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In this paper, we develop new threshold cointegration tests with SETAR and MTAR adjustment allowing for the presence of structural breaks in the equilibrium equation. We propose a simple procedure to simultaneously estimate the previously unknown breakpoint and test the null hypothesis of no cointegration. Thereby, we extend the well-known residual-based cointegration test with regime shift introduced by (Gregory, A. W., and B. E. Hansen. 1996a. “Residual-based Tests for Cointegration in Models with Regime Shifts.” Journal of Econometrics 70: 99–126) to include forms of nonlinear adjustment. We derive the asymptotic distribution of the test statistics and demonstrate the finite-sample performance of the tests in a series of Monte Carlo experiments. We find a substantial decrease of power of the conventional threshold cointegration tests caused by a shift in the slope coefficient of the equilibrium equation. The proposed tests perform superior in these situations. An application to the “rockets and feathers” hypothesis of price adjustment in the US gasoline market provides empirical support for this methodology.

Strategy is an important element in enhancing management competitiveness which directly influences firm performance. Therefore, contemporary business environments which are driven by technology require urgent and sustained attention by firms seeking to enhance their competitiveness. Simply put, technology is the tool through which a firm’s strategy is achieved. Online banking is an example of a technology driven business environment. It is therefore important for the banks who offer this service to understand that the strategy they adopt influences the uptake of the service being offered. The level of adoption of online banking is an indication of the success of the strategy that has been adopted by banks. Consumer perception of the service has a strong influence on its actual usage. This study has used the perceived usefulness (PU) and behavioural intention to adopt (ADO) construct from the technology adoption model and the e-strategy construct to study online banking adoption in Malaysia. The moderation effect of e-strategy on the relationship between PU and ADO was examined. To achieve this, a random sampling of urban residents in the cities Ipoh, Georgetown, Alor Setar and Kangar in Malaysia was used. A total of 2560 questionnaires were distributed but only 360 usable questionnaires were received. The analysis was conducted by using SPSS 24 and Hayes SPSS process macro. From the analysis that was conducted it was found that e-strategy had a positively significant moderating effect on the perceived usefulness of adopting online banking by the consumer. The study found that e-strategy had a stronger effect on PU when applied effectively by banks. The study therefore confirms the hypothesis that the element of strategy influences online banking adoption rates in Malaysia.

The nonlinearity in any rate of current account deficit is an important task of research because of the socalled Regime Switching Models: TAR, SETAR, Markov Switching Model, etc. Usually, the behavior of time series exhibit breaks is associated with structural changes in government policy or financial crises. In the present research it is used as an example the calculated data about the rate of current account deficit to the Bulgarian Health and Care National Product. The series are hard to be modeling because of the structural change of the government policy about the Bulgarian Health and Care National Product. The basic hypothesis that is tested in the conducted research is that when there is a case of changes in the time series in their structure it is impossible the principles of linearity assumption to be applied. In the traditional econometrics as a science the linearity is an important assumption but there are practical evidences in which most of the time series do not provide this assumption. These cases of such time series behavior are called nonlinearity series. It is important to test the linearity assumption because of the differing between the ways of modeling the series in the case of linearity and nonlinearity using the date of the rate of current account deficit to the Bulgarian Health and Care National Product. If any series do not provide the linearity assumption and also have change in the structure then the case can be modeling with TAR, SETAR or Markov Switching Model. We provided the research by questioning whether there is nonlinearity in the rate of proportion of current account deficit to the Bulgarian Health and Care National Product and it is experienced with four nonlinearity tests: Kaplan Test, McLeod-Li test, BDS Test and Tzay Test.

