scholarly journals Mathematical Development and Evaluation of Forecasting Models for Accuracy of Inflation in Developing Countries: A Case of Vietnam

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Nhu-Ty Nguyen ◽  
Thanh-Tuyen Tran
Keyword(s):  
Raw Materials ◽  
Arima Model ◽  
Consumer Price Index ◽  
Forecast Accuracy ◽  
Complex Problem ◽  
Forecast Performance ◽  
Forecasting Models ◽  
Out Of Sample ◽  
Forecasting Inflation ◽  
Using Data

Inflation is a key element of a national economy, and it is also a prominent and important issue influencing the whole economy in terms of marketing. This is a complex problem requiring a large investment of time and wisdom to attain positive results. Thus, appropriate tools for forecasting inflation variables are crucial significant for policy making. In this study, both clarified value calculation and use of a genetic algorithm to find the optimal parameters are adopted simultaneously to construct improved models: ARIMA, GM(1,1), Verhulst, DGM(1,1), and DGM(2,1) by using data of Vietnamese inflation output from January 2005 to November 2013. The MAPE, MSE, RMSE, and MAD are four criteria with which the various forecasting models results are compared. Moreover, to see whether differences exist, Friedman and Wilcoxon tests are applied. Both in-sample and out-of-sample forecast performance results show that the ARIMA model has highly accurate forecasting in Raw Materials Price (RMP) and Gold Price (GP), whereas, the calculated results of GM(1,1) and DGM(1,1) are suitable to forecast Consumer Price Index (CPI). Therefore, the ARIMA, GM(1,1), and DGM(1,1) can handle the forecast accuracy of the issue, and they are suitable in modeling and forecasting of inflation in the case of Vietnam.

On the Efficiency Between Forecasting Models—A Study in the Indian Context

1987 ◽  
Vol 36 (1-2) ◽  
pp. 19-28
Author(s):  
Lakshmikanta Datta
Keyword(s):  
Transfer Function ◽  
Moving Average ◽  
Arima Model ◽  
Consumer Price Index ◽  
Transfer Function Model ◽  
Industrial Workers ◽  
Economic Time Series ◽  
Function Model ◽  
Forecasting Models ◽  
Economic Time

In this paper, we have investigated the relative performances of two types of forecasting models, namely univariate autoregressive integrated moving average (ARIMA) model and transfer function model, with the help of two Indian economic time series viz. (i) Money Supply (M3 ) and (ii) Consumer Price Index Numbers for Industrial Workers. Our emperical results show that the efficiency of transfer function model is substantially superior to that of the univariate model.

scholarly journals The Impact Of Macroeconomic Variables On Real Estate Price Forecasting Modelling In Abuja Nigeria

2019 ◽  
Vol 8 (5C) ◽  
pp. 67-72
Keyword(s):  
Real Estate ◽  
Negative Impact ◽  
Positive Impact ◽  
Consumer Price Index ◽  
Price Forecasting ◽  
Macroeconomic Variables ◽  
Real Estate Price ◽  
Forecasting Models ◽  
Out Of Sample ◽  
The Impact

This paper examined the impact of macroeconomic variables on real estate price forecasting modelling in Abuja, Nigeria using the family of Box-Jenkins ARIMA models. The ARIMA and ARIMAX models were used to forecast real estate residential price in Abuja, Nigeria using quarterly data from 2000Q1 to 2017Q in Naira (N). The outcome revealed that, macroeconomic variables such as consumer price index, price of crude oil, exchange rate of Naira against US dollar, GDP, interest rate, household income has significant positive impact on the real estate residential price forecasting models of 2 bedroom flat, 3 bedroom flat and 5 bedroom flat, while they have strong negative impact on the price forecasting model of 4 bedroom flat. Moreover, ARIMA and ARIMAX provides best out of sample forecasting models.

scholarly journals Analysis of Indonesia’s Inflation Using ARIMA and Artificial Neural Network

