scholarly journals Forecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Models

2021 ◽  
Vol 2 (6) ◽  
pp. 50-63
Author(s):  
Teddy Mutugi Wanjuki ◽  
Adolphus Wagala ◽  
Dennis K. Muriithi
Keyword(s):  
Monetary Policy ◽  
Fiscal Policy ◽  
Price Index ◽  
Moving Average ◽  
Estimation Method ◽  
Series Data ◽  
Policy Objective ◽  
Autoregressive Integrated Moving Average ◽  
Food And Beverages ◽  
Sarima Models

Price stability is the primary monetary policy objective in any economy since it protects the interests of both consumers and producers. As a result, forecasting is a common practice and a vital aspect of monetary policymaking. Future predictions guide monetary and fiscal policy tools that that be used to stabilize commodity prices. As a result, developing an accurate and precise forecasting model is critical. The current study fitted and forecasted the food and beverages price index (FBPI) in Kenya using seasonal autoregressive integrated moving average (SARIMA) models. Unlike other ARIMA models like the autoregressive (AR), Moving Average (MA), and non-seasonal ARMA models, the SARIMA model accounts for the seasonal component in a given time series data better forecasts. The study relied on secondary data obtained from the KNBS website on monthly food and beverage price index in Kenya from January 1991 to February 2020. R-statistical software was used to analyze the data. The parameter estimation was done using the Maximum Likelihood Estimation method. Competing SARIMA models were compared using the Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE),.and Mean Absolute Percentage Error (MAPE). A first-order differenced SARIMA (1,1,1) (0,1,1)12 minimized these model evaluation criteria (AIC = 1818.15, BIC =1833.40). The forecasting ability evaluation statistics MAE = 2.00%, MAPE = 1.62% and MASE = 0.87%. The 24-step ahead forecasts showed that the FPBI is unstable with an overall increasing trend. Therefore, the monetary policy committee ought to control inflation through monetary or fiscal policy, strengthening food security and trade liberalization.

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 Peramalan Indeks Harga Konsumen dengan Metode Singular Spectral Analysis (SSA) dan Seasonal Autoregressive Integrated Moving Average (SARIMA)

2017 ◽  
Vol 3 (2) ◽  
pp. 74-82
Author(s):  
Deltha Airuzsh Lubis ◽  
Muhamad Budiman Johra ◽  
Gumgum Darmawan
Keyword(s):  
Spectral Analysis ◽  
Price Index ◽  
Moving Average ◽  
Consumer Price Index ◽  
Parametric Model ◽  
Seasonal Patterns ◽  
Autoregressive Integrated Moving Average ◽  
Goods And Services ◽  
High Inflation ◽  
Sarima Models

Consumer Price Index (CPI) are the indicators used to measure the inflation and deflation of a group of goods and services in general. Forecasting CPI to be important as early detection in facing price hikes. This study uses the SSA and SARIMA. SARIMA a parametric model that requires various assumptions while SSA is a nonparametric technique that is free from a variety of assumptions, but both methods require seasonal patterns in the data. Based on the research results, methods of SSA with length window(L) of 24 and a grouping of 4 (1 group of seasonal and 3 groups of trends) and SARIMA models of order (0,1,1), (0,1,1) 6 is the most accurate and reliable models in forecasting CPI to the value Padang Sidempuan City. Forecasting CPI Padang Sidempuan City for the next 5 months with SSA method and SARIMA (0,1,1), (0,1,1) 6 shows the pattern of a trend is likely to increase but forecasting the 5th month with SSA method showed a surge in the value of CPI high or high inflation will occur.

scholarly journals ANALISIS KAUSALITAS KEBIJAKAN FISKAL EKSPANSIF, KEBIJAKAN MONETER EKSPANSIF DAN PERTUMBUHAN EKONOMI DI INDONESIA

2020 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Fadhliah Yuniwinsah ◽  
Ali Anis
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Monetary Policy ◽  
Fiscal Policy ◽  
Time Series Data ◽  
Vector Autoregression ◽  
Series Data ◽  
Vector Autoregression Model ◽  
Autoregression Model

