scholarly journals Forecasting inflation in Romania to improve the monetary policy

2012 ◽  
Vol 64 (2) ◽  
pp. 131-143
Author(s):  
Mihaela Bratu
Keyword(s):  
Mean Squared Error ◽  
Forecast Error ◽  
Forecast Errors ◽  
Inflation Forecasts ◽  
Squared Error ◽  
Inflation Rates ◽  
State Of The Economy ◽  
National Bank ◽  
Economic State ◽  
Forecasting Inflation

Based on data of inflation forecasts provided quarterly by the National Bank of Romania, forecast intervals were built using the method of historical forecast errors. Forecast intervals were built considering that the forecast error series is normally distributed of zero mean and standard deviation equal to the RMSE (root mean squared error) corresponding to historical forecast errors. The author introduced as a measure of economic state the indicator- relative variance of the phenomenon at a specific time in relation with the variance on the entire time horizon. For Romania, when inflation rates follows an AR (1), She improved the technique of building forecast intervals taking into account the state of the economy in each period for which data were recorded. The author concludes that it is necessary to build forecasts intervals in order to have a measure of predictions uncertainty.

Toward Better Understanding of the Contiguous Rain Area (CRA) Method for Spatial Forecast Verification

2009 ◽  
Vol 24 (5) ◽  
pp. 1401-1415 ◽  
Author(s):  
Elizabeth E. Ebert ◽  
William A. Gallus
Keyword(s):  
Correlation Coefficient ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Forecast Verification ◽  
Rain Area ◽  
Squared Error ◽  
Verification Methods ◽  
The Mean

Abstract The contiguous rain area (CRA) method for spatial forecast verification is a features-based approach that evaluates the properties of forecast rain systems, namely, their location, size, intensity, and finescale pattern. It is one of many recently developed spatial verification approaches that are being evaluated as part of a Spatial Forecast Verification Methods Intercomparison Project. To better understand the strengths and weaknesses of the CRA method, it has been tested here on a set of idealized geometric and perturbed forecasts with known errors, as well as nine precipitation forecasts from three high-resolution numerical weather prediction models. The CRA method was able to identify the known errors for the geometric forecasts, but only after a modification was introduced to allow nonoverlapping forecast and observed features to be matched. For the perturbed cases in which a radar rain field was spatially translated and amplified to simulate forecast errors, the CRA method also reproduced the known errors except when a high-intensity threshold was used to define the CRA (≥10 mm h−1) and a large translation error was imposed (>200 km). The decomposition of total error into displacement, volume, and pattern components reflected the source of the error almost all of the time when a mean squared error formulation was used, but not necessarily when a correlation-based formulation was used. When applied to real forecasts, the CRA method gave similar results when either best-fit criteria, minimization of the mean squared error, or maximization of the correlation coefficient, was chosen for matching forecast and observed features. The diagnosed displacement error was somewhat sensitive to the choice of search distance. Of the many diagnostics produced by this method, the errors in the mean and peak rain rate between the forecast and observed features showed the best correspondence with subjective evaluations of the forecasts, while the spatial correlation coefficient (after matching) did not reflect the subjective judgments.

MONEY GROWTH AND INFLATION IN THE UNITED STATES

2007 ◽  
Vol 11 (1) ◽  
pp. 113-127 ◽  
Author(s):  
LANCE BACHMEIER ◽  
SITTISAK LEELAHANON ◽  
QI LI
Keyword(s):  
Linear Models ◽  
Nonlinear Models ◽  
Threshold Model ◽  
Forecast Error ◽  
The United States ◽  
Nonparametric Model ◽  
Money Growth ◽  
Inflation Forecasts ◽  
Inflation Forecasting ◽  
Forecasting Inflation

Specification tests reject a linear inflation forecasting model over the period 1959–2002. Based on this finding, we evaluate the out-of-sample inflation forecasts of a fully nonparametric model for 1994–2002. Our two main results are that: (i) nonlinear models produce much better forecasts than linear models, and (ii) including money growth in the nonparametric model yields marginal improvements, but including velocity reduces the mean squared forecast error by as much as 40%. A threshold model fits the data well over the full sample, offering an interpretation of our findings. We conclude that it is important to account for both nonlinearity and the behavior of monetary aggregates when forecasting inflation.

scholarly journals Forecasting Inflation in Latin American Countries Using a SARIMA-LSTM Combination