2019 ◽  
Vol 8 (2) ◽  
pp. 151-162
Author(s):  
Fauzi Insan Estiko ◽  
Wahyuddin Wahyuddin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Arima Model ◽  
Data Series ◽  
Short Term ◽  
Data Set ◽  
Forecast Performance ◽  
Squared Error ◽  
Forecasting Inflation

This study aims to compare forecast performance of Neural Network (NN) to ARIMA in the case of Indonesia’s inflation and to find if there is any interesting trend in Indonesia’s inflation. We use year-on-year monthly Indonesia’s inflation data from 2006:12 to 2018:12 released by Bank Indonesia (BI) and the Indonesian Central Bureau of Statistics (CBS). We divide the series into 3 data series to capture the trend in the inflation (i.e DS1, DS2 and DS3). The data set 1 (DS1) covers data  from 2006:12 to 2014:08, DS2 from 2006:12 to 2018:12, dan DS3 from 2010:12 to 2018:12. The series is then  processed using the  standard ARIMA method and NN model. We found that the NN model outperforms the ARIMA model in forecasting inflation for each respective series by analysing  its Root Mean Squared Error (RMSE). We also found that short term lagged-inflation (backward-looking) variable has lesser effect on inflation compared to the more recent series.

scholarly journals FORECASTING INDONESIAN INFLATION WITHIN AN INFLATION-TARGETING FRAMEWORK: DO LARGE-SCALE MODELS PAY OFF?

2019 ◽  
Vol 22 (4) ◽  
pp. 423-436 ◽  
Author(s):  
Solikin M. Juhro ◽  
Bernard Njindan Iyke
Keyword(s):  
Large Scale ◽  
Inflation Targeting ◽  
Model Averaging ◽  
Inclusion Probability ◽  
Forecasting Models ◽  
Inflation Forecasting ◽  
Scale Models ◽  
Out Of Sample ◽  
Forecasting Inflation ◽  
Dynamic Model Averaging

We examine the usefulness of large-scale inflation forecasting models in Indonesiawithin an inflation-targeting framework. Using a dynamic model averaging approachto address three issues the policymaker faces when forecasting inflation, namely,parameter, predictor, and model uncertainties, we show that large-scale modelshave significant payoffs. Our in-sample forecasts suggest that 60% of 15 exogenouspredictors significantly forecast inflation, given a posterior inclusion probability cut-offof approximately 50%. We show that nearly 87% of the predictors can forecast inflationif we lower the cut-off to approximately 40%. Our out-of-sample forecasts suggest thatlarge-scale inflation forecasting models have substantial forecasting power relative tosimple models of inflation persistence at longer horizons.

scholarly journals Uncertainty, Spillovers, and Forecasts of the Realized Variance of Gold Returns

2021 ◽  
Vol 26 (3) ◽  
pp. 49
Author(s):  
Rangan Gupta ◽  
Christian Pierdzioch
Keyword(s):  
Forecast Accuracy ◽  
Systematic Effect ◽  
Derivative Securities ◽  
Realized Variance ◽  
International Spillovers ◽  
Out Of Sample ◽  
Related Derivative ◽  
Using Data ◽  
Lasso Estimator

Using data for the group of G7 countries and China for the sample period 1996Q1 to 2020Q4, we study the role of uncertainty and spillovers for the out-of-sample forecasting of the realized variance of gold returns and its upside (good) and downside (bad) counterparts. We go beyond earlier research in that we do not focus exclusively on U.S.-based measures of uncertainty, and in that we account for international spillovers of uncertainty. Our results, based on the Lasso estimator, show that, across the various model configurations that we study, uncertainty has a more systematic effect on out-of-sample forecast accuracy than spillovers. Our results have important implications for investors in terms of, for example, pricing of related derivative securities and the development of portfolio-allocation strategies.

scholarly journals Forecasting the Volatility of Crude Oil: The Role of Uncertainty and Spillovers

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4173
Author(s):  
Rangan Gupta ◽  
Christian Pierdzioch
Keyword(s):  
Crude Oil ◽  
Predictive Value ◽  
Realized Variance ◽  
International Spillovers ◽  
Forecasting Models ◽  
West Texas ◽  
Out Of Sample ◽  
One Year ◽  
Lasso Estimator

