Examine the Reliability of Econometrics Software: An Empirical Comparison of Time Series Modelling

Researchers must understand that naively relying on the reliability of statistical software packages may result in suboptimal, biased, or erroneous results, which affects applied economic theory and the conclusions and policy recommendations drawn from it. To create confidence in a result, several software packages should be applied to the same estimation problem. This study examines the results of three software packages (EViews, R, and Stata) in the analysis of time-series econometric data. The time-series data analysis which presents the determinants of macroeconomic growth of Sri Lanka from 1978 to 2020 has been used. The study focuses on testing for stationarity, cointegration, and significant relationships among the variables. The Augmented Dickey-Fuller and Phillips Perron tests were employed in this study to test for stationarity, while the Johansen cointegration test was utilized to test for cointegration. The study employs the vector error correction model to assess the short-run and long-term dynamics of the variables in an attempt to determine the relationship between them. Finally, the Granger Causality test is employed in order to examine the linear causation between the concerned variables. The study revealed that the results produced by three software packages for the same dataset and the same lag order vary significantly. This implies that time series econometrics results are sensitive to the software that is used by the researchers while providing different policy implications even for the same dataset. The present study highlights the necessity of further analysis to investigate the impact of software packages in time series analysis of economic scenarios.

INDUSTRIAL PRODUCTION IN GERMANY AND AUSTRIA:A CASE STUDY IN STRUCTURAL TIME SERIES MODELLING

Industrial production series are volatile and often cyclical. Time series models can be used toestablish certain stylized facts, such as trends and cycles, which may be present in these series. Incertain situations, it is also possible that common factors, which may have an interesting interpretation,can be detected in production series. Series from two neighboring countries with close economicrelationships, such as Germany and Austria, are especially likely to exhibit such joint stylized facts.

A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting

Time series forecasting plays a significant role in numerous applications,including but not limited to,industrial planning,water consumption,medical domains,exchange rates and consumer price index.The main problem is insufficient forecasting accuracy.The present study proposes a hybrid forecastingmethods to address this need.The proposed method includes three models.The first model is based on the autoregressive integrated moving average(ARIMA)statistical model;the second model is a back propagation neural network(BPNN)with adaptive slope and momentum parameters;and the thirdmodel is a hybridization between ARIMA and BPNN(ARIMA/BPNN)and artificial neural networks and ARIMA(ARIMA/ANN)to gain the benefits of linear and nonlinearmodeling.The forecasting models proposed in this study are used to predict the indices of the consumer price index(CPI),and predict the expected number of cancer patients in the Ibb Province in Yemen.Statistical standard measures used to evaluate the proposed method include(i)mean square error,(ii)mean absolute error,(iii)root mean square error,and(iv)mean absolute percentage error.Based on the computational results,the improvement rate of forecasting the CPI dataset was 5%,71%,and 4%for ARIMA/BPNN model,ARIMA/ANN model,and BPNN model respectively;while the result for cancer patients’dataset was 7%,200%,and 19%for ARIMA/BPNNmodel,ARIMA/ANN model,and BPNNmodel respectively.Therefore,it is obvious that the proposed method reduced the randomness degree,and the alterations affected the time series with data non-linearity.The ARIMA/ANN model outperformed each of its components when it was applied separately in terms of increasing the accuracy of forecasting and decreasing the overall errors of forecasting.

Assessing the effectiveness of quarantine measures during the COVID-19 pandemic in Chile using Bayesian structural time series models

Background:With the emergence of the COVID-19 pandemic,all existing health protocols were tested under the worst health crisis humanity has experienced since the Black Death in the 14th century.Countries in Latin America have been the epicenter of the COVID-19 pandemic,with more than 1.5 million people killed.Worldwide health measures have included quarantines,border closures,social distancing,and mask use,among others.In particular,Chile implemented total or partial quarantine measures depending on the number of infections in each region of the country.Therefore,it is necessary to study the effectiveness of these quarantines in relation to the public health measures implemented by government entities at the national level.Objective:The main objective of this study is to analyze the effectiveness of national-and region-level quarantines in Chile during the pandemic based on information published by the Chilean Ministry of Health,and answers to the following question are sought:Were quarantine measures in Chile effective during the COVID-19 pandemic?Methods:The causal effect between the rates of COVID-19 infections and the population rates in Phase 1 and Phase 2 quarantines in the period from March 2020 to March 2021 in different regions of Chile were evaluated using intervention analyses obtained through Bayesian structural time series models.In addition,the Kendall correlation coefficient obtained through the copula approach was used to evaluate the comovement between these rates.Results:In 75%of the Chilean regions under study(12 regions out of a total of 16),an effective Phase 1 quarantine,which was implemented to control and reduce the number of cases of COVID-19 infection,was observed.The main regions that experienced a decrease in cases were those located in the north and center of Chile.Regarding Phase 2,the COVID-19 pandemic was effectively managed in 31%(5 out of 16)of the regions.In the southcentral and extreme southern regions of Chile,the effectiveness of these phases was null.Conclusion:The findings indicate that in the northern and central regions of Chile,the Phase 1 quarantine application period was an effective strategy to prevent an increase in COVID-19 infections.The same observation was made with respect to Phase 2,which was effective in five regions of northern Chile;in the rest of the regions,the effectiveness of these phases was weak or null.

