Cyclic dynamic patterns of Russian macroeconomic indicators found by spectral analysis

2021 ◽  
pp. 3-28
Author(s):  
Olga Vinogradova ◽  
Anna Krupkina ◽  
Kseniya Pierpoint ◽  
Denis Kokosinskii
Keyword(s):  
Time Series Data ◽  
Series Data ◽  
Time Lags ◽  
Economic Cycle ◽  
Economic Sectors ◽  
Economic Agents ◽  
Macroeconomic Indicators ◽  
Linear Vibrations ◽  
The One ◽  
Spectral Data Analysis

The paper proposes a contemporary interdisciplinary method to identify consistent patterns within cyclical dynamics of GDP and its macroeconomics determinants in the Russian Federation. This method may contribute to better recognition of the stages of economic cycle and of potential early predicators to recessions and crises. We first identify the trend component of Russian GDP and then apply the spectral data analysis to its cyclical component which reveals its multi-frequency, and non-linear vibrations. These vibrations are then further investigated by transforming time series data on GDP and its determinants into a frequency spectrum series via Fourier transform techniques. Wavelength scanning of selected macroeconomic indicators shows the basic economic cycle of real GDP with duration time of approx. 3.13 years. Other procyclical indicators nevertheless discover asynchronous behavior towards GDP due to the relative autonomy of the sectors standing behind these indicators. Their autonomy lies behind differences in reaction forces (shifts) and periods (lags) to both internal and external shocks. We estimate differentials between the dynamics of GDP and its determinants by evaluating phase deviations of their pairwise harmonic components, mutual pairwise phase shifts, and by comparison of their pairwise cross-spectrum. The one of output is the quantification of time lags between GDP and key macroeconomic indicators of individual economic sectors. This result reveals the complexity of GDP dynamics that sends an aliased rather than a unit signal to economic agents. Our decomposition of this signal into signals from key economic sectors and quantification of phase discrepancies between sectoral signals may contribute to findings in early crisis predicators. We also estimate the depth and velocity of shocks penetrations into both economy as a whole and its particular sectors.

Post Crisis Performance and Confidence of the Indian Economy

2015 ◽  
pp. 62-90
Author(s):  
Rajib Bhattacharyya
Keyword(s):  
Time Series Data ◽  
Global Financial Crisis ◽  
Structural Break ◽  
Real Effective Exchange Rate ◽  
Series Data ◽  
Economic Agents ◽  
Indian Economy ◽  
Macroeconomic Indicators ◽  
Effective Exchange ◽  
Confidence Index

The recent global financial crisis is viewed as a glaring example of limitless pursuit of deregulation of financial markets and failure of global corporate governance. Though the global economic slowdown had its epicenter in the US but its impact is being witnessed in all major economies of the world. The present chapter seeks to analyze the post crisis experience of the Indian economy as compared to the global economic performances, using various macroeconomic indicators as output, employment, inflation, current account balance, movement in real effective exchange rate and inflow of FDI. It is based on a statistical analysis using secondary time-series data and is based on the Exogenous Structural Break Model developed by Perron (1989). Finally it tries to highlight the confidence of the economic agents based on some well recognized confidence indices (for e.g. Business Confidence Index, Consumer Confidence Index, FDI Confidence Index etc.) during the post-crisis period.

scholarly journals Bernoulli Time Series Modelling with Application to Accommodation Tourism Demand

2021 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Miguel Ángel Ruiz Reina
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Prognostic Models ◽  
Series Data ◽  
Economic Agents ◽  
Time Series Modelling ◽  
Simulation And Training ◽  
Uncertainty Method ◽  
And Training ◽  
Hotel Accommodation

In this research, a new uncertainty method has been developed and applied to forecasting the hotel accommodation market. The simulation and training of Time Series data are from January 2001 to December 2018 in the Spanish case. The Log-log BeTSUF method estimated by GMM-HAC-Newey-West is considered as a contribution for measuring uncertainty vs. other prognostic models in the literature. The results of our model present better indicators of the RMSE and Ratio Theil’s for the predictive evaluation period of twelve months. Furthermore, the straightforward interpretation of the model and the high descriptive capacity of the model allow economic agents to make efficient decisions.