We use a dataset for the group of G7 countries and China to study the out-of-sample predictive value of uncertainty and its international spillovers for the realized variance of crude oil (West Texas Intermediate and Brent) over the sample period from 1996Q1 to 2020Q4. Using the Lasso estimator, we found evidence that uncertainty and international spillovers had predictive value for the realized variance at intermediate (two quarters) and long (one year) forecasting horizons in several of the forecasting models that we studied. This result holds also for upside (good) and downside (bad) variance, and irrespective of whether we used a recursive or a rolling estimation window. Our results have important implications for investors and policymakers.

scholarly journals Selecting a Model for Forecasting

Econometrics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 26
Author(s):  
Jennifer L. Castle ◽  
Jurgen A. Doornik ◽  
David F. Hendry
Keyword(s):  
Structural Breaks ◽  
Simulation Experiment ◽  
Structural Break ◽  
Static Model ◽  
Forecast Period ◽  
Forecast Performance ◽  
Significance Level ◽  
Forecasting Models ◽  
Theoretical Results

We investigate forecasting in models that condition on variables for which future values are unknown. We consider the role of the significance level because it guides the binary decisions whether to include or exclude variables. The analysis is extended by allowing for a structural break, either in the first forecast period or just before. Theoretical results are derived for a three-variable static model, but generalized to include dynamics and many more variables in the simulation experiment. The results show that the trade-off for selecting variables in forecasting models in a stationary world, namely that variables should be retained if their noncentralities exceed unity, still applies in settings with structural breaks. This provides support for model selection at looser than conventional settings, albeit with many additional features explaining the forecast performance, and with the caveat that retaining irrelevant variables that are subject to location shifts can worsen forecast performance.

scholarly journals Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

2017 ◽  
Vol 21 (9) ◽  
pp. 4841-4859 ◽  
Author(s):  
Sean W. D. Turner ◽  
James C. Bennett ◽  
David E. Robertson ◽  
Stefano Galelli
Keyword(s):  
System Performance ◽  
Research Effort ◽  
Forecast Accuracy ◽  
Strong Relationship ◽  
Forecast Skill ◽  
Severe Drought ◽  
Forecast Performance ◽  
Performance Improvements ◽  
Sensitivity Assessment ◽  
Seasonal Streamflow

Abstract. Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

scholarly journals Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption

2017 ◽  
Vol 17 (2) ◽  
pp. 1187-1205 ◽  
Author(s):  
Guangliang Fu ◽  
Fred Prata ◽  
Hai Xiang Lin ◽  
Arnold Heemink ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Volcanic Ash ◽  
Three Dimensional ◽  
Forecast Accuracy ◽  
Improve Model ◽  
Powerful Approach ◽  
Using Data ◽  
D Model

Abstract. Using data assimilation (DA) to improve model forecast accuracy is a powerful approach that requires available observations. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations for the assimilation scheme. However, because these primary satellite-retrieved data are often two-dimensional (2-D) and the ash plume is usually vertically located in a narrow band, directly assimilating the 2-D ash mass loadings in a three-dimensional (3-D) volcanic ash model (with an integral observational operator) can usually introduce large artificial/spurious vertical correlations.In this study, we look at an approach to avoid the artificial vertical correlations by not involving the integral operator. By integrating available data of ash mass loadings and cloud top heights, as well as data-based assumptions on thickness, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2-D volcanic ash mass loadings to 3-D concentrations. The 3-D SOO makes the analysis step of assimilation comparable in the 3-D model space.Ensemble-based DA is used to assimilate the extracted measurements of ash concentrations. The results show that satellite DA with SOO can improve the estimate of volcanic ash state and the forecast. Comparison with both satellite-retrieved data and aircraft in situ measurements shows that the effective duration of the improved volcanic ash forecasts for the distal part of the Eyjafjallajökull volcano is about 6 h.

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