scholarly journals Effectiveness and Forecasting of Interest Rate Reversal BI 7-Day Repo Rate in Indonesia: Lower Bound on Monetary Policy?

2018 ◽  
Vol 9 (1) ◽  
pp. 171-180
Author(s):  
I Gede Sanica ◽  
I Ketut Nurcita ◽  
I Made Mastra ◽  
Desak Made Sukarnasih
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Implementation Of Monetary Policy ◽  
Repo Rate ◽  
The Right ◽  
The Impact

AbstractThis study aims to analyze effectivity and forecast of interest rate BI 7-Day Repo Rate as policy reference in the implementation of monetary policy. The method was used in this study contains Vector Autoregression (VAR) to estimate effectivity of BI 7-Day Repo Rate and Autoregressive Integrated Moving Average (ARIMA) to forecast of BI 7-Day Repo Rate. Period of observation in this study used time series data during 2016.4 until 2017.6. The result of this research shows that the transformation of the BI Rate to BI 7-Day Repo Rate is the right step in the monetary policy operation in the effort to reach deepening of the financial market and strengthen the interbank money market structure so that it will decrease loan interest rate and encourage credit growth. The effectiveness of the use of BI 7 Day-Repo Rate on price stability is indicated by the positive relationship between the benchmark interest rate and inflation compared to the BI Rate. The impact of BI 7-Day Repo Rate on economic growth that tends to be positive. Forecasting the use of BI 7-Day Repo Rate shows good results with declining value levels, so this will encourage deepening the financial markets.

scholarly journals Pemodelan Dan Peramalan Indeks Harga Konsumen (IHK) Kota Sampit Dengan Seasonal Arima (Sarima)

2020 ◽  
Vol 6 (2) ◽  
pp. 61-72
Author(s):  
Agustina Elisa Dyah Purwandari
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Consumer Price Index ◽  
Real Income ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Goods And Services ◽  
Seasonal Factors ◽  
The Government ◽  
The Right

AbstractSampit is one of 82 cities in Indonesia which calculate inflation. Inflation is an increase of prices on goods and services in a region. Government’s control is very important because inflation relates to the real income, the exchange rate, import exports, and so on. Inflation is based on the Consumer Price Index (CPI). Because of CPI is a monthly data prices, it is highly influenced by seasonal factors. Therefore, CPI data modelling is needed because it helps the government to make appropriate policies. Method that can be used for time series data with seasonal influences is Seasonal Autoregressive Integrated Moving Average (SARIMA). The results of the study show that the right model for Sampit’s CPI is SARIMA with the order p = 1, d = 1, P = 1, D = 1, Q = 1, s = 12. It is the best model that can built and be used for forecasting because with 95 percent of confidence, the model explains 87.23 percent of data. Forecasting in this research use interval analysis and found that January 2020 may be the highest increase of CPI (inflation) in 2020. Keywords: CPI, Inflation, SARIMA

scholarly journals Implication of Monetary Policy Rate and Interest Rate on Exchange Rate in Nigeria: 1981-2017

2019 ◽  
pp. 1-11
Author(s):  
Nnamani, Vincent ◽  
Anyanwaokoro, Mike
Keyword(s):  
Monetary Policy ◽  
Exchange Rate ◽  
Interest Rate ◽  
Time Series Data ◽  
Unit Root Test ◽  
Series Data ◽  
Statistical Tool ◽  
Long Run ◽  
The Exchange Rate ◽  
The Impact

The study investigated the implication of monetary policy rate on the exchange rate and interest rate in Nigeria, 1981-2017. Because of the above-stated problems, the specific objectives are to: Investigate the effect of monetary policy rate on the exchange rate in Nigeria, determine the effect of the monetary policy rate on interest rate in Nigeria. The analysis of error correction and autoregressive lags fully covers both long-run and short-run relationships of the variable under study. The statistical tool of analysis employed in the study is Autoregressive Distributed Lags (ARDL) and Philips Peron method of stationary testing and structural breakpoint unit root test., these methods were employed to check the stationarity and breakpoint analysis of the time series data employed in this study. The study observed that monetary policy rate has a positive and significant effect on the exchange rate in Nigeria. It was also observed that the monetary policy rate has a positive and significant effect on the interest rate in Nigeria. Overall, our results indicated that the impact of monetary policy on the exchange rate was significant. There was a positive and significant relationship between monetary policy variables and exchange rate. The conclusion that is drawn from our results is that monetary policy remains an effective and potent tool for ensuring a stable exchange rate in Nigeria. The study recommended that monetary policy should be used to create a favourable investment environment by facilitating the emergence of market-based interest rate and exchange rate regimes which could attract domestic and foreign investments. Second; the Central bank of Nigeria (CBN) need to avoid ordination and balance between monetary and fiscal policies to ensure the smooth realization of monetary policy goals. Policy inconsistency or summersault to determine its policy impact before contemplating a change. Finally, there should be a coo.

