Jitendra Kumar

• Ph D
• Professor at Central University of Rajasthan

The media, speculators, investors, and governments throughout the world have all become increasingly interested in cryptocur- rencies in recent years. The price swings of cryptocurrencies are notoriously unstable and have a high level of volatility. This study focused on modeling that volatility of cryptocurrencies, the pur- pose of this study is t...

In present scenario, handling covariate/explanatory variable with the model is one of most important factor to study with the models. The main advantages of covariate are it’s dependency on past observations. So, study variable is modelled after explaining both on own past and past and future observation of covariates. Present paper deals estimatio...

The spread of coronavirus disease 2019 (COVID-19) in various countries varies in different manners, depending on the demographic and climatic conditions. This needs expediting better strategies to combat the novel coronavirus and also the emergence of new viral strains in the future. Our in-depth analysis suggests the spread of COVID-19 cases world...

To analyses the spread of the virus and take necessary measures to handle the outbreak, a better strategies is introduced on time to counter the virus and reduce the severity of the infection which can be a serious explanation for death within the present situation. In India, type of transmission was measured which was 66% due to local spread and 3...

As the outbreak of coronavirus disease 2019 (COVID-19) is continuously increasing in India, so epidemiological modeling of COVID-19 data is urgently required for administrative strategies. Time series and is capable to predict future observations by modeling the data based on past and present data. Here, we have modeled the epidemiological COVID-19...

Vector autoregressive (VAR) model is the most popular modeling tool in macroeconomics. This study considers a Bayesian framework for VAR(k) model with a structural break in the mean. The struc- tural change problem in VAR is of theoretical and practical importance in reference to the economic time series data. The main motivation of the study is...

In the present study, spatial compound growth rates were estimated to know the growth pattern and instability in the area, production, and productivity of sugarcane in major sugarcane growing states of India. A secondary time series data of major sugarcane producing states of India like Uttar Pradesh, Maharashtra, Karnataka, Tamil Nadu, Bihar, Andh...

A vast majority of the countries are under economic and health crises due to the current epidemic of coronavirus disease 2019 (COVID-19). The present study analyzes the COVID-19 using time series, an essential gizmo for knowing the enlargement of infection and its changing behavior, especially the trending model. We consider an autoregressive model...

In this paper, we develop an estimation procedure for an autoregressive model with polynomial time trend approximated by a spline function. Spline function has the advantage of approximating the non-linear time series in an appropriate degree of polynomial time trend model. For Bayesian parameter estimation, the conditional posterior distribution i...

This paper proposes a new panel autoregressive model named as merged panel autoregressive (M-PAR) model that explains the desired inferences of merger and acquisition (M&A) concept. Bayesian analysis of the M-PAR model is introduced to show the impact of the merger series in the acquire series and then obtain the Bayesian estimator under different...

Most economic time series, such as GDP, real exchange rate and banking series are irregular by nature as they may be affected by a variety of discrepancies, including political changes, policy reforms, import-export market instability, etc. When such changes entail serious consequences for time series modelling, various researchers manage this prob...

This paper provides a Bayesian setup for multiple regimes threshold autoregressive model with possible break points. A full conditional posterior distribution is obtained for all model parameters with considering suitable prior information. Threshold and break point vari-ables do not attain standard form distributions. To compute posterior distribu...

This paper deals with the problem of modelling time series data with structural breaks occur at multiple time points that may result in varying order of the model at every structural break. A flexible and generalized class of Autoregressive (AR) models with multiple structural breaks is proposed for modelling in such situations. Estimation of model...

As the outbreak of coronavirus disease 2019 (COVID-19) is continuously increasing in India, so epidemiological modeling of COVID-19 data is urgently required for administrative strategies. Time series and is capable to predict future observations by modeling the data based on past and present data. Here, we have modeled the epidemiological COVID-19...

A vast majority of the countries is under the economic and health crises due to the current epidemic of coronavirus disease 2019 (COVID-19). The present study analyzes the COVID-19 using time series, which is an essential gizmo for knowing the enlargement of infection and its changing behavior, especially the trending model. We have considered an a...

The objective of present study is to develop a time series model for handling the non-linear trend process using a spline function. Spline function is a piecewise polynomial segment concerning the time component. The main advantage of spline function is the approximation, non linear time trend, but linear time trend between the consecutive join poi...

All transmission disease depends on the transmission opportunity or medium like humans in COVID-19. Due to globalization and regular movement of people from one country to another, spread of COVID 19 reached to 208 countries till May 10, 2020. For any society health is major concern for humanity as well as administration. Any pandemic is declared a...

COVID-19 is an infectious disease, growth of which depends upon the linked stages of the epidemic, the average number of people one person can infect and the time it takes for those people to become infectious themselves. We have studied the COVID-19 time series to understand the growth behaviour of COVID-19 cases series. A structural break occurs...

There has been a history of outbreak of viruses from time to time like emergence of Ebola virus, H1N1, Astrovirus etc. Coronaviruses (CoVs) having RNA as their genetic material came into limelight during the outbreak of Severe Acute Respiratory Syndrome-CoV (SARS-CoV) in 2002 and further during the outbreak of Middle East respiratory syndrome coron...

Panel data consists of repeated observations over time on the same set of cross-sectional units and impact of covariate may influence the estimation and testing procedures. Present paper proposes a covariate panel autoregressive (C-PAR(1)) unit root tests and estimation considering structural break under Bayesian approach. We use Monte Carlo simula...

The idea about structural break in unit root hypothesis under time series model had received great amount of attention over many last decades. The importance of structural break in the mean had been comprehensively studied by Perron [15], Perron and Vogelsang [17], Zivot and Andrews [25] etc. This had also studied in considering of break in varianc...