This study explored the nature of fiscal and monetary policy coordination and its impact on long-run sustainability in Kenya. The study employed annual time series data from 1963 to 2014. Two objectives were investigated. (i) The determinants ofmonetary and fiscal policy rules under different policy regimes. (ii) The nature of fiscal and monetary policy regimes coordination in Kenya. Markov switching models were used to determine fiscal and monetary policy regimes endogenously. The fiscal policy regime was regarded as passive if the coefficient of debt in the MS model was significant and negative. This fiscal policy regime is regarded as unsustainable since the rise in debt is associated with a deterioration of the fiscal balance. On the other hand, the active monetary policy is synonymous with contractionary monetary policy since real in interest rate reacts positively to an increase in inflation. Robust analysis conducted using self-exciting threshold models confirms that monetary and fiscal policy reaction functions are nonlinear. The study findings show that passive or unsustainable fiscal regime was more dominant over the study period. There is evidence to support coordination between fiscal and monetary policy. There is a tendency for monetary policy to actively and prudently respond to unsustainable fiscal policy. Secondly, monetary policy sequentially responds to fiscal policy. The study recommended the adoption of systematic monetary response to a periodic deviation of fiscal policy from a long-run sustainability path.

called Regime Switching Models: TAR, SETAR, Markov Switching Model, etc. Usually, the behavior of time series exhibit breaks is associated with structural changes in government policy or financial crises. In the present research it is used as an example the calculated data about the rate of current account deficit to the Bulgarian Health and Care National Product. The series are hard to be modeling because of the structural change of the government policy about the Bulgarian Health and Care National Product. The basic hypothesis that is tested in the conducted research is that when there is a case of changes in the time series in their structure it is impossible the principles of linearity assumption to be applied. In the traditional econometrics as a science the linearity is an important assumption but there are practical evidences in which most of the time series do not provide this assumption. These cases of such time series behavior are called nonlinearity series. It is important to test the linearity assumption because of the differing between the ways of modeling the series in the case of linearity and nonlinearity using the date of the rate of current account deficit to the Bulgarian Health and Care National Product. If any series do not provide the linearity assumption and also have change in the structure then the case can be modeling with TAR, SETAR or Markov Switching Model. We provided the research by questioning whether there is nonlinearity in the rate of proportion of current account deficit to the Bulgarian Health and Care National Product and it is experienced with four nonlinearity tests: Kaplan Test, McLeod-Li test, BDS Test and Tzay Test .

In this study, we model and forecast Ghana’s inflation rate using nonlinear models between the period January 1981 and August 2016. Nonlinearity tests were conducted on the logarithm of the monthly rates of inflation using Keenan and Tsay tests, and based on the results, we rejected the null hypothesis of linearity of monthly rates of inflation. We used threshold models and compared their fitness and forecasting performance with standard linear Autoregressive (AR) models. We found out that the Self-Exiting Threshold Autoregressive (SETAR) and Logistic Smooth Threshold Autoregressive (LSTAR) models fit the data better. The simple linear Autoregressive (AR) models however, out-performed the nonlinear models in terms of forecasting. Various research projects have been carried out in this area of inflation modeling in Ghana, but these researchers modeled inflation in Ghana using nonlinear models that did not account for the conditional heteroscedasticity in the model. These models have been used and empirical evidence of their relative performance has been given for the success of developed economies such as US and Europe. However, limited studies have been done in the case of Ghana. This indicates a gap in the literature and poses a challenge as to which of these models is the optimal choice for modeling economic and financial data such as inflation rates for developing countries. It is recommended that policy makers, industry players and all those interested in modeling future rates of inflation in Ghana should consider using the threshold models instead of the traditional Box and Jenkins models since the threshold models are able to capture the heteroscedasticity in the model. Also by using the threshold models, policy makers and industry players would be able to properly capture the variability persistence in the monthly rates of inflation and hence estimates would be more accurate. Lastly, from the upward trend of the out-sample forecasts, it can be predicted that Ghana would experience double digit inflation in 2017. This would have several impacts on many aspects of the economy and could erode the economic gains made in the year 2016.