2021 ◽  
Author(s):  
Rodrigo Peirano ◽  
Werner Kristjanpoller ◽  
Marcel Minutolo
Keyword(s):  
Latin American ◽  
Emerging Economies ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Moving Average ◽  
Inflation Forecasts ◽  
Inflation Forecasting ◽  
Forecasting Inflation ◽  
New Research ◽  
Model Confidence Set

Abstract Inflation forecasting has been and continues to be an important issue for the world's economies. Governments, through their central banks, watch closely inflation indicators to make national decisions and policies. Controlling growth and contraction requires governments to keep a close eye on the rate of inflation. When planning strategic national investments, governments attempt to forecast inflation over longer periods of time. Getting the inflation forecast wrong, can result in significant economic hardships. However, even given its significance, there is limited new research that applies updated methodologies to forecast it, and even fewer studies in emerging economies where inflation may be drastically higher. This study proposes to forecast the inflation rate in emerging economies based on the commonly used Seasonal Autoregressive Integrated Moving Average (SARIMA) approach combined with Long Short Term Memory (LSTM). The results indicate that the proposed model based on the combination of SARIMA and LSTM, have a higher accuracy in inflation forecasts as measured by the Mean Square Error (MSE) of the proposed models over the SARIMA model and LSTM alone. The loss function used is Mean Squared Error (MSE), and the Model Confidence Set (MCS) is used to test the superiority of the models in the economies of Mexico, Colombia and Peru.

scholarly journals Stock Market Prediction Performance of Neural Networks: A Literature Review

2017 ◽  
Vol 9 (11) ◽  
pp. 100 ◽  
Author(s):  
Özgür Ican ◽  
Taha Bugra Çelik
Keyword(s):  
Neural Networks ◽  
Prediction Model ◽  
Mean Squared Error ◽  
Forecast Error ◽  
Absolute Error ◽  
Absolute Deviation ◽  
Error Measures ◽  
Squared Error ◽  
Number Of Publications

In this paper, previous studies featuring an artificial neural networks based prediction model have been reviewed. The main purpose of this review is to examine studies which use directional prediction accuracy (also known as hit ratio) or profitability of the model as a benchmark since other forecast error measures - namely mean absolute deviation (MAD), root mean squared error (RMSE), mean absolute error (MAE) and mean squared error (MSE) - have been criticized for the argument that they are not able to actually show how useful the prediction model is, in terms of financial gains (i.e. for practical usage). In order to meet the publication selection criteria mentioned above, a large number of publications have been examined and 25 of papers satisfying the criteria are selected for comparison. Classification of the eligible papers are summarized in a table format for future studies.

scholarly journals Imputation of Missing Values in Economic and Financial Time Series Data Using Five Principal Component Analysis (PCA) Approaches

2019 ◽  
pp. 51-73
Author(s):  
Chisimkwuo John ◽  
Emmanuel J. Ekpenyong ◽  
Charles C. Nworu
Keyword(s):  
Private Sector ◽  
Least Squares ◽  
Missing Values ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Forecast Error ◽  
Series Data ◽  
Imputation Methods ◽  
Root Mean Squared Error ◽  
Squared Error

This study assessed five approaches for imputing missing values. The evaluated methods include Singular Value Decomposition Imputation (svdPCA), Bayesian imputation (bPCA), Probabilistic imputation (pPCA), Non-Linear Iterative Partial Least squares imputation (nipalsPCA) and Local Least Squares imputation (llsPCA). A 5%, 10%, 15% and 20% missing data were created under a missing completely at random (MCAR) assumption using five (5) variables (Net Foreign Assets (NFA), Credit to Core Private Sector (CCP), Reserve Money (RM), Narrow Money (M1), Private Sector Demand Deposits (PSDD) from Nigeria quarterly monetary aggregate dataset from 1981 to 2019 using R-software. The data were collected from the Central Bank of Nigeria statistical bulletin. The five imputation methods were used to estimate the artificially generated missing values. The performances of the PCA imputation approaches were evaluated based on the Mean Forecast Error (MFE), Root Mean Squared Error (RMSE) and Normalized Root Mean Squared Error (NRMSE) criteria. The result suggests that the bPCA, llsPCA and pPCA methods performed better than other imputation methods with the bPCA being the more appropriate method and llsPCA, the best method as it appears to be more stable than others in terms of the proportion of missingness.