This study examined the causality between expansionary fiscal policy, expansionary monetary policy and economic growth in Indonesia’s using a time series data with vector autoregression model (VAR) in the period of 1969-2018. The results of this study showed that are there is no causality between expansionary fiscal policy and expansionary monetary policy but there one-way relationship between them, it is the expansionary monetary policy gives influence to expansionary fiscal policy. There is no causality between expansionary fiscal policy and economic growth but there one-way relationship between them, It is economic growth gives influence to expansionary fiscal policy. And there is no causality between expansionary monetary policy and economic growth but there one-way relationship between them, it is economic growth gives influence to expansionary monetary policy.

scholarly journals An econometric analysis of the effectiveness of fiscal and monetary policies in India

2021 ◽  
Author(s):  
Anand Nadar
Keyword(s):  
Economic Growth ◽  
Monetary Policy ◽  
Fiscal Policy ◽  
Money Supply ◽  
Series Data ◽  
Monetary Policies ◽  
Indian Economy ◽  
Long Run ◽  
Short Run ◽  
Bound Test

This study investigatesthe effectiveness of fiscal policy and monetary policy in India. We collected thetime series data for India ranging from 1960 to 2019 from World Development Indicator (WDI). Weapplied the bound test co-integration approach to check the long-run relationship between fiscalpolicy, monetary policy, and economic growth in the context of Indian economy. The short-run andlong-run effects of fiscal policy and monetary policy have been estimated using ARDL models. Theresults showed that there is a long-run relationship between fiscal and monetary policies witheconomic growth. The estimated short-run coefficients indicated that a few immediate short runimpacts of fiscal and monetary policies are insignificant. However, the short-run impacts becomesignificant as time passes. The long-run results suggested that the long-run impact of both fiscal andmonetary policies on economic growth are positive and significant. More specifically, the GDP levelincreases if the money supply and government expenditure increase (Expansionary fiscal andmonetary policies). On the other hand, the GDP level decreasesif the money supply and governmentexpenditure decrease (contractionary fiscal and monetary policies). Therefore, this studyrecommends to use expansionary policies to spur the Indian economy.

FINANCIAL STABILITY AND MONETARY POLICY REACTION FUNCTION FOR SOUTH ASIAN COUNTRIES: AN ECONOMETRIC APPROACH

2019 ◽  
pp. 1-30
Author(s):  
SAMIA NASREEN ◽  
SOFIA ANWAR
Keyword(s):  
Monetary Policy ◽  
South Asian ◽  
Financial Stability ◽  
Central Banks ◽  
Series Data ◽  
Reaction Function ◽  
Asian Countries ◽  
Policy Objective ◽  
Monetary Policy Reaction Function ◽  
Policy Reaction Function

This study empirically investigates a monetary policy reaction function for South Asian economies by incorporating financial stability as an additional policy objective in the central bank’s loss function. Empirical results are estimated by applying auto-regressive distributed lag (ARDL) approach to cointegration and vector autoregressive (VAR) approach using time-series data of five South Asian countries, namely, Pakistan, India, Bangladesh, Nepal and Sri Lanka. Estimated results indicate that monetary policy significantly reacts to the level of financial stability in all countries. The result further suggests that central banks would tighten monetary policy if output gap widens and exchange rate depreciate. In addition, central banks of Pakistan and India do not respond significantly to inflation gap.

Using AutoRegressive Integrated Moving Average and Gaussian Processes with LSTM neural networks to predict discrete geomagnetic signals

2020 ◽  
Author(s):  
Laurentiu Asimopolos ◽  
Alexandru Stanciu ◽  
Natalia-Silvia Asimopolos ◽  
Bogdan Balea ◽  
Andreea Dinu ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Moving Average ◽  
Time Series Forecasting ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Frequency Information ◽  
Probabilistic Forecasts ◽  
Geomagnetic Data ◽  
Gp Model