The rainfall measurement can be done by using rain gauge and weather radar instruments. However, weather radar does not measure the rainfall depth directly as contrary to rain gauge. Therefore, an empirical relationship between reflectivity (Z) and rainfall rate (R) which is commonly known as reflectivity-rainfall (Z-R) relationship consisting of parameter A and exponential b (Z= A·Rb ), usually used to convert reflectivity data into rainfall rate for a radar. Presently, the Z-R relationship parameters proposed by Marshall and Palmer (1947) used in Malaysia is seem no longer suitable for Malaysia condition. Hence, a new relationship should be developed. The reflectivity data from year 2006 to year 2007 at Alor Setar radar and gauge rainfall data from 14 rain gauge in the Northern of Peninsular Malaysia were calibrated. By using the Traditional Matching Method (TMM), a new parameter was developed for Alor Setar radar which located in Northern Peninsular Malaysia. By minimizing the errors, a Mean Field Bias Correction (MFB) technique was apply to all selected parameter in this study with adjusting the value A and fixed the value b. As a results, new climatological Z-R relationship (Z=14.30R 1.9) was obtained. To justify the new relationship, validation analysis has been performed by using the five statistical measure. It was found that the validation analysis has given the best results of Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias and Pearson Correlation Coefficient (r) with value of 0.00, 0.00, 7.54, 1.00 and 0.85 respectively. Concluded that, it is suitable for radar rainfall estimation in the Northern Region of Peninsular Malaysia.

The linear time series model refers to the class of models for which fixed correlation parameters can fully explain the dependency between two random variables, but many real-life circumstances, such as monthly unemployment results, supplies and demands, interest rate, exchange rate, share prices, rainfall, etc., violate the assumption of linearity. For fitting and forecasting of nonlinear time series data, the self-exiting threshold autoregressive (SETAR) model was suggested. Using R to generate random nonlinear autoregressive data, a Monte Carlo simulation was performed, the SETAR model was fitted to the simulated data and Lafia rainfall data, Nasarawa State, Nigeria to determine the best regime orders and/or scheme number to make future forecast. Using Mean Square Error (MSE) and Akaike Information Criteria (AIC), the relative performance of models was examined. At a specific autoregressive order, regime order, sample size and step ahead, the model with minimum criteria was considered as the best. The results show that the best autoregressive and regime orders to be chosen are 3rd and 2nd [SETAR (3, 2)] respectively for fitting and forecasting nonlinear autoregressive time series data with small and moderate sample sizes. As the sample size increases, the output of the four models increases. Finally, it is shown that when sample size and number of steps forward are increased, the efficiency and forecasting capacity of the four models improves.

In this paper, we use the local influence method to study a vector autoregressive model under Student′s t-distributions. We present the maximum likelihood estimators and the information matrix. We establish the normal curvature diagnostics for the vector autoregressive model under three usual perturbation schemes for identifying possible influential observations. The effectiveness of the proposed diagnostics is examined by a simulation study, followed by our data analysis using the model to fit the weekly log returns of Chevron stock and the Standard & Poor's 500 Index as an application.

In most researches in the area of profile monitoring, it is assumed that observations are independent of each other. Whereas, this assumption is usually violated in practice and observations are autocorrelated. The control charts are the most important tools of the statistical process control which are used to monitor the processes over time. The control charts usually signal the out-of-control status of the process with a time delay. Whereas knowing real-time of the change (change point), one can achieve great savings on time and expenses. In this paper, the estimation of the change point in the simple linear profiles with AR (1) autocorrelation structure within each profile is considered. In the proposed method, by acquiring the joint probability density function of the autocorrelated observations, the maximum likelihood estimation method is applied to estimate the step change point. Here, we specifically focus on Phase II and compare the performance of the proposed estimator with the existing estimators in the literature through simulation studies. In addition, the application of the proposed estimator in comparison with the two estimators is illustrated through a real case. The results show the better performance of the proposed estimator.

