ТHE METHODOLOGY FOR INFLATION’ FORECASTING BY THE BANK OF RUSSIA IN THE MEDIUM TERM

The paper sets out to examine approaches to the forecasting of inflation by a macro market regulator. Various approaches to short-term inflation forecasting, inflation factors and their main channels of influence used by bank regulators in various countries are studied. The shortcomings of the used models for predicting inflation in the post-pandemic economy have been formulated. A comparative analysis of the use of various models has been conducted and solutions for building forecasting models in the medium term have been proposed. The approach has been tested for regional inflation forecasting; calculations of the indicators using VAR model, SARIMA, and dynamic method have been presented. It is proposed to use extended combined VAR models supplemented with exogenous factors for medium-term forecasting.

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Short-term inflation forecasting models for Turkey and a forecast combination analysis

Economic Modelling ◽

10.1016/j.econmod.2013.04.001 ◽

2013 ◽

Vol 33 ◽

pp. 312-325 ◽

Cited By ~ 18

Author(s):

Fethi Öğünç ◽

Kurmaş Akdoğan ◽

Selen Başer ◽

Meltem Gülenay Chadwick ◽

Dilara Ertuğ ◽

...

Keyword(s):

Forecast Combination ◽

Short Term ◽

Forecasting Models ◽

Inflation Forecasting

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Three Attempts at Inflation Forecasting in Pakistan

The Pakistan Development Review ◽

10.30541/v45i3pp.341-368 ◽

2006 ◽

Vol 45 (3) ◽

pp. 341-368 ◽

Cited By ~ 3

Author(s):

Madhavi Bokil ◽

Axel Schimmelpfennig

Keyword(s):

Phillips Curve ◽

Var Model ◽

Money Growth ◽

Leading Indicators ◽

Vector Autoregressive ◽

Parameter Instability ◽

Inflation Forecasting ◽

Forecasting Inflation ◽

Empirical Approaches ◽

Univariate Approach

This paper presents three empirical approaches to forecasting inflation in Pakistan. The preferred approach is a leading indicators model, in which broad money growth and private sector credit growth help forecast inflation. A univariate approach also yields reasonable forecasts, but seems less suited to capturing turning-points. A vector autoregressive (VAR) model illustrates how monetary developments can be described by a Phillips-curve-type relationship. We deal with potential parameter instability on account of fundamental changes in Pakistan’s economic system by restricting our sample to more recent observations. Aspects of Gregorian and Islamic calendar seasonality are addressed by using 12-month moving averages.

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

Buletin Ekonomi Moneter dan Perbankan ◽

10.21098/bemp.v22i4.1235 ◽

2019 ◽

Vol 22 (4) ◽

pp. 423-436 ◽

Cited By ~ 1

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.

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Is money the creation of the state or the market? (On the “modern monetary theory” as described in the textbook by W. Mitchell, L. R. Wray and M. Watts “Macroeconomics”)

Voprosy Ekonomiki ◽

10.32609/0042-8736-2020-6-121-134 ◽

2020 ◽

pp. 121-134

Author(s):

S. A. Andryushin

Keyword(s):

Public Debt ◽

Scientific Community ◽

Full Employment ◽

Term Effect ◽

Scientific Concept ◽

Monetary Theory ◽

Left Wing ◽

Medium Term ◽

Short Term ◽

Stimulation Of

In 2019, a textbook “Macroeconomics” was published in London, on the pages of which the authors presented a new monetary doctrine — Modern Monetary Theory, MMT, — an unorthodox concept based on the postulates of Post-Keynesianism, New Institutionalism, and the theory of Marxism. The attitude to this scientific concept in the scientific community is ambiguous. A smaller part of scientists actively support this doctrine, which is directly related to state monetary and fiscal stimulation of full employment, public debt servicing and economic growth. Others, the majority of economists, on the contrary, strongly criticize MMT, arguing that the new theory hides simple left-wing populism, designed for a temporary and short-term effect. This article considers the origins and the main provisions of MMT, its discussions with the mainstream, criticism of the basic tenets of MMT, and also assesses possible prospects for the development of MMT in the medium term.