scholarly journals Pengukuran Kapasitas Container Yard Menggunakan Yard Occupancy Ratio dalam Upaya Optimalisasi Penggunaan Lapangan Penumpukan Kontainer di PT XYZ

2020 ◽  
Vol 4 (1) ◽  
pp. 1-11
Author(s):  
Somadi Somadi ◽  
Intan Dewi Permatasari ◽  
Rahmi Chintia
Keyword(s):  
Mean Squared Error ◽  
Forecast Error ◽  
Moving Averages ◽  
Service Company ◽  
Percent Error ◽  
Logistics Service ◽  
Mean Absolute Percent Error ◽  
Squared Error ◽  
Freight Forwarding ◽  
Absolute Percent Error

PT. XYZ is a logistics service company engaged in freight forwarding services for ships / air ships and warehousing. In its operations, the company experienced a problem, namely the flow of containers that entered the company's container yard capacity experienced overcapacity. The purpose of this study was to deter-mine the results of the measurement of container yard capacity using the yard occupancy ratio at PT. XYZ This study uses the Yard Occupancy Ratio (YOR) method to determine the capacity of the container yard, while for forecasting using moving averages and exponential smoothing. Meanwhile, to calculate forecast error using mean squared error and mean absolute percent error. Based on the results of measurements made that the results of forecasting container flows for July 2019 to December 2019 amounted to 1,487 containers, 1,493 con-tainers, 1,614 containers, 1,377 containers, 1,532 containers and 1,495 contain-ers, respectively. Based on the results of the YOR analysis that scenario 3 is the best scenario compared to scenario 1 and scenario 2, because it produces a low-er YOR value, namely for July 2019 at 45%, August 2019 at 45%, September 2019 at 48%, October 2019 at 41%, November 2019 at 46% and December 2019 at 45%. This means that by using the YOR method there will be no overcapasity in the future because the YOR value does not exceed 100%.

Forecast Error Bounds By Stochastic Simulation

1996 ◽  
Vol 156 ◽  
pp. 72-79 ◽  
Author(s):  
Andrew P. Blake
Keyword(s):  
Stochastic Simulation ◽  
Error Bounds ◽  
Forecast Error ◽  
Policy Instruments ◽  
Forecast Errors ◽  
Structural Effects ◽  
Medium Term ◽  
Policy Regime ◽  
Forecasting Inflation ◽  
Better Than

What can the National Institute model tell us about the accuracy of forecasting inflation and growth? We make ‘point’ forecasts over the short to medium term, and assess the accuracy of those forecasts by examining past forecast errors (see Poulizac, Weale and Young, 1996). But the model itself can be used for the same purpose and can inform us better than historical exercises if a new policy regime has been adopted which is a major departure from past experience. In that case, the behaviour of the economy would be expected to be considerably different and so using a model which captures the structural effects of the changes may give a more accurate view of the likely behaviour of policy targets, policy instruments and other variables.

scholarly journals Determining The Optimal Values Of Exponential Smoothing Constants Does Solver Really Work?

2013 ◽  
Vol 6 (3) ◽  
pp. 347-360 ◽  
Author(s):  
Handanhal V. Ravinder
Keyword(s):  
Operations Management ◽  
Mean Squared Error ◽  
Forecast Error ◽  
Management Science ◽  
Exponential Smoothing ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Squared Error ◽  
Optimal Values ◽  
The Impact

A key issue in exponential smoothing is the choice of the values of the smoothing constants used.One approach that is becoming increasingly popular in introductory management science and operations management textbooks is the use of Solver, an Excel-based non-linear optimizer, to identify values of the smoothing constants that minimize a measure of forecast error like Mean Absolute Deviation (MAD) or Mean Squared Error (MSE).We point out some difficulties with this approach and suggest an easy fix. We examine the impact of initial forecasts on the smoothing constants and the idea of optimizing the initial forecast along with the smoothing constants.We make recommendations on the use of Solver in the context of the teaching of forecasting and suggest that there is a better method than Solver to identify the appropriate smoothing constants.

scholarly journals Forecast Error Calculation with Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE)

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Ansari Saleh Ahmar
Keyword(s):  
Decision Making ◽  
Mean Squared Error ◽  
Forecast Error ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Forecasting Accuracy ◽  
Error Calculation ◽  
Absolute Percentage Error ◽  
Squared Error ◽  
High Level

Calculation errors in forecasting a data are very important from a forecasting process. The high level of forecasting accuracy will affect the level of confidence in forecasting decision making.

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.

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