The threshold autoregressive model with generalized autoregressive conditionally heteroskedastic GARCH specification is a popular nonlinear model that captures the well-known asymmetric phenomena in financial market data. The switching mechanisms of hysteretic autoregressive GARCH models are different from threshold autoregressive model with GARCH as regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. This paper conducts a Bayesian model comparison among competing models by designing an adaptive Markov chain Monte Carlo sampling scheme. We illustrate the performance of three kinds of criteria by comparing models with fat-tailed and/or skewed errors: deviance information criteria, Bayesian predictive information, and an asymptotic version of Bayesian predictive information. A simulation study highlights the properties of the three Bayesian criteria and the accuracy as well as their favorable performance as model selection tools. We demonstrate the proposed method in an empirical study of 12 international stock markets, providing evidence to strongly support for both models with skew fat-tailed innovations. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we reconsider the mixture vector autoregressive model, which was proposed in the literature for modelling non-linear time series. We complete and extend the stationarity conditions, derive a matrix formula in closed form for the autocovariance function of the process and prove a result on stable vector autoregressive moving-average representations of mixture vector autoregressive models. For these results, we apply techniques related to a Markovian representation of vector autoregressive moving-average processes. Furthermore, we analyse maximum likelihood estimation of model parameters by using the expectation–maximization algorithm and propose a new iterative algorithm for getting the maximum likelihood estimates. Finally, we study the model selection problem and testing procedures. Several examples, simulation experiments and an empirical application based on monthly financial returns illustrate the proposed procedures.

The radiation data is important for solar data analysis which is significantly for modeling, designing on energy conversion system development and outputting it as a variable that is needed by other related scientific researches.Obtaining the complete data for solar intensity is a tough job when the technical problem is raised either from the sensor’s devices or faulty data transmission. Next, missing values occur accordingly.As a solution, missing value estimation has been introduced to substitute the absent values with imputed values.This article is highlighted to propose cubic Bezier interpolation as potential techniques to generate replacement values. Afterwards, compare the performance capability of few submitted interpolation techniques dealing with difference amount of simulated missing data. Based on our knowledge, curiosity works for replaceable values by applying Bezier interpolation on solar radiation data is limited. The cubic Bezier interpolation performs very well on both areas but the most effective condition is at the lower missing values and higher absent values that come from Alor Setar and Penang cities, respectively, which offer the lowest RMSE value and the r² value nearest to 1

Integer-valued time series analysis offers various applications in biomedical, financial, and environmental research. However, existing works usually assume no or constant over-dispersion. In this paper, we propose a new model for time series of counts, the autoregressive conditional negative binomial model that has a time-varying conditional autoregressive mean function and heteroskedasticity. The location and scale parameters of the negative binomial distribution are flexible in the proposed set-up, inducing dynamic over-dispersion. We adopt Bayesian methods with a Markov chain Monte Carlo sampling scheme to estimate model parameters and utilize deviance information criterion for model comparison. We conduct simulations to investigate the estimation performance of this sampling scheme for the proposed negative binomial model. To demonstrate the proposed approach in modelling time-varying over-dispersion, we consider two types of criminal incidents recorded by New South Wales (NSW) Police Force in Australia. We also fit the autoregressive conditional Poisson model to these two datasets. Our results demonstrate that the proposed negative binomial model is preferable to the Poisson model.

To better describe the characteristics of time series of counts such as over-dispersion, asymmetry, structural change, and a large proportion of zeros, this paper considers a class of generalized Poisson autoregressive models that properly capture flexible asymmetric and nonlinear responses through a switching mechanism. We also investigate zero-inflated generalized Poisson autoregressive models with a structural break that can cope with data having a large portion of zeros and changes in dynamics. We employ an adaptive Markov Chain Monte Carlo (MCMC) sampling scheme to locate the structural break and to estimate model parameters. As an illustration, we conduct a simulation study and empirical analysis of New South Wales crime data sets. Our findings show a remarkable improvement by modeling the data based on such generalized Poisson autoregressive models and the Bayesian method.

In this paper, a general framework of mixture time series is introduced as General Mixture of Linear Autoregressive. This framework contains well known mixture time series such as Mixture Autoregressive, Local Linear Global Neural Network and Logistic Mixture Autoregressive models. The sufficient conditions for geometric ergodicity of such model are derived.

