scholarly journals Forecast China’s monthly urban fixed asset investment based on the ARIMA model

2021 ◽  
Vol 290 ◽  
pp. 02017
Author(s):  
Jiangle Yuan ◽  
Yaning Li ◽  
Yayu Li
Keyword(s):  
Arima Model ◽  
Seasonal Effect ◽  
Upward Trend ◽  
Fixed Asset ◽  
Monthly Data ◽  
Urbanization Level ◽  
Fluctuation Range ◽  
The Future ◽  
Fitting Model ◽  
Fixed Asset Investment

In recent years, as China’s urbanization level has risen, China’s urban fixed asset investment has also been rising. Judging from monthly data, China’s urban fixed asset investment has shown a volatile upward trend, with an obvious 11-month cycle. And in each cycle, the fluctuation range of the investment amount is getting larger. This paper uses an ARIMA model with an additive seasonal effect to fit the monthly urban fixed asset investment sequence and predict the future investment. In the end, this paper established a fitting model for China’s urban fixed asset investment, and obtained a good forecasting effect.

scholarly journals Estimationof a Tax Compliance Index with Specific Application to Pay-As-You-Earn

2021 ◽  
Vol 04 (02) ◽  
Author(s):  
Cathrine T Koloane ◽  
Keyword(s):  
Time Series ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Composite Index ◽  
Tax Compliance ◽  
Upward Trend ◽  
Monthly Data ◽  
The World ◽  
The Future ◽  
Components Analysis

This article provides a composite index for Pay-As-You-Earn (PAYE)tax using Principal Components Analysis (PCA). The study uses time series from April 2012 to March 2020 (using monthly data) for the ratios derived from the four compliancemeasures namely, payments on time, registration on time, filing on time and accurate declarations. The index is computed using the weights of the four derived principal components. According to the model results, the PAYE tax compliance index averages around 75.0% for the period, with the lowest value of 72.3% in 2013/14 and the highestvalue of 77.1% achieved in 2018/19. There is a clear upward trend, indicating improving levels of compliance in PAYE. Similarly, setting the baseline index of 100 i.e. assuming 100% compliance for 2012/13, results in PAYE tax compliance index averaging around 101.6% for the period, with the lowest value of 97.72% in 2013/14 and the highest value of 104.26% achieved in 2018/19. The study recommends this methodology to be applied to all the tax products and that the overall tax compliance index be computed. This will assist tax authorities all over the world to actively monitor tax compliance levels and institute timeous corrective measures in order to address non-compliance and ultimately maximise PAYE revenue collections. Moreover, this study also serve as a base for many of the future tax compliance indices studies.

scholarly journals Developing Monthly Hydrometeorological Timeseries Forecasts To Reservoir Operation in a Transboundary River Catchment

2021 ◽  
Author(s):  
Hammad Ur Rehman ◽  
Ijaz Ahmad ◽  
Faraz ul Haq ◽  
Waseem Muhammad ◽  
Jinxin Zhang
Keyword(s):  
Water Availability ◽  
Reservoir Operation ◽  
Moving Average ◽  
Arima Model ◽  
Water Shortage ◽  
Vital Role ◽  
Seasonal Effect ◽  
The Future ◽  
Moving Average Model ◽  
Average Water

Abstract Forecasting of hydrometeorological timeseries data play vital role in flood forecasting and predicting the future water availability for various uses such as irrigation, hydropower generation, industrial, domestic, etc. Therefore, present study aims to forecast the hydrometeorological timeseries data, i.e., river inflows, precipitation, and evaporation for the improved reservoir operation of a transboundary Mangla catchment by using ARIMA (auto-regressive integrated moving average) model. Prior to applying the ARIMA model, stationarity of hydrometeorological timeseries data was checked. Moreover, ACF and PACF of timeseries were determined to determine the “p” and “q” terms of the ARIMA model. The best fitted structure of ARIMA model was used by evaluating the R2, MAE and RMSE to forecast the hydrometeorological timeseries. The seasonal ARIMA structure of (1,0,0)(2,1,2)12 was found best fitted for the inflow timeseries whereas ARIMA structures of (14,1,15) and (9,1,19) were considered for forecasting the precipitation and evaporation timeseries, respectively. An average water shortage of 14% was detected by using these forecasted hydrometeorological timeseries in the reservoir operation during the period of 2016–2030. It was also observed that inflows into Mangla reservoir have seasonal effect more prominent compared to evaporation and precipitation. However, variations in the precipitation timeseries were found less smooth than the inflows timeseries. It is believed that the results of this study may support reservoir operators and managers for developing efficient real-rime reservoir operation policies and strategies based on the variations in the future water availability.

