, Vector Error Correction Model, Johansen Cointegration, and ARIMA. If the Indian stock markets are efficient, the need for government intervention is minimal. On the other hand, the inefficient stock market offers opportunities for profitable transactions. Unit root model two endogenous structural breaks errors presence Granger cause is technique for whether determining another method is effective of predicting. Normally,reflect setbacks "just" relationships. If variable A in the presence of lagged about another lagged information on a variable B provides any statistically significant information The VECM tries to correct the drawbacks of the Engle-Granger approach. Industrial Production Schedule. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate weighted time-varying causal graphs based on daily log-return and realized volatility time series of the S&P500 stocks. In order to construct causal graphs for each quarter between Q1 2005 and Q2 2020, we compute cross-correlations between series and conduct pairwise Granger Causality tests for lead-lag relationships. We take individual stocks as nodes and significant causal relationships as weighted edges. Our analysis is based on a comparison of network topology and properties for different periods, including the 2007-2010 subprime crisis, the European sovereign debt crisis, and the coronavirus pandemic, as well as tranquil periods. In addition to edge weights, our study also takes into account the estimated lag of the causal interactions and uses network level measures to identify meaningful patterns over time. We also conduct an analysis of the industrial sectors interconnectedness via smaller graphs derived from the stock-level causality graphs. The results of the work reveal that network topology changes during periods of market stress and crisis and this can be captured by using convenient network construction methods and measures. Our results also show that the patterns of network topology transformation and magnitude of spillovers transmission during the coronavirus crisis and accompanying events differs from previous crisis periods. [ABSTRACT FROM AUTHOR]

The most important economic controversies among monetary authorities, policy makers and economists is "The relationship between inflation and economic growth". The empirical and theoretical evidences provide three types of relationship between inflation and economic growth - negative, positive and none. Although the relationship between inflation and economic growth has been widely examined and investigated over the years, still this is a burning issue which hinders the economic growth of a country. In this paper, it is proposed to investigate the relationship between Economic growth and Inflation in India during the period 1980-2016 using various cointegration techniques and correlation. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between energy consumption, environmental degradation and economic growth for high and middle-income countries using spatial panel and non-spatial panel regression based on yearly data from 2002-2013 (N=14, T=12). The empirical results suggest that spatial panel models are more appropriate than the non-spatial panel model for high-income countries data. Energy consumption provides a significant impact on economic growth and environmental degradation in middle-income countries. The results are consistent with most of the previous studies and reveal useful information about the spatial effect in the estimated models. This provides a better understanding of the inter-relationship among the variables in different groups of countries to attain sustainable development. [ABSTRACT FROM AUTHOR]

This paper proposes a fractional filter and Auto Regressive Fractional Unit Root Integral Moving Average (ARFURIMA) model on daily Malaysian Tapis Crude Oil Price (MTCOP) for the period 4th June 2007 to 29th June 2018. The goodness of fits and dependence tests of each model are discussed. Results indicate that ARFURIMA model is superior to the Auto Regressive Integral Moving Average (ARIMA) and Auto Regressive Fractional Integral Moving Average (ARFIMA) models in modelling and forecasting the Tapis Crude Oil Price. [ABSTRACT FROM AUTHOR]

This research article explores some misspecification tests for stochastic linear regression model viz. Durbin-Watson test, Ramsey's regression specification error test, Lagrange's multiplier test, and UTTS’ rainbow test. Any type of error occurred in the set of underlying assumptions of a stochastic linear regression model and the associated inferences lead to 'specification errors'. These errors will present particularly in specifying the error vector ε and the data matrix X. Generally specification errors are caused by the inclusion of irrelevant independent variables or exclusion of relevant independent variables in the stochastic linear regression model. Ivan Krivy et.al, in 2000 in their research article depicted two stochastic algorithms which are useful in estimating the parameters of nonlinear regression models. Russell Davidson et al. in 1984 (see [2]), in their paper, developed a simple computational procedure for performing a wide variety of model specification tests. In1993, LudgerRuschendorf et.al in their research article constructed as nonlinear regression representations of general stochastic processes so that they got a particular special regression representations on Markov chains and of certain m-dependent sequences. [ABSTRACT FROM AUTHOR]