In this article, the problem of interest is testing the conditional heteroscedasticity of Poisson autoregressive model. We construct a non parametric test statistic based on empirical likelihood method. The asymptotic distribution of the proposed statistic is derived and its finite-sample property is examined through Monte Carlo simulations. The simulation results show that the proposed method is good for practical use.

This article develops a novel test for a unit root in general transitional autoregressive models, which is based on the infimum of t-ratios for the coefficient of a parametrized transition function. Our test allows for very flexible specifications of the transition function and short-run dynamics and is significantly more powerful than all the other existing tests. Moreover, we develop a large sample theory general enough to deal with randomly drifting parameter spaces, which is essential to properly test for a unit root against stationary transitional models. An empirical application of our test to the exchange rate data is also provided.

This paper is concerned with properties of a transitional Markov switching autoregressive (TMSAR) model, together with its maximum-likelihood estimation and inference. We extend existing MSAR models by allowing dependence of AR parameters on hidden states at time points prior to the current time t. A stationary solution is given and expressions for the theoretical autocovariance function are derived. Two time series are analyzed and the new model outperforms two existing MSAR models in terms of maximized log-likelihood, residual correlations, and one-step-ahead forecasting performance. The new model also gives more regime changes in agreement with real events.

This note is concerned with the limiting properties of the least squares estimation for the random coefficient autoregressive model. In contrast with existing results, ours is applicable to a wide range of models under more general assumptions.

Despite significant research advances achieved during the last decades, seemingly inconsistent forecasting results related to stochastic, chaotic, and black-box approaches have been reported. Herein, we attempt to address the entropy/complexity resulting from hydrological and climatological conditions. Accordingly, mutual information function, correlation dimension, averaged false nearest neighbor with E1 and E2 quantities, and complexity analysis that uses sample entropy coupled with iterative amplitude adjusted Fourier transform were employed as nonlinear deterministic identification tools. We investigated forecasting of daily streamflow for three climatologically different Swedish rivers, Helge, Ljusnan, and Kalix Rivers using self-exciting threshold autoregressive (SETAR), k-nearest neighbor (k-nn), and artificial neural networks (ANN). The results suggest that the streamflow in these rivers during the 1957–2012 period exhibited dynamics from low to high complexity. Specifically, (1) lower complexity lead to higher predictability at all lead-times and the models’ worst performances were obtained for the most complex streamflow (Ljusnan River), (2) ANN was the best model for 1-day ahead forecasting independent of complexity, (3) SETAR was the best model for 7-day ahead forecasting by means of performance indices, especially for less complexity, (4) the largest error propagation was obtained with the k-nn and ANN and thus these models should be carefully used beyond 2-day forecasting, and (5) higher number input variables except for the dominant variables made insignificant impact on forecasting performances for ANN and k-nn models.

To address the uncertainty in outputs of numerical weather prediction (NWP) models, ensembles of forecasts are used. To obtain such an ensemble of forecasts, the NWP model is run multiple times, each time with variations in the mathematical representations of the model and/or initial or boundary conditions. To correct for possible biases and dispersion errors in the ensemble, statistical postprocessing models are frequently employed. These statistical models yield full predictive probability distributions for a weather quantity of interest and thus allow for a more accurate representation of forecast uncertainty. This article proposes to combine the state-of-the-art Ensemble Model Output Statistics (EMOS) with an ensemble that is adjusted by an autoregressive process fitted to the respective error series by a spread-adjusted linear pool in the case of temperature forecasts. The basic ensemble modification technique we introduce may be used to simply adjust the ensemble itself as well as to obtain a full predictive distribution for the weather quantity. As demonstrated for temperature forecasts from the European Centre for Medium-Range Weather Forecasts ensemble, the proposed procedure gives rise to improved results over the basic (local) EMOS method.