scholarly journals A statistical study of COVID-19 pandemic in Egypt

2021 ◽  
Vol 54 (1) ◽  
pp. 233-244
Author(s):  
Taha Radwan
Keyword(s):  
Linear Relationship ◽  
Statistical Study ◽  
Moving Average ◽  
Arima Model ◽  
Decision Makers ◽  
Accurate Data ◽  
Autoregressive Integrated Moving Average ◽  
The World ◽  
The Future

Abstract The spread of the COVID-19 started in Wuhan on December 31, 2019, and a powerful outbreak of the disease occurred there. According to the latest data, more than 165 million cases of COVID-19 infection have been detected in the world (last update May 19, 2021). In this paper, we propose a statistical study of COVID-19 pandemic in Egypt. This study will help us to understand and study the evolution of this pandemic. Moreover, documenting of accurate data and taken policies in Egypt can help other countries to deal with this epidemic, and it will also be useful in the event that other similar viruses emerge in the future. We will apply a widely used model in order to predict the number of COVID-19 cases in the coming period, which is the autoregressive integrated moving average (ARIMA) model. This model depicts the present behaviour of variables through linear relationship with their past values. The expected results will enable us to provide appropriate advice to decision-makers in Egypt on how to deal with this epidemic.

Influencing Factors and Decoupling Analysis of Carbon Emissions in China's Manufacturing Industry

2021 ◽  
Author(s):  
baoling jin ◽  
ying Han
Keyword(s):  
Carbon Emissions ◽  
Manufacturing Industry ◽  
Energy Intensity ◽  
Value Added ◽  
Carbon Intensity ◽  
Energy Structure ◽  
Investment Efficiency ◽  
Fixed Asset ◽  
Carbon Productivity ◽  
Fixed Asset Investment

Abstract The manufacturing industry directly reflects national productivity, and it is also an industry with serious carbon emissions, which has attracted wide attention. This study decomposes the influential factors on carbon emissions in China’s manufacturing industry from 1995 to 2018 into industry value added (IVA), energy consumption (E), fixed asset investment (FAI), carbon productivity (CP), energy structure (EC), energy intensity (EI), investment carbon intensity (ICI) and investment efficiency (IE) by Generalized Divisia Index Model (GDIM). The decoupling analysis is carried out to investigate the decoupling states of the manufacturing industry under the pressure of "low carbon" and "economy.” Considering the technological heterogeneity, we study the influential factors and decoupling status of the light industry and the heavy industry. The results show that: (1) Carbon emissions of the manufacturing industry present an upward trend, and the heavy industry is the main contributor. (2) Fixed asset investment (FAI), industry value added (IVA) are the driving forces of carbon emissions. Investment carbon intensity (ICI), carbon productivity (CP), investment efficiency (IE), and energy intensity (EI) have inhibitory effects. The impact of the energy consumption (E) and energy structure (EC) are fluctuating. (3) The decoupling state of the manufacturing industry has improved. Fixed asset investment (FAI), industry value added (IVA) hinder the decoupling; carbon productivity (CP), investment carbon intensity (ICI), investment efficiency (IE), and energy intensity (EI) promote the decoupling.

scholarly journals PENGARUH KINERJA KEUANGAN TERHADAP INVESTASI AKTIVA TETAP PT. SEPATU BATA, Tbk DI BURSA EFEK INDONESIA

2018 ◽  
Author(s):  
STIM Sukma
Keyword(s):  
Financial Performance ◽  
Hypothesis Test ◽  
Data Use ◽  
Coefficient Of Determination ◽  
Return On Assets ◽  
Fixed Asset ◽  
Processing Data ◽  
Regression Test ◽  
Simple Regression ◽  
Fixed Asset Investment

The purpose of this research is to determine impact of the financial performance of fixed asset investment. The data analyzedby simple regression test with hypothesis test, using test the coefficient of determination (R2), partial test (t test), while processing data use SPSS. The results showed that the coefficient of determination (R2) variable Return On Assets (ROA) able to explain the variations that occur in fixed asset investment, but it is partially noeffect and significant fixed asset investment.

scholarly journals Autoregressive Integrated Moving Average (ARIMA) Sebagai Model Peramalan Kasus Demam Berdarah Dengue

2020 ◽  
Vol 10 (2) ◽  
pp. 76-80
Author(s):  
Roro Kushartanti ◽  
Maulina Latifah
Keyword(s):  
Community Health ◽  
Health Center ◽  
Moving Average ◽  
Arima Model ◽  
Community Health Center ◽  
Percentage Error ◽  
Dengue Case ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method

ARIMA is a forecasting method time series that does not require a specific data pattern. This study aims to analyze the forecasting of Semarang City DHF cases specifically in the Rowosari Community Health Center. The study used monthly data on DHF cases in the Rowosari Community Health Center in 2016, 2017, and 2019 as many as 36 dengue case data. The best ARIMA model for forecasting is a model that meets the requirements for parameter significance, white noise and has the MAPE (Mean Absolute Percentage Error Smallest) value. The results of the analysis show that the best model for predicting the number of dengue cases in the Rowosari Public Health Center Semarang is the ARIMA model (1,0,0) with a MAPE value of 43.98% and a significance coefficient of 0.353, meaning that this model is suitable and feasible to be used as a forecasting model. DHF cases in the Rowosari Community Health Center in Semarang City.