Capital markets can be an indicator of the development of a country's economy. The presence of capital markets also encourages investors to trade; therefore investors need information and knowledge of which shares are better. One way of making decisions for short-term investments is the need for modeling to forecast stock prices in the period to come. Issue of stock market-stock integration ASEAN is very important. The problem is that ASEAN does not have much time to implement one market in the economy, so it would be very interesting if there is evidence whether the capital market in the ASEAN region, especially the countries of Indonesia, Malaysia, Philippines, Singapore and Thailand deserve to be integrated or still segmented. Furthermore, it should also be known and proven What kind of integration is happening: what A capital market affects only the market Other capital, or a capital market only Influenced by other capital markets, or a Capital market as well as affecting as well Influenced by other capital markets in one ASEAN region. In this study, it will compare forecasting of Indonesian share price (IHSG) with neighboring countries (ASEAN) including developed and developing countries such as Malaysia (KLSE), Singapore (SGE), Thailand (SETI), Philippines (PSE) to find out which stock country the most superior and influential. These countries are the founders of ASEAN and share price index owners who have close relations with Indonesia in terms of trade, especially exports and imports. Stock price modeling in this research is using multivariate time series analysis that is VAR (Vector Autoregressive) and VECM (Vector Error Correction Modeling). VAR and VECM models not only predict more than one variable but also can see the interrelations between variables with each other. If the assumption of white noise is not met in the VAR modeling, then the cause can be assumed that there is an outlier. With this modeling will be able to know the pattern of relationship or linkage of share prices of each country in ASEAN. The best modeling comparison result of the ASEAN stock price index is VAR. [ABSTRACT FROM AUTHOR]

A country has several important parameters to achieve economic prosperity, such as tax revenue and inflation rate. One of the largest revenues of the State Budget in Indonesia comes from the tax sector. Meanwhile, the rate of inflation occurring in a country can be used as an indicator, to measure the good and bad economic problems faced by the country. Given the importance of tax revenue and inflation rate control in achieving economic prosperity, it is necessary to analyze the structure of tax revenue relations and inflation rate. This study aims to produce the best VECM (Vector Error Correction Model) with optimal lag using various alpha and perform structural analysis using the Impulse Response Function (IRF) of the VECM models to examine the relationship of tax revenue, and inflation in Banda Aceh. The results showed that the best model for the data of tax revenue and inflation rate in Banda Aceh City using alpha 0.01 is VECM with optimal lag 2, while the best model for data of tax revenue and inflation rate in Banda Aceh City using alpha 0.05 and 0,1 VECM with optimal lag 3. However, the VECM model with alpha 0.01 yielded four significant models of income tax model, inflation rate of Banda Aceh, inflation rate of health and inflation rate of education in Banda Aceh. While the VECM model with alpha 0.05 and 0.1 yielded one significant model that is income tax model. Based on the VECM models, then there are two structural analysis IRF which is formed to look at the relationship of tax revenue, and inflation in Banda Aceh, the IRF with VECM (2) and IRF with VECM (3). [ABSTRACT FROM AUTHOR]

Local model order reduction methods provide better results than global ones to problems with intricate manifold solution structure. A posteriori methods (e.g. Proper Orthogonal Decomposition) have been many times applied locally, but a priori methods (e.g. Proper Generalized Decomposition) have the difficulty of determining the manifold structure of the solution in a previous way. We propose three strategies for estimating the appropriate size of the local sub-domains where afterwards local PGD (ℓ-PGD) is applied. It can be seen as a sort of a priori manifold learning or non-linear dimensionality reduction technique. Finally, three examples support the work. [ABSTRACT FROM AUTHOR]

The objectives of this paper is two-fold: First, to empirically investigate the effects of an enlarged number of healthy and well-educated people on economic growth in Malaysia within the Endogeneous Growth Model framework. Second, to examine the causal links between education, health and economic growth using annual time series data from 1981 to 2014 for Malaysia. Data series were checked for the time series properties by using ADF and KPSS tests. Long run co-integration relationship was investigated with the help of vector autoregressive (VAR) method. For short and long run dynamic relationship investigation vector error correction model (VECM) was applied. Causality analysis was performed through Engle-Granger technique. The study results showed long run co-integration relation and positively significant effects of education and health on economic growth in Malaysia. The reported results also confirmed a feedback hypothesis between the variables in the case of Malaysia. The study results have policy relevance of the importance of human capital (health and education) to the growth process of the Malaysia. Thus, it is suggested that policy makers focus on education and health sectors for sustainable economic growth in Malaysia. [ABSTRACT FROM AUTHOR]