Oil and Methanol Price volatility

2013 ◽  
Vol 03 (08) ◽  
pp. 01-10
Author(s):  
Majid Delavari ◽  
Nadiya Gandali Ali khani ◽  
Esmaeil Naderi
Keyword(s):  
Crude Oil ◽  
Time Series Data ◽  
Price Volatility ◽  
High Sensitivity ◽  
Oil Price ◽  
Series Data ◽  
Cultural Issues ◽  
Crude Oil Price ◽  
Oil Export ◽  
The One

Crude oil as one of the main sources of energy is also the main source of income for members of OPEC. So, the volatility of crude oil price is one of the main economic variables in the world and analysis of the effect of its changes on key economic factors has been always considered as significant. The reason might be the high sensitivity of oil price to political, economic and cultural issues worldwide and consequently its volatility on the one hand, and the high influence of the volatile prices on macroeconomic variables. On the other hand, for different reasons such as oil price volatilities and income from oil export, economic planners and policy makers in Iran have been mainly focused on the promotion of non-oil exports especially during the last few decades. Therefore, methanol as one of the most commonly used petrochemical products has a high potential for production and export of non-oil products in Iran. For this reason, in the present study there was an attempt to examine the relationship between the prices of Iran’s crude oil and methanol using FIGARCH model and based on the weekly time series data related to the research variables. The results of the study showed that the long memory parameter is equal to 0.32 which is meaning the shocks caused by volatility of methanol market and crude oil price to the methanol price were lasting and meaningful and were revealed in the long term.

scholarly journals Islamic Finance and Economic Growth Nexus: An Econometric Analysis

2019 ◽  
Vol 2 (1) ◽  
pp. 11-22
Author(s):  
Kashif Raza ◽  
Rashid Ahmad ◽  
Muhammad Abdul Rehman Shah ◽  
Muhammad Umar
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Trade Openness ◽  
Unit Root Test ◽  
Least Square ◽  
Series Data ◽  
Islamic Finance ◽  
Causality Test ◽  
Economic Agents ◽  
Fixed Capital Formation

Researchers have written chain of research papers about the dynamics of financial development and economic growth. The financial capital plays a productive role when it delivers to economic agents who are facing shortage or excess of funds.  This study explores the linkages among Islamic financing and economic growth for Pakistan, by using annual time series data from 2005-2018. Islamic banks’ financing funds used as a proxy of Islamic financing, Gross Domestic Product (GDP), Gross Fixed Capital Formation (GFCF), labor force (LF),Broad money(M) and Trade openness (TO) to presents real sector of an economy. For the exploration, the unit root test, Ordinary least square technique and Granger causality test are applied. The results validate a substantial causal relationship of Islamic financing and GDP, which supports the Schumpeter’s supply-leading view. The results indicate that Islamic finance contributed towards economic growth.  

scholarly journals Partial Correlation-Based Attention for Multivariate Time Series Forecasting

2020 ◽  
Vol 34 (10) ◽  
pp. 13720-13721
Author(s):  
Won Kyung Lee
Keyword(s):  
Time Series ◽  
Partial Correlation ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Time Series Forecasting ◽  
Data Driven ◽  
Series Data ◽  
Time Lags ◽  
Dependency Structure ◽  
Agnostic Learning

A multivariate time-series forecasting has great potentials in various domains. However, it is challenging to find dependency structure among the time-series variables and appropriate time-lags for each variable, which change dynamically over time. In this study, I suggest partial correlation-based attention mechanism which overcomes the shortcomings of existing pair-wise comparisons-based attention mechanisms. Moreover, I propose data-driven series-wise multi-resolution convolutional layers to represent the input time-series data for domain agnostic learning.

scholarly journals The Effects Of Asymmetric Transmission Of Exchange Rate On Inflation In Iran: Application Of Threshold Models

2015 ◽  
Vol 10 (2) ◽  
pp. 99-113
Author(s):  
Yazdan Naghdi ◽  
Soheila Kaghazian
Keyword(s):  
Exchange Rate ◽  
Time Series Data ◽  
Public Procurement ◽  
Consumer Price Index ◽  
Series Data ◽  
Asymmetric Transmission ◽  
Exchange Rate Fluctuation ◽  
Rate Fluctuation ◽  
Non Linear ◽  
The One