<p>In this paper, we present the results obtained for the geomagnetic data acquired at the Surlari Observatory, located about 30 Km North of Bucharest - Romania. The observatory database contains records from the last seven solar cycles, with different sampling rates.</p><p>We used AR, MA, ARMA and ARIMA (AutoRegressive Integrated Moving Average) type models for time series forecasting and phenomenological extrapolation. ARIMA model is a generalization of an autoregressive moving average (ARMA) model, fitted to time series data to predict future points in the series</p><p>We made spectral analysis using Fourier Transform, that gives us a relevant picture of the frequency spectrum of the signal component, but without locating it in time, while the wavelet analysis provides us with information regarding the time of occurrence of these frequencies. </p><p>Wavelet allows local analysis of magnetic field components through variable frequency windows. Windows with longer time intervals allow us to extract low-frequency information, medium-sized intervals of different sizes lead to medium-frequency information extraction, and very narrow windows highlight the high-frequencies or details of the analysed signals.</p><p>We extend the study of geomagnetic data analysis and predictive modelling by implementing a Long Short-Term Memory (LSTM) recurrent neural network that is capable of modelling long-term dependencies and is suitable for time series forecasting. This method includes a Gaussian process (GP) model in order to obtain probabilistic forecasts based on the LSTM outputs. </p><p>The evaluation of the proposed hybrid model is conducted using the Receiver Operating Characteristic (ROC) Curve that provides a probabilistic forecast of geomagnetic storm events. </p><p>In addition, reliability diagrams are provided in order to support the analysis of the probabilistic forecasting models.</p><p>The implementation of the solution for predicting certain geomagnetic parameters is implemented in the MATLAB language, using the Toolbox Deep Learning Toolbox, which provides a framework for the design and implementation of deep learning models.</p><p>Also, in addition to using the MATLAB environment, the solution can be accessed, modified, or improved in the Jupyter Notebook computing environment.</p>

Impact of Fiscal Policy on Agricultural Output in Nigeria

2019 ◽  
Vol 9 (7) ◽  
pp. 1428 ◽  
Author(s):  
Adedoyin Isola LAWAL ◽  
Ernest Onyebuchi FIDELIS ◽  
Abiola Ayoopo BABAJIDE ◽  
Barnabas O. OBASAJU ◽  
Oluwatoyese OYETADE ◽  
...  
Keyword(s):  
Fiscal Policy ◽  
Agricultural Sector ◽  
Time Series Data ◽  
Negative Impact ◽  
Capital Expenditure ◽  
Research Work ◽  
Estimation Method ◽  
Series Data ◽  
Agricultural Output ◽  
The Impact

This study examines the impact of fiscal policy on agricultural output in Nigeria using the most recent official data. The metrics for fiscal policy is government capital expenditure and custom duties on fertilizer. The study used annual time series data obtained from CBN annual statistical bulletin, NCS, and FIRS which was found to be stationary at the order of I(1) and I(0). The order of unit root test led to the use of ARDL estimation method employed in the empirical analysis of this research work. The study found evidence of both short and long run relationship between the variables (VAO, GEX, IDMF, and ACGSF) using both Johansen co-integration and ARDL Bounds test. Although government expenditure (GEX) to agricultural sector was found to be statistically insignificant which recommend that government should increase agriculture capital expenditure to ensure that its contribution is significant. Consequently, custom duties on fertilizer (IDMF) was found to be negatively signed and significant indicating a negative impact on agricultural output. This demands that the policy makers should be prudent in the use of fiscal policy instrument in achieving its desired objective.

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 Forecasting wholesale price of coarse rice in Bangladesh: A seasonal autoregressive integrated moving average approach

2014 ◽  
Vol 11 (2) ◽  
pp. 271-276
Author(s):  
MF Hassan ◽  
MA Islam ◽  
MF Imam ◽  
SM Sayem
Keyword(s):  
Moving Average ◽  
Wholesale Price ◽  
Information Criteria ◽  
Suitable Model ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Lag Times ◽  
Predicted Values ◽  
Absolute Percent Error ◽  
Sarima Models

This article attempts to develop the model and to forecast the wholesale price of coarse rice in Bangladesh. Seasonal Autoregressive Integrated Moving Average (SARIMA) models have been developed on the monthly data collected from July 1975 to December 2011and validated using the data from December 2010 to December 2011. The results showed that the predicted values were consistent with the upturns and downturns of the observed series. The model with non seasonal autoregressive 1, difference 1 and moving average 1 and seasonal difference 1 and moving average 1 that is SARIMA (1,1,1)(0,1,1)12 model has been found as the most suitable model with least Root Mean Square Error (RMSE) of 61.657, Normalised Bayesian Information Criteria (BIC) of 8.300 and Mean Absolute Percent Error (MAPE) of 3.906. The model was further validated by Ljung-Box test (Q18=17.394 and p>.20) with no significant autocorrelation between residuals at different lag times. Finally, a forecast for the period January 2012 to December 2013 was made. DOI: http://dx.doi.org/10.3329/jbau.v11i2.19925 J. Bangladesh Agril. Univ. 11(2): 271-276, 2013

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