Bootstrap-based unit root tests are a viable alternative to asymptotic distribution-based procedures and, in some cases, are preferable because of the serious size distortions associated with the latter tests under certain situations. While several bootstrap-based unit root tests exist for autoregressive moving average processes with homoskedastic errors, only one such test is available when the innovations are conditionally heteroskedastic. The details for the exact implementation of this procedure are currently available only for the first order autoregressive processes. Monte-Carlo results are also published only for this limited case. In this paper we demonstrate how this procedure can be extended to higher order autoregressive processes through a transformed series used in augmented Dickey–Fuller unit root tests. We also investigate the finite sample properties for higher order processes through a Monte-Carlo study. Results show that the proposed tests have reasonable power and size properties.

This paper introduces a new model to generate a stationary Markov sequence of exponential random variables, which is a mixture of the first order exponential autoregressive model and a first order minification model. Apart from studying the probabilistic properties of the model we have also proposed methods for estimating the parameters to check its suitability in analyzing the practical situations. The applications of the model are illustrated using simulation and data analysis.

The aim of this paper is to evaluate the forecasting performance of SETAR models with an application to the Industrial Production Index (IPI) of four major European countries over a period which includes the last Great Recession. Both point and interval forecasts are considered at different horizons against those obtained from two linear models. We follow the approach suggested by Terasvirta et al. (2005) according to which a dynamic specification may improve the forecast performance of the nonlinear models with respect to the linear models. We re-specify the models every twelve months and we find that the advantages of this procedure are particularly evident in the forecast rounds immediately following the re-specification.

This article first proposes a score-based test for a double autoregressive model against a threshold double autoregressive (AR) model. It is an asymptotically distribution-free test and is easy to implement in practice. The article further studies the quasi-maximum likelihood estimation of a threshold double autoregressive model. It is shown that the estimated threshold is n-consistent and converges weakly to a functional of a two-sided compound Poisson process and the remaining parameters are asymptotically normal. Our results include the asymptotic theory of the estimator for threshold AR models with autoregressive conditional heteroscedastic (ARCH) errors and threshold ARCH models as special cases, each of which is also new in literature. Two portmanteau-type statistics are also derived for checking the adequacy of fitted model when either the error is nonnormal or the threshold is unknown. Simulation studies are conducted to assess the performance of the score-based test and the estimator in finite sa...

The aim of this paper is to examine the interaction patterns between equity and real estate returns in 8 emerging economies from central and south-eastern Europe. For that purpose we apply the TAR model entailing two regimes and endogenously determined threshold, delay parameter, and lag length. The results suggest that in all the countries, the interaction between equity and real estate returns is subject to regime switching in at least one direction. Equity returns in general seem to be much more sensitive to real estate returns changes, while the reaction of real estate returns to changes in equity returns is not always present and it is much more subdued and delayed. In the majority of the countries, the value of the threshold is large and negative suggesting that equity returns (real estate returns) react differently to large negative changes of real estate returns (equity returns) when compared to changes of different magnitudes and alternative signs. Equity returns react more strongly to large negative changes of the real estate returns than to small negative or positive real estate returns changes, while the reaction of the real estate returns to the equity returns changes varies across countries, making neither regime more prevalent.

In this paper, we study the consistence of a recurrent stochastic algorithm under quasi-associated random errors. Kholev's algorithm estimates an unknown non-zero parameter θ introduced in a nonlinear autoregressive model. We establish the complete convergence and deduce a confidence interval for θ.

A nonlinear stochastic self-exciting threshold autoregressive (SETAR) model and a chaotic k-nearest neighbour (k-nn) model, for the first time, were compared in one and multi-step ahead daily flow forecasting for nine rivers with low, medium, and high flows in the western United States. The embedding dimension and the number of nearest neighbours of the k-nn model and the parameters of the SETAR model were identified by a trial-and-error process and a least mean square error estimation method, respectively. Employing the recursive forecasting strategy for the first time in multi-step forecasting of SETAR and k-nn, the results indicated that SETAR is superior to k-nn by means of performance indices. SETAR models were found to be more efficient in forecasting flows in one and multi-step forecasting. SETAR is less sensitive to the propagated error variances than the k-nn model, particularly for larger lead times (i.e., 5 days). The k-nn model should carefully be used in multi-step ahead forecasting where peak flow forecasting is important by considering the risk of error propagation.