Abstract Given the recent fluctuation in the exchange rate and the presence of several factors such as the various economy-political sanctions (mainly embargos on oil and banking), extreme volatility in different economic fields, and consequently the devaluation of national and public procurement -A landmark that is emanating from exchange rate fluctuation - two points should be noted: First, it is essential to review the effect of exchange rate fluctuation on macro economic variables such as inflation and to provide appropriate policies. Second, the existence of this condition provides the chance to study the relation between exchange rate and inflation in a non-linear and asymmetric method. Hence, the present study seeks to use TAR model and, on the basis of monthly time series data over the period March 2002 to March 2014, to analyze the cross-asymmetric and non-linear exchange rate on consumer price index (CPI) in Iran. The results also show the presence of an asymmetric long-term relationship between these variables (exchange rate and CPI). Also, in the Iranian economy, the effect of negative shocks of exchange rate on inflation is more sustainable than the one from positive shocks.

Time Series Data Mining Implemented on Football Match

2010 ◽  
Vol 26-28 ◽  
pp. 98-103 ◽  
Author(s):  
Ben Cheng Chai
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Data Mining Technique ◽  
Time Series Data Mining ◽  
Proposed Model ◽  
Interesting Pattern ◽  
Football Match ◽  
The One

This study utilizes time series data mining to find the interesting pattern and cooperation custom. Meanwhile, data mining technique and some special football skills such as ball possession are employed to build a novel decision model in football match. The proposed model is expatiated through real football match. In short, on the one hand, the model provides a feasible route to guide the decision makers including football coach to establish effective mechanism in football match. On the other hand, it extends the application scope of time series data mining.

scholarly journals Stride of Service Sector in Nepal’s Trajectories of Structural Change

2018 ◽  
pp. 69-98
Author(s):  
Madhav Prasad Dahal
Keyword(s):  
Structural Transformation ◽  
Service Sector ◽  
Time Series Data ◽  
Value Added ◽  
Developed Countries ◽  
Policy Implications ◽  
Series Data ◽  
Economic Sectors ◽  
Distributed Lag ◽  
Gross Value Added

Agriculture, manufacturing and service sectors are the major economic sectors of a country. The long held view is that economies’ development trajectories move from agriculture to manufacturing to services. These conclusions are primarily based on the studies of developed countries. However more recent studies relating to developing countries have brought evidences that the structural transformation path is not linear as experienced by today’s developed countries. Nepal is not an exception is experiencing the waves of sector-wise structural transformation. Using time series data of the period 1975-2016 of the economy of Nepal this paper analyses the association between gross value added and service sector value added in the analytic-framework of the autoregressive distributed lag (ARDL) to cointegration. The empirical result reveal a cointegrating relationship between real gross value added and service sector value added. Result also show service sector enhancing role of education and export trade of Nepal. The paper finally draws few policy implications essential for service sector sustainability to support overall economic growth.Economic Journal of Development Issues Vol. 21 & 22 No. 1-2 (2016) Combined Issue

scholarly journals Intermedia Agenda Setting in the Social Media Age: How Traditional Players Dominate the News Agenda in Election Times

2017 ◽  
Vol 22 (3) ◽  
pp. 275-293 ◽  
Author(s):  
Raymond A. Harder ◽  
Julie Sevenans ◽  
Peter Van Aelst
Keyword(s):  
Social Media ◽  
News Media ◽  
Agenda Setting ◽  
Time Series Data ◽  
Fixed Time ◽  
Series Data ◽  
News Story ◽  
Time Lags ◽  
Intermedia Agenda Setting ◽  
Twitter Users

Intermedia agenda setting is a widely used theory to explain how content transfers between news media. The recent digitalization wave, however, challenges some of its basic presuppositions. We discuss three assumptions that cannot be applied to online and social media unconditionally: one, that media agendas should be measured on an issue level; two, that fixed time lags suffice to understand overlap in media content; and three, that media can be considered homogeneous entities. To address these challenges, we propose a “news story” approach as an alternative way of mapping how news spreads through the media. We compare this with a “traditional” analysis of time-series data. In addition, we differentiate between three groups of actors that use Twitter. For these purposes, we study online and offline media alike, applying both measurement methods to the 2014 Belgium election campaign. Overall, we find that online media outlets strongly affect other media that publish less often. Yet, our news story analysis emphasizes the need to look beyond publication schemes. “Slow” newspapers, for example, often precede other media’s coverage. Underlining the necessity to distinguish between Twitter users, we find that media actors on Twitter have vastly more agenda-setting influence than other actors do.

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