TIME SERIES MODEL OF LONG-RANGE PREDICTION AND ITS APPLICATION

By generalizing the concept of mean in mathematical statistics to a mean generation function(MGF), the extended matrix of MGF is defined and then a new model of time series is presented.A calculatingseheme for modelling of monovariate time series is deduced cooperating with a normalization procedure of vector and a couple score criterion.An example of climatic prediction for ten-year scale is given in this paper,the tendency of variation for every year can be predicted skillfully with the model.

Multi-factor high-order intuitionistic fuzzy timeseries forecasting model

Fuzzy sets theory cannot describe the neutrality degreeof data, which has largely limited the objectivity of fuzzy time seriesin uncertain data forecasting. With this regard, a multi-factor highorderintuitionistic fuzzy time series forecasting model is built. Inthe new model, a fuzzy clustering algorithm is used to get unequalintervals, and a more objective technique for ascertaining membershipand non-membership functions of the intuitionistic fuzzy setis proposed. On these bases, forecast rules based on multidimensionalintuitionistic fuzzy modus ponens inference are established.Finally, contrast experiments on the daily mean temperature ofBeijing are carried out, which show that the novel model has aclear advantage of improving the forecast accuracy.

Multimodality Prediction of Chaotic Time Series with Sparse Hard-Cut EM Learning of the Gaussian Process Mixture Model

The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. （2） An effective sparse hard-cut expec- tation maximization （SHC-EM） learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.

Reconstructing historical forest fire risk in the non-satellite era using the improved forest fire danger index and long short-term memory deep learning-a case study in Sichuan Province,southwestern China

Historical forest fire risk databases are vital for evaluating the effectiveness of past forest management approaches,enhancing forest fire warnings and emergency response capabilities,and accurately budgeting potential carbon emissions resulting from fires.However,due to the unavailability of spatial information technology,such databases are extremely difficult to build reliably and completely in the non-satellite era.This study presented an improved forest fire risk reconstruction framework that integrates a deep learning-based time series prediction model and spatial interpolation to address the challenge in Sichuan Province,southwestern China.First,the forest fire danger index(FFDI)was improved by supplementing slope and aspect information.We compared the performances of three time series models,namely,the autoregressive integrated moving average(ARIMA),Prophet and long short-term memory(LSTM)in predicting the modified forest fire danger index(MFFDI).The bestperforming model was used to retrace the MFFDI of individual stations from 1941 to 1970.Following this,the Anusplin spatial interpolation method was used to map the distributions of the MFFDI at five-year intervals,which were then subjected to weighted overlay with the distance-to-river layer to generate forest fire risk maps for reconstructing the forest fire danger database.The results revealed LSTM as the most accurate in fitting and predicting the historical MFFDI,with a fitting determination coefficient(R^2)of 0.709,mean square error(MSE)of0.047,and validation R^2 and MSE of 0.508 and 0.11,respectively.Independent validation of the predicted forest fire risk maps indicated that 5 out of 7 historical forest fire events were located in forest fire-prone areas,which is higher than the results determined from the original FFDI(2 out of 7).This proves the effectiveness of the improved MFFDI and indicates a high level of reliability of the historical forest fire risk reconstruction method proposed in this study.

Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River,China

This paper presents the application of autoregressive integrated moving average （ARIMA）, seasonal ARIMA （SARIMA）, and Jordan-Elman artificial neural networks （ANN） models in forecasting the monthly streamflow of the Kizil River in Xinjiang, China. Two different types of monthly streamflow data （original and deseasonalized data） were used to develop time series and Jordan-Elman ANN models using previous flow conditions as predictors. The one-month-ahead forecasting performances of all models for the testing period （1998-2005） were compared using the average monthly flow data from the Kalabeili gaging station on the Kizil River. The Jordan-Elman ANN models, using previous flow conditions as inputs, resulted in no significant improvement over time series models in one-month-ahead forecasting. The results suggest that the simple time series models （ARIMA and SARIMA） can be used in one-month-ahead streamflow forecasting at the study site with a simple and explicit model structure and a model performance similar to the Jordan-Elman ANN models.