Various models have been proposed in the literature to study non-negative integer-valued time series. In this paper, we study estimators for the generalized Poisson autoregressive process of order 1, a model developed by Alzaid and Al-Osh [1]. We compare three estimation methods, the methods of moments, quasi-likelihood and conditional maximum likelihood and study their asymptotic properties. To compare the bias of the estimators in small samples, we perform a simulation study for various parameter values. Using the theory of estimating equations, we obtain expressions for the variance-covariance matrices of those three estimators, and we compare their asymptotic efficiency. Finally, we apply the methods derived in the paper to a real time series.

In regions where economies that are highly dependent on agricultural activities, estimation of evapotranspiration is vital for scheduling and managing the water resources. In this study, the estimation of the reference evapotranspiration (ET0) was performed for the west coast of Peninsular Malaysia, where oil palm is the major crop. Three stations, namely Station 48603 (Alor Setar), Station 48620 (Sitiawan) and Station 48650 (KLIA Sepang) were selected representing different regions along the west coast. Estimation of ET0 using the popular and conventional Penman-Monteith (PM) model is data intensive. On the contrary, simpler empirical models such as the temperature-based Hargreaves-Samani (HS), mass transfer-based Romanenko and radiation-based Makkink models are less accurate, unstable and are greatly affected by local climate conditions. In order to solve the problems of the PM and other simple empirical models, the SVM was used to mimic the simple empirical models. The results of this study revealed that the HS and Makkink models were favorable for ET0 estimation at the northern region, whereas for the central region, the Makkink model was not suitable. Simple averaging of the empirical models improved the estimations. However, the model was still deemed to be poor as the MARE ranged from 0.087 to 0.099, with the R2 still being possible to dip below 0.900. It was found that the SVM could greatly improve the ET0 estimation. Unlike the empirical models, the regional factor had minimal effect on the ET0 estimation; where the SVM-Mak model performed better than the SVM-HS and SVM-Rom models over the whole study area. The best performance was achieved by SVM-Mak model at Station 48620 (Sitiawan) where the MARE was only 0.020 when compared to the chosen standard PM model. It was found that the simple averaging of the SVM models could further boost the performance, but such an approach was non-rewarding. The SVM models were found to be better alternatives for the empirical models when using similar inputs. The study concluded that the SVM model which imitated the Makkink model was suitable for ET0 estimation along the west coast of Peninsular Malaysia.

This study seeks to investigate the nature of fiscal policy regime in Kenya, and the extent to which fiscal policy is sustainable in the long run by taking into account periodic regime changes. Markov-switching models were used to determine fiscal policy regimes endogenously. Regime switching tests were used to test whether the No-Ponzi game condition and the debt stabilizing condition were met. The results established that the regime-switching model was suitable in explaining regime sustainable and sustainable cycles. An investigation of fiscal policy regimes established that both sustainable and unsustainable regimes were dominant and each lasted for an average of four years. There was evidence to suggest the existence of procyclical fiscal policy in Kenya. Regime switching tests for long-run sustainability suggested that the No-Ponzi game condition weakly holds in the Kenyan economy. Regime-based sensitivity analysis suggests that the persistence of unsustainability regime for more than four years could threaten long-run fiscal sustainability. Sensitivity tests are conducted by resorting to (i) Self-Exciting Threshold Autoregressive Models at the country-level, and (ii) non-linear Granger causalities across a Feed- Forward Artificial Neural Network composed of East-African countries (Burundi, Kenya, Rwanda, Tanzania and Uganda).

As a kind of clean energy, wind energy has become the focus among the world because of its rich reserves and easiness of exploitation and utilization. In order to reduce the abandoned of wind resource, stabilize the power quality and realize the power grid dispatching, The power grid needs to know the changes of the wind speed at every moment and accurately predict the change of the wind speed at the next moment, so the high quality wind speed forecast is more and more important. The analysis shows that the wind speed series has obvious nonlinear characteristics, so the traditional linear model is not accurate enough. In this paper, an improved nonlinear self excitation threshold autoregressive (SETAR) model, which is the self excitation threshold autoregressive moving average (SETARMA) model, is proposed to predict the wind speed. A detailed modeling method of SETARMA model is presented, and the method of testing the nonlinearity of wind speed series is discussed in this paper. The model is verified by a case study of a wind power plant in Spain, the simulation results show that compared with the traditional ARMA prediction methods, the wind speed prediction based on SETARMA model is better and has a higher accuracy.