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Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽

10.3390/electronics10101151 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1151

Author(s):

Carolina Gijón ◽

Matías Toril ◽

Salvador Luna-Ramírez ◽

María Luisa Marí-Altozano ◽

José María Ruiz-Avilés

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Analysis ◽

Data Traffic ◽

Medium Term ◽

Short Term ◽

Traffic Forecasting ◽

Series Analysis ◽

Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

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Short-term Load Forecasting by Using Improved GEP and Abnormal Load Recognition

ACM Transactions on Internet Technology ◽

10.1145/3447513 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1-28

Author(s):

Song Deng ◽

Fulin Chen ◽

Xia Dong ◽

Guangwei Gao ◽

Xindong Wu

Keyword(s):

Gene Expression ◽

Probability Distribution ◽

Detection Rate ◽

Cross Validation ◽

Normal Load ◽

Gene Expression Programming ◽

Load Forecasting ◽

Short Term ◽

Forecasting Models ◽

Short Term Load Forecasting

Load forecasting in short term is very important to economic dispatch and safety assessment of power system. Although existing load forecasting in short-term algorithms have reached required forecast accuracy, most of the forecasting models are black boxes and cannot be constructed to display mathematical models. At the same time, because of the abnormal load caused by the failure of the load data collection device, time synchronization, and malicious tampering, the accuracy of the existing load forecasting models is greatly reduced. To address these problems, this article proposes a Short-Term Load Forecasting algorithm by using Improved Gene Expression Programming and Abnormal Load Recognition (STLF-IGEP_ALR). First, the Recognition algorithm of Abnormal Load based on Probability Distribution and Cross Validation is proposed. By analyzing the probability distribution of rows and columns in load data, and using the probability distribution of rows and columns for cross-validation, misjudgment of normal load in abnormal load data can be better solved. Second, by designing strategies for adaptive generation of population parameters, individual evolution of populations and dynamic adjustment of genetic operation probability, an Improved Gene Expression Programming based on Evolutionary Parameter Optimization is proposed. Finally, the experimental results on two real load datasets and one open load dataset show that compared with the existing abnormal data detection algorithms, the algorithm proposed in this article have higher advantages in missing detection rate, false detection rate and precision rate, and STLF-IGEP_ALR is superior to other short-term load forecasting algorithms in terms of the convergence speed, MAE, MAPE, RSME, and R 2 .

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Multidimensional assessment of women after severe maternal morbidity: the COMMAG cohort study

BMJ Open ◽

10.1136/bmjopen-2020-041138 ◽

2020 ◽

Vol 10 (12) ◽

pp. e041138

Author(s):

Elton C Ferreira ◽

Maria Laura Costa ◽

Rodolfo C Pacagnella ◽

Carla Silveira ◽

Carla B Andreucci ◽

...

Keyword(s):

Cohort Study ◽

Sexual Function ◽

Screening Test ◽

Maternal Morbidity ◽

Severe Maternal Morbidity ◽

Medium Term ◽

Short Term ◽

Multidimensional Assessment ◽

Index Pregnancy ◽

Sf 36

ObjectivesTo perform a multidimensional assessment of women who experienced severe maternal morbidity (SMM) and its short-term and medium-term impact on the lives and health of women and their children.DesignA retrospective cohort study.SettingA tertiary maternity hospital from the southeast region of Brazil.ParticipantsThe exposed population was selected from intensive care unit admissions if presenting any diagnostic criteria for SMM. Controls were randomly selected among women without SMM admitted to the same maternity and same time of childbirth.Primary and secondary outcome variablesValidated tools were applied, addressing post-traumatic stress disorder (PTSD) and quality of life (SF-36) by phone, and then general and reproductive health, functioning (WHO Disability Assessment Schedule), sexual function (Female Sexual Function Index (FSFI)), substance abuse (Alcohol, Smoking and Substance Involvement Screening Test 2.0) and growth/development (Denver Developmental Screening Test) of children born in the index pregnancy in a face-to-face interview.ResultsAll instruments were applied to 638 women (315 had SMM; 323 were controls, with the assessment of 264 and 307 children, respectively). SF-36 score was significantly lower in the SMM group, while PTSD score was similar between groups. Women who had SMM became more frequently sterile, had more abnormal clinical conditions after the index pregnancy and a higher score for altered functioning, while proportions of FSFI score or any drug use were similar between groups. Furthermore, children from the SMM group were more likely to have weight (threefold) and height (1.5 fold) for age deficits and also impaired development (1.5-fold).ConclusionSMM impairs some aspects of the lives of women and their children. The focus should be directed towards monitoring these women and their children after birth, ensuring accessibility to health services and reducing short-term and medium-term repercussions on physical, reproductive and psychosocial health.

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