scholarly journals CASH FLOW, DEBT ISSUANCE, EQUITY ISSUANCE, AND FIXED ASSET INVESTMENT ON MANUFACTURE COMPANY 2010-2014CASH FLOW, DEBT ISSUANCE, EQUITY ISSUANCE, AND FIXED ASSET INVESTMENT ON MANUFACTURE COMPANY 2010-2014

2020 ◽  
Vol 4 (1) ◽  
pp. 11-21
Author(s):  
Ritma Palupi
Keyword(s):  
Cash Flow ◽  
Equity Issuance ◽  
Pecking Order ◽  
Fixed Asset ◽  
Financing Decision ◽  
Manufacturing Company ◽  
Debt Issuance ◽  
Manufacture Company ◽  
Internal Fund ◽  
Fixed Asset Investment

Matters about financing decision based on pecking order theory’s hierarchy are currently appealing. This research strives to discover how corporate’s fixed asset investment reacts to cash flow, debt issuance, and equity issuance. Researcher uses 75 samples of manufacturing company in Indonesia during 2010-2014 period with 199 firm-year observation. Multiple linear regression’s result indicates that cash flow and debt issuance have influence towards corporate’s fixed asset investment, but the equity issuance have no influence towards corporate’s fixed asset investment. Also regression coefficient exhibits that manufacturing company in Indonesia follows pecking order theory’s hierarchy.  Cash flow’s influence towards fixed asset investment is more significant than debt issuance’s, and debt issuance’s influence is stronger than equity issuance. This points out that corporate’s fixed asset investment is more sensitive towards cash flow (internal fund) compared to debt issuance (external fund), and so is debt issuance is more sensitive compared to equity issuance. With all that in mind, it is concluded that manufacturing company in Indonesia follows pecking order theory in terms of financing decision, which uses internal fund at first then started to use external fund if deemed necessary. 

scholarly journals Investment Of Fixed Equipment Assets On Improving Profitability (case study cv.Parahyangan Express BranchKarawang)

2020 ◽  
Vol 17 (1) ◽  
pp. 30-42
Author(s):  
Sri Suartini ◽  
Dian Hakip Nurdiansyah ◽  
Sheli Rosdayanti
Keyword(s):  
Primary Data ◽  
Test Results ◽  
Fixed Asset ◽  
Financial Report ◽  
Verification Method ◽  
Average Value ◽  
Fixed Assets ◽  
Data Source ◽  
Fixed Asset Investment

The purpose of this study to determine how much influence of fixed asset investment vehicles against profitability CV. Parahyangan Express Karawang Branch. This research uses a descriptive verification method with a primary data source that is a financial report CV. Parahyangan Express Karawang Branch period 2007 to 2016. The result of this research is r average investment value CV. Parahyangan Express in the period 2007 until 2016 tends to decrease, the average value of profitability CV. Parahyang n Express in the period 2007 to 2016 tends to decline. Based on test results t comparison t arithmetic with t table showing 2.840> 2.093 t count more than t table. vehicle fixed asset investment has a significant effect on profitability in a CV. Parahyangan Express. The percentage of influence of fixed asset investment of 30% means 30 % development of profitability CV. Parahyangan Express is influenced by in-kind fixed assets while 70 % is influenced by other factors not examined in this study.

Probability of loss reversal in Australia

2016 ◽  
Vol 42 (4) ◽  
pp. 560-582 ◽  
Author(s):  
Hai Wu
Keyword(s):  
Stock Returns ◽  
Mineral Exploration ◽  
Positive Association ◽  
Firm Valuation ◽  
Fixed Asset ◽  
Abnormal Stock Returns ◽  
Future Loss ◽  
High Level ◽  
Loss Firms ◽  
Fixed Asset Investment

Investors in loss firms assess the likelihood of these firms reverting to profit (i.e. loss reversal). This research examines the factors useful for predicting future loss reversal in the Australian market. Specifically, it focuses on loss firms’ investment activities, in addition to factors examined in previous US literature. The results show that when the level of investment in specialised assets, such as mineral exploration and research and development, is high relative to fixed-asset investment, future loss reversals are less likely to occur. In contrast, a high level of fixed-asset investment increases the likelihood of future loss reversal. These results hold implications for loss-firm valuation. Further analysis documents a positive association between the ex-ante probability of loss reversal and future abnormal stock returns for loss firms with a weak information environment. Investors in these loss firms could benefit from the findings of this study.

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