The unquenchable thirst of several sectors to crude oil in the recent years makes a common belief regarding its key role towards the acceleration of the recent economic recession and financial instability. This paper aims to examine the impact of oil shocks on the sovereign credit risk for a sample of 38 worldwide oil-producing and non-oil producing countries, over a period ranging from January 2006 to March 2017. In contrast to the existing literature, CDS volatility is employed as a measure for the creditworthiness level, rather than the commonly used CDS spreads first-order moment. The methodological framework used in this paper goes beyond previous studies and takes into account more financial data features (long memory behavior, asymmetric effects and nonlinear-ities) according to a self-exciting regime switching model. Results reveal some dissimilarities in the explanatory power of the exogenous variables between regimes and across countries. Particularly, restricted evidence of oil significant impact on sovereign CDS volatility are detected during the stable regime, whilst during the risky regime credit volatility becomes more sensitive to oil market conditions for most of the studied markets. Generally, the decline in oil price worsens the public finances tenability whether the country is oil-related or not. JEL Classification G1, G15, E44.

In this paper, we develop new threshold cointegration tests with SETAR and MTAR adjustment allowing for the presence of structural breaks in the equilibrium equation. We propose a simple procedure to simultaneously estimate the previously unknown breakpoint and test the null hypothesis of no cointegration. Thereby, we extend the well-known residual-based cointegration test with regime shift introduced by Gregory and Hansen (1996a) to include forms of nonlinear adjustment. We derive the asymptotic distribution of the test statistics and demonstrate the finite-sample performance of the tests in a series of Monte Carlo experiments. We find a substantial decrease of power of the conventional threshold cointegration tests caused by a shift in the slope coefficient of the equilibrium equation. The proposed tests perform superior in these situations. An application to the ‘rockets and feathers’ hypothesis of price adjustment in the US gasoline market provides empirical support for this methodology.

Traffic congestion is a major problem in developing areas, where it disrupts emergency response and more severe that may cause delays and lead to loss of life and loss of valuable goods. This study carried out with the aim of applying geographic information system (GIS) to provide an effective and efficient database for emergency responses in Alor Setar. It examines a methodology where to identify the closest emergency responses and to solve the problem of emergency responses to each incident or vice versa by considering the historical traffic data year 2015 using shortest path and time slider analysis. The finding of this study is development of dynamic road network model for incorporating historical traffic data and performing the best way for a given time and date. Hence, the road network model is considered dynamic in the sense that cost attributes such as travel speed change with respect to time. This study is beneficial to emergency responses as they able to utilize the database to reduce congestion and contribute to efficient emergency responses management.

In this paper a robust approach to modelling electricity spot prices is introduced. Differently from what has been recently done in the literature on electricity price forecasting, where the attention has been mainly drawn by the prediction of spikes, the focus of this contribution is on the robust estimation of nonlinear SETARX models (Self-Exciting Threshold Auto Regressive models with eXogenous regressors). In this way, parameters estimates are not, or very lightly, influenced by the presence of extreme observations and the large majority of prices, which are not spikes, could be better forecasted. A Monte Carlo study is carried out in order to select the best weighting function for Generalized M-estimators of SETAR processes. A robust procedure to select and estimate nonlinear processes for electricity prices is introduced, including robust tests for stationarity and nonlinearity and robust information criteria. The application of the procedure to the Italian electricity market reveals the forecasting superiority of the robust GM-estimator based on the polynomial weighting function respect to the non-robust Least Squares estimator. Finally, the introduction of external regressors in the robust estimation of SETARX processes contributes to the improvement of the forecasting ability of the model.