60 research outputs found
Structural Modelling And Analysis Of The Behavioural Dynamics Of Foreign Exchange Rate [HG3851. Y51 2006 f rb].
Tesis ini berkaitan dengan Kadar Wang Pertukaran Asing (KWPA) yang dihasilkan oleh satu regime urusniaga bebas. Pada amnya, kita mengkaji Pemodelan Struktur dan Analisis Tingkahlaku Dinamik Kadar Pertukaran Wang Asing.
This thesis deals specifically with the foreign exchange rates that resulted from free float regimes. In general, we study the structural modelling and analysis of the behavioural dynamics of foreign exchange rates
The impacts of oil shocks on Malaysia's GDP growth
This paper suggests that instrumental variable regression is a good alternative to nonlinear specification model when
estimating the impacts of oil shocks on GDP growth in Malaysia
Structural Modelling And Analysis Of The Behavioural Dynamics Of Foreign Exchange Rate
Tesis ini berkaitan dengan Kadar Wang Pertukaran Asing (KWPA) yang dihasilkan oleh satu regime urusniaga bebas.
This thesis deals specifically with the foreign exchange rates that resulted from free float regimes
Modeling the asymmetric in conditional variance
The purpose of this study is to model the asymmetric in conditional variance of Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) with Combine White Noise (CWN) model to obtain suitable results. Combine white noise has the minimum
information criteria and high log likelihood when compare with EGARCH estimation.The determinant of the residual covariance matrixvalue indicates that CWN estimation is efficient. Combine white noise has minimum information criteria and high log likelihood value that signify suitable estimation. Combine white noise has a minimum forecast errors which indicates forecast accuracy.Combine white
noise estimation results have proved more efficient when compared with EGARCH model estimatio
Difference or not to difference an integrated time series? An empirical investigation
This paper uses the gross domestic product growth rates of Malaysia,
Thailand, Indonesia and China in an empirical examination to
determine whether an integrated time series should be differenced
before it is used for forecasting. The results reveal that Mallows model
combination (M.M.A.) of original and differenced series is a better
choice than just differencing the series only if the perturbation
instability measure is more than 1.25 for autoregressive (A.R.) model,
and 1.105 for moving average (M.A.) model and autoregressive
fractional integrated moving average (A.R.F.I.M.A.) model.
Furthermore, it is found that M.M.A. performs better in forecasting
with better model stability for the case of M.A. and A.R.F.I.M.A. than
A.R. However, M.M.A. is very sensitive in financial crisis
A new variant of ARFIMA1 Process and its predictive ability
ARFIMA models generated an enormous amount of interest in the literature about three decades ago.However, this interest vaned after Granger (1999) showed that an ARFIMA
process might have stochastic properties that do not mimic the properties of the data at all.The empirical results of our research in which we used exchange rate data for the analysis, show that a variant of an ARFIMA process indeed can beat the ARFIMA, the Random Walk and the ARMA process of the order one in out of sample forecasting.This indirectly indicates that our variant of the ARFIMA process
can be considered as the data generating process for the long memory time series
Achieving Community Happiness through Affordable Eco-Friendly Smart Houses
The significant lack of affordable house is contributed by urbanization, economic power, and population growth. Accessible housing views having different types of houses at variety of costs to suit the needs of different levels of household income. This paper proposes to resolve the unaffordable house issues by constructing eco-friendly smart houses. Eco-friendly smart houses play a vital role in developing affordable houses because it will minimize construction costs, minimize negative environmental impact and reduce energy consumption. Besides, it was suggested that those houses will lead to community happiness. This study identifies the determinants for buyer intends to purchase the houses and make recommendations on the impact of the houses towards community happiness expectation. Questionnaires have been distributed to the potential buyer using the online application. The results conclude that attitude, perceive behavioral control and social media influence potential buyer intention to purchase the affordable eco-friendly smart houses. The findings further reveal that purchase intention leads to expectation on community happiness. This study contributes in two ways. First, it contributes to sustainable development goals agenda and offers a new approach in constructing affordable houses. Second, it contributes to theoretical development on the linkage between purchase intention and expectation on community happiness
Modeling the heteroscedasticity in data distribution
The main objective of this study is to provide a model that will uplift the weaknesses of the existing model for efficient estimation. Generalized autoregressive conditional heteroscedasticity (GARCH) family models
weaknesses were overcome by the new Combine White Noise (CWN) model which proved to be more efficient.CWN estimation passed stability condition, stationary, serial correlation, the ARCH effect tests and it also passed the
Levene’s test of equal variances using both Australia (A.U.) and United States (U.S.) GDP data sets. The CWN estimation produced better results with minimum information criteria and high log likelihood values in both U.S. and A.U. data estimation.CWN has the minimum forecast errors which were better results when compare with the GARCH model dynamic evaluation forecast errors in both countries.The determinant of the residual of covariance matrix values revealed that CWN was efficient in the two countries, but A.U.was more efficient.Based on every result in the empirical analysis of the two countries, CWN was the more appropriate model
Evaluating combine white noise with US and UK GDP quarterly data
The main objective of this study is to evaluate the Combine White Noise (CWN) model for the confirmation of its effectiveness in addressing the error term challenges.CWN models the leverage effect appropriately with better estimation results of which the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model cannot handled.The determinant of the residual co variance matrix values indicates that CWN estimation is efficient for each country.CWN has a minimum forecast errors which indicates forecast accuracy by estimating the countries data individually.The overall results indicate that CWN estimation provide more efficient and better forecast accuracy than EGARCH estimation.This boosts the economy
Comparing vector autoregressive (VAR) estimation with combine white noise (CWN) estimation
The purpose of this study is to compare one of the existing models, which is VAR model with the new Combine White Noise model. The VAR models have not been able to model the conditional heteroscedasticity and the leverage effect exhibited by the data. Likewise, GARCH family models cannot model leverage effect. The Combine White Noise (CWN) has proved more efficient and takes care of these weaknesses. CWN has the minimum information criteria and high log likelihood when compare with VAR estimation. The determinant of the residual covariance matrix value indicates that CWN estimation is efficient. It passes the Levene’s test of equal variances. CWN has a minimum forecast errors which indicates forecast accuracy. All its outcomes outperform all the outcomes of VAR widely
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