In this paper, vector error correction model (VECM) has been utilized to model the dynamic relationships between world crude oil price and the sector indices of Malaysia. The sector indices have been collected are covering the period Jan 1998 to Dec 2013. Surprisingly, our investigations show that oil price changes do not Granger-cause any of the sectors in all of Malaysia. However, sector indices of Food Producer and Utilities are found to be the cause of the changes in world crude oil prices. Furthermore, from the results of variance decomposition, very high percentage of shocks is explained by world crude oil price itself over the 12 months and small impact from other sector indices. [ABSTRACT FROM AUTHOR]

Multivariate Markov switching models able to provide useful information on the study of structural change data since the regime switching model can analyze the time varying data and capture the mean and variance in the series of dependence structure. This paper will investigates the oil price and gold price effects on Malaysia, Singapore, Thailand and Indonesia stock market returns. Two forms of Multivariate Markov switching models are used namely the mean adjusted heteroskedasticity Markov Switching Vector Error Correction Model (MSMH-VECM) and the mean adjusted heteroskedasticity Markov Switching Vector Error Correction Model with exogenous variable (MSMH-VECMX). The reason for using these two models are to capture the transition probabilities of the data since real financial time series data always exhibit nonlinear properties such as regime switching, cointegrating relations, jumps or breaks passing the time. A comparison between these two models indicates that MSMH-VECM model able to fit the time series data better than the MSMH-VECMX model. In addition, it was found that oil price and gold price affected the stock market changes in the four selected countries. [ABSTRACT FROM AUTHOR]

Knowledge on linkages between stock prices and macroeconomic variables are essential in the formulation of effective monetary policy. This study investigates the relationship between stock prices in Malaysia (KLCI) with four selected macroeconomic variables, namely industrial production index (IPI), quasi money supply (MS2), real exchange rate (REXR) and 3-month Treasury bill (TRB). The variables used in this study are monthly data from 1996 to 2012. Vector error correction (VEC) model and Kalman filter (KF) technique are utilized to assess the impact of macroeconomic variables on the stock prices. The results from the cointegration test revealed that the stock prices and macroeconomic variables are cointegrated. Different from the constant estimate from the static VEC model, the KF estimates noticeably exhibit time-varying attributes over the entire sample period. The varying estimates of the impact coefficients should be better reflect the changing economic environment. Surprisingly, IPI is negatively related to the KLCI with the estimates of the impact slowly increase and become positive in recent years. TRB is found to be generally negatively related to the KLCI with the impact fluctuating along the constant estimate of the VEC model. The KF estimates for REXR and MS2 show a mixture of positive and negative impact on the KLCI. The coefficients of error correction term (ECT) are negative in majority of the sample period, signifying the stock prices responded to stabilize any short term deviation in the economic system. The findings from the KF model indicate that any implication that is based on the usual static model may lead to authorities implementing less appropriate policies. [ABSTRACT FROM AUTHOR]

Gold is a popular precious metal where the demand is driven not only for practical use but also as a popular investments commodity. While stock market represents a country growth, thus gold price effect on stock market behavior as interest in the study. Markov Switching Vector Error Correction Models are applied to analysis the relationship between gold price and stock market changes since real financial data always exhibit regime switching, jumps or missing data through time. Besides, there are numerous specifications of Markov Switching Vector Error Correction Models and this paper will compare the intercept adjusted Markov Switching Vector Error Correction Model and intercept adjusted heteroskedasticity Markov Switching Vector Error Correction Model to determine the best model representation in capturing the transition of the time series. Results have shown that gold price has a positive relationship with Malaysia, Thailand and Indonesia stock market and a two regime intercept adjusted heteroskedasticity Markov Switching Vector Error Correction Model is able to provide the more significance and reliable result compare to intercept adjusted Markov Switching Vector Error Correction Models. [ABSTRACT FROM AUTHOR]