Impact of Monetary Policy on Small Scale Enterprises Financing in Nigeria

2018 ◽  
Vol 1 (2) ◽  
pp. 1-12
Author(s):  
Ebire Kolawole
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Time Series Data ◽  
Negative Impact ◽  
Unit Root Test ◽  
Series Data ◽  
Small Scale ◽  
Correction Model ◽  
Monetary Authorities ◽  
The Impact

Small and Medium Scale Enterprises play vital roles in the economy which are usually instrumental in achieving macroeconomic goals. This has attracted the attention of monetary authorities to institute policiesto boostconducive environment for SMEs to thrive. This study therefore empirically investigates the impact of monetary policy on SMEs financing in Nigeria spanning from the first quarter of 1992 to the last quarter of 2016. The time series data were subjected to unit root test to ascertain the stationarity of the variables and thereafter, cointegration and Error Correction Model (ECM) technique were used for the analysis. The residuals of the analysis were further subjected to various diagnostics tests. The result revealed that interest rate has a positive and significant impact on the SMEs financing in Nigeria. On the other hand, inflation rate was found to have a significant but negative impact on SMEs financing in Nigeria. Money supply and exchange rate were found to be insignificant in impactingSMEs financing. Based on this finding, the study recommends that, monetary authorities should give special attention to SMEs in specific sectors by creating special windows through various financial institutions to grant low interest rate so as to grant SMEs access to funds.This will boost business growth and consequently achieve macroeconomic goals.

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

A RESEARCH PAPER ON MONETARY POLICY AND INFRASTRUCTURAL GROWTH: FURTHER EVIDENCE FROM NIGERIA.

2020 ◽  
Vol 7 (11) ◽  
pp. 467-484
Author(s):  
Sunday Osahon Igbinedion
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Affordable Housing ◽  
Inflation Rate ◽  
Time Series Data ◽  
Capital Expenditure ◽  
Series Data ◽  
Single Digit ◽  
Key Factor ◽  
The Face

Extant economic literature has acknowledged monetary policy as a key factor influencing infrastructural growth through different channels, such as affordable housing and efficient transportation, among others. However, in recent times, the Nigeria’s experience suggests a conflicting position on the above supposition. It is against this backdrop that this study set out to investigate the nexus between monetary policy and infrastructural growth within the Nigerian context, time series data from 1981 to 2018, and utilizing the Fully Modified Least Squares (FMOLS) estimation technique. The results show that both real interest rate and inflation rate exerted negative and statistically significant impact on infrastructural growth, while federal government capital expenditure and net official development assistance impacted positively on the level of infrastructural growth in the period under assessment. In the light of the study’s findings, the study recommends that, the monetary authority should carefully review existing lending interest rate downward to a single digit that will be investment driven particularly in the face of current global economic uncertainties occasioned by the COVID-19 pandemic that has led to the collapse of many economies across the world.

scholarly journals Pemodelan Harga Beras di Pulau Sumatera dengan Menggunakan Model Generalized Space Time ARIMA

2018 ◽  
Vol 2 (2) ◽  
pp. 49-57
Author(s):  
Dwi Yulianti ◽  
I Made Sumertajaya ◽  
Itasia Dina Sulvianti
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Space Time ◽  
Series Data ◽  
Model Parameters ◽  
Autoregressive Integrated Moving Average ◽  
Sumatra Island ◽  
Rice Price ◽  
Multiple Variables

Generalized space time autoregressive integrated  moving average (GSTARIMA) model is a time series model of multiple variables with spatial and time linkages (space time). GSTARIMA model is an extension of the space time autoregressive integrated moving average (STARIMA) model with the assumption that each location has unique model parameters, thus GSTARIMA model is more flexible than STARIMA model. The purposes of this research are to determine the best model and predict the time series data of rice price on all provincial capitals of Sumatra island using GSTARIMA model. This research used weekly data of rice price on all provincial capitals of Sumatra island from January 2010 to December 2017. The spatial weights used in this research are the inverse distance and queen contiguity. The modeling result shows that the best model is GSTARIMA (1,1,0) with queen contiguity weighted matrix and has the smallest MAPE value of 1.17817 %.