On Mixed Model for Improvement in Stock Price Forecasting

Stock market trading is an activity in which investors need fast and accurate information to make effective decisions.But the fact is that forecasting stock prices by using various models has been suffering from low accuracy,slow convergence,and complex parameters.This study aims to employ a mixed model to improve the accuracy of stock price prediction.We present how to use a random walk based on jump-diffusion,to obtain stock predictions with a good-fitting degree by adjusting different parameters.Aimed at getting better parameters and then using the time series model to predict the data,we employed the time series model to smooth the sequence utilizing logarithm and difference,which successfully resulted in drawing the auto-correlation figure and partial the auto-correlation figure.According to the comparative analysis,which focuses on checking the mean absolute error,including root mean square error and R square evaluation index,we have drawn a clear conclusion that our mixed model prediction effect is relatively good.In the context of Chinese stocks,the hybrid random walk model is very suitable for predicting stocks.It can“interpret”the randomness of stocks very well,and it also has an unparalleled prediction effect compared with other models.Based on the time series model’s application in this paper,the abovementioned series is more suitable for predicting trends.

Analysis of seasonal position variation for selected GNSS sites in Poland using loading modelling and GRACE data

In this study we compared weekly GNSS position time series with modelled values of crustal deformations on the basis of Gravity Recovery and Climate Experiment （GRACE） data. The Global Navigation Satellite Systems （GNSS） time series were taken from homogeneously reprocessed global network solutions within the International GNSS Service （IGS） Reprucessing 1 project and from regional solutions performed by Warsaw University of Technology （WUT） European Permanent Network （EPN） Local Analysis Center （LAC） within the EPN reprocessing project. Eight GNSS sites from the territory of Poland with observation timespans between 2.5 and 13 years were selected for this study. The Total Water Equivalent （TWE） estimation from GRACE data was used to compute deformations using the Green＇s function formalism. High frequency components were removed from GRACE data to avoid aliasing problems. Since GRACE observes mainly the mass transport in continental storage of water, we also compared GRACE deformations and the GNSS position time series, with the deformations computed on the basis of a hydrosphere model. We used the output of Water GAP Hydrology Model （WGHM） to compute deformations in the same manner as for the GRACE data. The WGHM gave slightly larger amplitudes than GNSS and GRACE. The atmospheric non-tidal loading effect was removed from GNSS position time series before comparing them with modelled deformations. The results confirmed that the major part of observed seasonal variations for GNSS vertical components can be attributed to the hy- drosphere loading. The results for these components agree very well both in the amplitude and phase. The decrease in standard deviation of the residual GNSS position time series for vertical components corrected for the hydrosphere loading reached maximally 36% and occurred for all but one stations for both global and regional solutions. For horizontal components the amplitudes are about three times smaller than for vertical components therefore the comparison is much more complicated and the conclusions are ambiguous.

MODELING AND PREDICTION CONCERNING TIME SERIES OF FLOOD/DROUGHT RUNS USING THE SELF-EXCITING THRESHOLD AUTOREGRESSIVE MODEL

When linear regressive models such as AR or ARMA model are used for fitting and predicting climatic time series,results are often not sufficiently good because nonlinear variations in the time series.In this paper, a nonlinear self-exciting threshold autoregressive(SETAR)model is applied to modeling and predicting the time series of flood/drought runs in Beijing,which were derived from the graded historical flood/drought records in the last 511 years(1470—1980).The results show that the modeling and predicting with the SETAR model are much better than that of the AR model.The latter can predict the flood/drought runs with a length only less than two years,while the formal can predict more than three-year length runs.This may be due to the fact that the SETAR model can renew the model according to the run-turning points in the process of predic- tion,though the time series is nonstationary.

Discussion on the paper “Analyzing short time series data from periodically fluctuating rodent populations by threshold models: A nearestblock bootstrap approach”

This is a very attractive article. It combines fascinating new methodology with a most interesting dataset, and a highly motivating presentation. However, despite the many

Discussion on the paper “Analyzing short time series data from periodically fluctuating rodent populations by threshold models: A nearestblock bootstrap approach”

The authors are to be congratulated for an innovative paper in terms of both modelling methodology and subject matter significance. The analysis of short time series is known to be

Sensitivity Test and Data Analysis for Storage Reliability Assessment of Explosive Initiator

The explosive initiator is one kind of sensitivity products with long life and high reliability.In order to improve the storage reliability assessment,the method of storage reliability assessment for explosive initiator was proposed based on time series model using the sensitivity test data.In the method,the up and down test was used to estimate the distribution parameters of threshold.And an approach to design the up and down test was present to draw better estimations.Furthermore,the method of shrinkage estimation was introduced to get a better estimation of scale parameter by combining the sample information with prior information.The simulation result shows that the shrinkage estimation is better than traditional estimation under certain conditions.With the distribution parameters estimations,the time series models were used to describe the changing trends of distribution parameters along with storage time.Then for a fixed storage time,the distribution parameters were predicted based on the models.Finally,the confidence interval of storage reliability was obtained by fiducial inference.The illustrative example shows that the method is available for storage reliability assessment of the explosive initiator with high reliability.