Present manuscript proposed a shifted panel autoregressive (PAR) model through structural break assumption. A Bayesian estimation method is developed considering known from of prior information. Since expression of posterior distribution under different loss functions is in complicated form, therefore Gibbs sampler technique is used to obtain the c...

Univariate time series models are variable centric and exclude the information of other associated variables. However, panel data time series models are frequently applied when there are several similar variables under study or a variable is recorded over time at multiple places. Present work deals with Bayesian analysis of panel data unit root tes...

Present paper proposes an autoregressive time series model to study the behaviour of merger and acquire concept which is equally important as other available theories like structural break, de-trending etc. The main motivation behind newly proposed merged autoregressive (M-AR) model is to study the impact of merger in the parameters as well as acqu...

Present paper considers structural break in panel AR(1) model which allows instability in mean, variance and autoregressive coefficient. This model is extension of univariate model proposed by Meligkotsiduo et al. (2004) and review of existing panel data time series model considering break studied by Levin et al. (2002), Pesaran (2004), Bai (2010),...

This paper explores the effect of multiple structural breaks to estimate the parameters and test the unit root hypothesis in panel data time series model under Bayesian perspective. These breaks are present in both mean and error variance at the same time point. We obtain Bayes estimates for different loss function using conditional posterior distr...

Present study explored the unit root test for an autoregressive time series panel data model in the presence of stationary covariate under Bayesian framework. For testing of unit root hypothesis, posterior odds ratio has been derived with the help of posterior probability under informative and non-informative priors. It investigates the unit root t...

Time series modelling is very popular technique used in data science. Main motive of time series modelling is to know the data generating process and also get its parameters which depend on all the observations. There may be few observations which misinterpret the data and also influence the parameters, such type of observations are called Outlier....

The univariate time series models, in the case of unit root hypothesis, are more biased towards the acceptance of the Unit Root Hypothesis especially in a short time span. However, the panel data time series model is more appropriate in such situation. The Bayesian analysis of unit root testing for a panel data time series model is considered. An a...

A variable may be affected by some associated variables which may influence the estimation and testing procedures and also not much important to model separately, such types of variables are called covariates. The present paper dealt the covariate autoregressive (C-AR(1)) time series model with structural break in mean and variance under Bayesian f...

Data analysis has importance in real-time data. It is collective representation of all the facts of population or variables under interest. As velocity of data generating process (DGP) is third very important identity in Big Data prospective, so this cannot be managed through routine statistical tools of analysis. Big data time series recording is...

Present paper studies the panel data auto regressive (PAR) time series model for testing the unit root hypothesis. The posterior odds ratio (POR) is derived under appropriate prior assumptions and then empirical analysis is carried out for testing the unit root hypothesis of Net Asset Value of National Pension schemes (NPS) for different fund manag...

Present paper studies the panel data auto regressive (PAR) time series model for testing the unit root hypothesis. The posterior odds ratio (POR) is derived under appropriate prior assumptions and then empirical analysis is carried out for testing the unit root hypothesis of Net Asset Value of National Pension schemes (NPS) for different fund manag...

Insurance is a tool of future risk management by transferring the financial liabilities to the insurance companies if any uncertain loss occurs under the covered risks. For the coverage of equivalent financial loss upto maximum sum assured for which insured is liable to pay the premium. Time series is chronologically recorded data and multivariate...

The government is liable to help every citizen for employability, minimum facilitation of livelihood and almost all countries have shown their concern for. There are several ways of helping the poor and weaker section of society as well as all resident of a community/area or whole population and one of them subsidy. All subsidy schemes have two obj...

Present paper considers the Bayesian analysis of autoregressive time series model to identify the outlier for a trend stationary time series. We have obtained the posterior probability under different setups of AR(1) time series with linear time trend such as when series is contaminated by additive outlier, when series is trend stationary, and when...

The time varying observation recorded in chronological order is called time series. The extreme values are from the same time series model or appear because of some unobservable causes having serious implications in the esti-mation and inference. This change deviate the error more and the recorded observation is called outlier. The present paper de...

The financial institutions are serving to common man for social welfare and servicing in respect to demand but the fraud identity is doing financial crimes. Some major crimes happening are like money laundering, terrorist funding etc that should be prevented by financial institution and central bank of India. The existing KYC verification believes...

The presence of outliers in time series may have serious implications in the estimation of parameters or testing of hypothesis. The analysis of time series in the presence of outlier is not much explored using Bayesian framework. The present paper deals with the Bayesian analysis of an autoregressive model involving linear time trend and contaminat...

The crime is act not accepted by society and closely associated with geographical and demographic variables. Present study aims to identify the area suffering major crime acts named hotspots and the area has less crime named safe zone with respect to different heads of crime against body. The data collected by State Crime Record Bureau, Uttar Prade...

India is the second largest populated country in this world and has wealthy financial institutions; there are 21 public sector banks, 23 private banks and 36 foreign banks besides a large number of cooperative banks and rural banks etc. Indian banking is well matured and serves common men through multiple banking channels and products. At present,...

National Stock Exchange (NSE) is the largest exchange market in India in terms of turnover and declaring sixteen indices in reference of different sectors and company profile. Time series approach is most popular way of analyzing the economic series. Stationarity of time series is an important issue before estimating the parameters of data generati...

The present paper considers Bayesian analysis of an autoregressive model involving a partially linear time trend. The posterior odds ratio for testing the unit root hypothesis is obtained under appropriate prior assumptions. A simulation study is carried out with the objective of observing the impact of misspecifying the time trend on the posterior...