scholarly journals Impact of Macro-Economic Variable on Pakistan Stock Exchange KSE-100 Index

2021 ◽  
Vol 2 (3) ◽  
pp. 17-23
Author(s):  
Muhammad Faisal Hassan ◽  
Hashim Bin Jusoh ◽  
Sajjad Khan ◽  
Fahad Ali Khan ◽  
Muhammad Naseem ◽  
...  
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Time Series Data ◽  
Positive Impact ◽  
Stock Exchange ◽  
Ordinary Least Squares ◽  
Series Data ◽  
Short Run ◽  
The Impact ◽  
Adf Test

The researcher investigates the Impact of inflation, exchange rate and interest rate on Pakistan stock Exchange performance KSE-100 index by using monthly time series data which covers the period of 2013 to 2020. The econometrics techniques which are employed includes ADF test, Ordinary Least squares regression Model, testing for Multi-collinearity, Residual analysis serial correlation, testing for co-integration, Error correction model (ECM), variance decomposition (VAR) and Pair wise granger causality test. The results indicate that there is positive impact of exchange rate on PSX 100 index and the impact of inflation and interest rate is fond negative but inflation have insignificant relationship with PSX 100 index and the other two relationships are found significant. From the ECM result it is found that in short run 20% of the variation in dependent variable is due to inflation, exchange rate and interest rate and 80% variation is unexplained in short run. Form the results of VAR test it is concluded that exchange rate 1.67, inflation 14.25%, and interest rate 3.90% variation cause in PSX 100 index performance due to these three independent variables.

scholarly journals Comparison of the impact of lending and inflation rates on economic growth in Vietnam and China

2020 ◽  
Vol 15 (4) ◽  
pp. 193-203
Author(s):  
Doan Van Dinh
Keyword(s):  
Economic Growth ◽  
Monetary Policy ◽  
Time Series Data ◽  
Close Correlation ◽  
Least Square ◽  
Series Data ◽  
Lending Rate ◽  
Lending Rates ◽  
The Impact

Inflation and lending rates are two important macroeconomic indicators as they affect economic growth. The correlation between the inflation rate and the lending rate in Vietnam and China is analyzed to determine whether the lending rate causes inflation or not. An ordinary least square model (OLS) and a unit root test are applied to check the correlation and cointegration related to the inflation and lending rates to avoid spurious regression. The research time series data were collected from 1996 to 2017. The correlation of Vietnam’s variables is 56%, the correlation of China’s variables is 55%, which is a close correlation. The empirical cointegration test results for Vietnam and China are suitable for two research models. The relationship between these two indicators influences each other. In the short term, inflation stimulates economic growth through loose monetary policy through the lending rate. However, in the long term, if the money supply increases continuously, inflation will slow economic growth and increase bad debt. The empirical results are to make accurate forecasts and determine monetary policy for micro-managers who set the goal of sustainable economic growth and have a strategy for economic development in the short and long term.

scholarly journals PERAMALAN JUMLAH PENUMPANG PESAWAT TERBANG DI PINTU KEDATANGAN BANDAR UDARA INTERNASIONAL PATTIMURA AMBON DENGAN MENGGUNAKAN METODE ARIMA BOX-JENKINS

2019 ◽  
Vol 13 (3) ◽  
pp. 135-144
Author(s):  
Sasmita Hayoto ◽  
Yopi Andry Lesnussa ◽  
Henry W. M. Patty ◽  
Ronald John Djami
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Significant Parameter ◽  
International Airport ◽  
Forecast Time

The Autoregressive Integrated Moving Average (ARIMA) model is often used to forecast time series data. In the era of globalization, rapidly progressing times, one of them in the field of transportation. The aircraft is one of the transportation that the residents can use to support their activities, both in business and tourism. The objective of the research is to know the forecasting of the number of passengers of airplanes at the arrival gate of Pattimura Ambon International Airport using ARIMA Box-Jenkins method. The best model selection is ARIMA (0, 1, 3) because it has significant parameter value and MSE value is smaller.

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