The Analysis of Impact of Brexit on the Post-Brexit EU Using Intervented Multivariate Time Series

The UK is the most important partner of the EU in terms of economic and other fields due to the geographical proximity.It was one of the largest economies in the EU and its per capita income is higher than the EU average,so it is a net contributor to the EU.With UKs membership of the EU ended on 31 January 2019,there are concerns that the Brexit may have a significant impact on the EU,resulting in social,economic,political,and institutional changes,etc.in EU.While the impact of Brexit on the UK has always been the subject of considerable scholarly interest in recent years,there is relatively little literature on the impact of Brexit on the EU.This paper focuses on the evaluation of the impact of Brexit on the EU economy and other relevant aspects along three dimensions:GDP,PPP,Quarterly GDP growth.Employing powerful quantitative analysis technology that includes vector autoregression model,multivariate time series model with intervention variables,and autoregression integrated moving average,this paper obtains the important and novel evidence about the potential impact of Brexit on the EU economy,pointing out that Brexit is of far-reaching significance to the EU.This analysis uses several statistical models to screen out several key influencing factors,which can be used to predict the total GDP of EU in the next five years.The results show that EU economy will react negatively to"no-deal"Brexit,and its growth rate of economy will slow down significantly in next 5 years.Finally,we put forward relevant policy suggestions on how to deal with the negative impact of Brexit on EU.

Trading Strategies for All Stock Programs

Market traders buy and sell volatile assets frequently,with a goal to maximize their total return.There is usually a commission for each purchase and sale.Two such assets are gold and bitcoin.In order to solve the existing issues of purchases between gold and bitcoin,given that we have 1,000 USD,what strategies should we take to maximize our profits?In this article,the authors established seven models to predict the value of gold and bitcoins and how you should buy them,as the trends of value fluctuate,our models must be accurate enough to avoid being influenced.Targeted at that,the content is divided into three parts.For part 1:The authors selected several indicators that feature how the stock runs.For instance,price of gold and profit of gold to build first two models,which are the risk of investment model and the judgment on bull-or-bear market model.Then we use these models to evaluate whether it is safe to invest.The models are as follows:bear-bull market judgment model,risk of investment evaluation model,prediction model,trade model.For part 2:Based on the data concerned,the authors established the time series model to predict the way the market fluctuates.Meanwhile,the result of this model can be applied in correcting the results of former two models so as to make it more accurate.For part 3:The authors combined models above to give the best trading strategy.In addition,we improved the models by adding more indicators to make it more precise.We hope that by applying our models and strategies,you can successfully maximize your profit.

Confidence Intervals of Variance Functions in Generalized Linear Model

In this paper we introduce an appealing nonparametric method for estimating variance and conditional variance functions in generalized linear models （GLMs）, when designs are fixed points and random variables respectively, Bias-corrected confidence bands are proposed for the （conditional） variance by local linear smoothers. Nonparametric techniques are developed in deriving the bias-corrected confidence intervals of the （conditional） variance. The asymptotic distribution of the proposed estimator is established and show that the bias-corrected confidence bands asymptotically have the correct coverage properties. A small simulation is performed when unknown regression parameter is estimated by nonparametric quasi-likelihood. The results are also applicable to nonparamctric autoregressive times series model with heteroscedastic conditional variance.

Using state space models as a statistical impact measurement of survey redesigns: a case study of the labour force survey of the Australian Bureau of Statistics

The goals of any major business transformation programme in an official statistical agency often include improving data collection efficiency,data processing methodologies and data quality.However,the achievement of such improvements may have transitional statistical impacts that could be misinterpreted as real-world changes if they are not measured and handled appropriately.This paper describes a development work that sought to explore the design and analysis of a times-series experiment that measured the statistical impacts that sometimes occur during survey redesigns.The Labour Force Survey(LFS)of the Australian Bureau of Statistics(ABS)was used as a case study.In the present study:(1)A large-scale field experiment was designed and conducted that allowed the outgoing and the incoming surveys to run in parallel for some periods to measure the impacts of any changes to the survey process;and(2)The precision of the impact measurement was continuously improved while the new survey design was being implemented.The state space modelling(SSM)technique was adopted as the main approach,as it provides an efficient impact measurement.This approach enabled sampling error structure to be incorporated in the time-series intervention analysis.The approach was also able to be extended to take advantage of the availability of other related data sources(e.g.,the data obtained from the parallel data collection process)to improve the efficiency and accuracy of the impact measurement.As stated above,the LFS was used as a case study;however,the models and methods developed in this study could be extended to other surveys.