scholarly journals Modelling of Fiscal and Monetary Policy Interactions in the Republic of Belarus

2019 ◽  
Vol 5 (3) ◽  
pp. 220-235
Author(s):  
I. A. Loukianova ◽  
◽  
M. A. Shkliarova ◽  
S. Yu. Vysotsky ◽  
◽  
...  
Keyword(s):  
Fiscal Policy ◽  
Markov Switching ◽  
Markov Switching Model ◽  
Tax Reforms ◽  
Policy Regime ◽  
Monetary Policies ◽  
Switching Model ◽  
Consumer Lending ◽  
Autonomous Consumption ◽  
The Republic

The article discusses classical and modern macroeconomic models of interaction of fiscal and monetary policies in Belarus. The hypothesis of this research is that the interaction of fiscal and monetary policies has a synergistic effect on economic growth and that at certain stages, one of these policies prevails over the other. This hypothesis was tested with the help of an IS-LM model, which was used to investigate the joint effects of monetary and fiscal policies on business activity in Belarus. A Markov switching model was developed in Eviews software to analyze the interaction between these policies. Regression dependences of the average tax burden (including the burden imposed by social security contributions) and GDP, investment and the refinancing rate were built by using Excel software. To solve the IS-LM model, the value of autonomous consumption was computed with the help of the adjusted value of the average propensity to consume. It was found that autonomous consumption is comparable with the budget of subsistence minimum in Belarus. The share of government spending in the GDP structure was on average 35.01%. The comparison of gross savings and investment showed that in the majority of periods, gross savings insignificantly exceeded the amount of investment, that is, the available funds were used for consumer lending rather than for investment. Analysis of the Markov switching model has led us to the conclusion that from the first quarter 2005 until the fourth quarter of 2009, the fiscal policy in Belarus was in the active regime. The passive fiscal policy regime was observed in the period between the first quarter of 2010 and the first quarter of 2019. In this period, a rise in the public debt was accompanied by an increase in the budget surplus. In the second quarter of 2019, there was a transition to a more active fiscal policy, which points to the need to intensify tax reforms.

A Markov-Switching model for building occupant activity estimation

2019 ◽  
Vol 183 ◽  
pp. 672-683 ◽  
Author(s):  
Sebastian Wolf ◽  
Jan Kloppenborg Møller ◽  
Magnus Alexander Bitsch ◽  
John Krogstie ◽  
Henrik Madsen
Keyword(s):  
Markov Switching ◽  
Markov Switching Model ◽  
Switching Model ◽  
Activity Estimation

scholarly journals Estimasi Bullish dan Bearish dengan Model Perpindahan Markov dan Risiko Sistematis (beta) dengan Model Penilaian Modal Sharpe dalam Investasi Saham di Bursa Efek Indonesia, Tahun 2011 - 2016

2018 ◽  
Vol 8 (3) ◽  
pp. 221
Author(s):  
Prima Respati ◽  
Budi Purwanto ◽  
Abdul Kohar Irwanto
Keyword(s):  
Interest Rates ◽  
Capital Market ◽  
Stock Price ◽  
Markov Switching ◽  
Systematic Risk ◽  
Risk Identification ◽  
Pricing Model ◽  
Markov Switching Model ◽  
Switching Model ◽  
Capital Assets

<p><em>ABSTRACT</em></p><p><em>Various research including Panggabean (2010) and Usman (2016) show that the long-term trend of Indonesia's capital market is on an uptrend, marked by more bullish periods and longer duration than bearish; and the development determined by rising rates of return rather than interest rates and exchange rates (Defrizal et al, 2015). However, the research has not determined yet whether there are any difference risks in bullish and bearish conditions, especially for systematic or market risk. This study aims to 1) identify the bullish and bearish segmentation period using the Markov Switching Model, and 2) measure systematic risk using the capital assets pricing model (CAPM) with the Sharpe beta indicator. Using the composite stock price index (JCI) and trading data from TICMI (The Indonesia Capital Market Institute) period 2011-2016, consists of 560 issuers, it was found that there were 10 segments that could be identified as 5 bullish periods for 30 weeks , and 5 bearish periods for 8 weeks. Other finding indicates that the probability of switching from bullish to bearish is 3.33% and from bearish to bullish is 12.14%. That means there are positive sentiments that the market tends to be bullish rather than vice versa. The result of beta or systematic risk identification indicates that during bullish and bearish period the market proved to be different risk. Other interesting findings, in both these two different conditions there are negative betas exist that still gives a positive yield.</em></p><p> </p><p>ABSTRAK</p><p>Berbagai riset termasuk Panggabean (2010) dan Usman (2016) menunjukkan bahwa kecenderungan jangka panjang pasar modal Indonesia berada pada kecenderungan naik (uptrend), ditandai dengan periode bullish lebih banyak, dan durasi lebih panjang, daripada bearish.  Perkembangan perkembangan itu dipicu oleh kenaikan tingkat imbalan, alih-alih suku bunga dan nilai tukar (Defrizal et al 2015). Namun riset-riset tersebut tidak mengidentifikasi eksistensi kondisi bullish dan bearish dan berdampak perbedaan risiko, terutama risiko sistematis atau risiko pasar, kecuali mengasumsikan saja keberadaannya.  Penelitian ini bertujuan 1) mengidentifikasi segmentasi periode bullish dan bearish dengan menggunakan model perpindahan Markov (Markov Switching), dan mengukur risiko sistematis menggunakan model penilaian modal (capital assets pricing model, CAPM) dengan indikator beta Sharpe.  Menggunakan data indeks harga saham gabungan (IHSG) serta data perdagangan bersumber dari TICMI (The Indonesia Capital Market Institute) periode 2011-2016 yang mencakup 560 emiten, diperoleh hasil bahwa dalam periode tersebut terdapat 10 segmen yang dapat diidentifikasi sebagai 5 periode bullish selama 30 pekan, dan 5 periode bearish selama 8 pekan.  Temuan lain menunjukkan bahwa peluang perpindahan dari kondisi bullish ke bearish sebesar 3,33% dan dari kondisi bearish ke bullish sebesar 12,14%. Artinya terdapat sentimen positif bahwa pasar cenderung menjadi bullish daripada sebaliknya.  Hasil identifikasi risiko sistematis menunjukkan, berbeda dengan konsep dasar CAPM, bahwa beta pada periode bullish dan bearish tidak sama.  Temuan menarik lainnya, pada kedua kondisi tersebut terdapat beta negatif yang dapat memberikan tingat imbalan positif.</p>

scholarly journals A Markov-Switching Model Approach to Heart Sound Segmentation and Classification

2020 ◽  
Vol 24 (3) ◽  
pp. 705-716 ◽  
Author(s):  
Fuad Noman ◽  
Sh-Hussain Salleh ◽  
Chee-Ming Ting ◽  
S. Balqis Samdin ◽  
Hernando Ombao ◽  
...  
Keyword(s):  
Heart Sound ◽  
Markov Switching ◽  
Markov Switching Model ◽  
Switching Model ◽  
Heart Sound Segmentation ◽  
Model Approach
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