scholarly journals MCMC and GLMs for estimating regression parameters

2019 ◽  
Vol 2 (1) ◽  
pp. 46-55
Author(s):  
Mahmoud ELsayed ◽  
Amr Soliman
Keyword(s):  
Linear Regression ◽  
Decision Maker ◽  
Gibbs Sampling ◽  
Posterior Distribution ◽  
R Package ◽  
Investment Strategy ◽  
Mcmc Method ◽  
Content Type ◽  
Regression Parameters ◽  
Alternative Techniques

Purpose The purpose of this study is to estimate the linear regression parameters using two alternative techniques. First technique is to apply the generalized linear model (GLM) and the second technique is the Markov Chain Monte Carlo (MCMC) method. Design/methodology/approach In this paper, the authors adopted the incurred claims of Egyptian non-life insurance market as a dependent variable during a 10-year period. MCMC uses Gibbs sampling to generate a sample from a posterior distribution of a linear regression to estimate the parameters of interest. However, the authors used the R package to estimate the parameters of the linear regression using the above techniques. Findings These procedures will guide the decision-maker for estimating the reserve and set proper investment strategy. Originality/value In this paper, the authors will estimate the parameters of a linear regression model using MCMC method via R package. Furthermore, MCMC uses Gibbs sampling to generate a sample from a posterior distribution of a linear regression to estimate parameters to predict future claims. In the same line, these procedures will guide the decision-maker for estimating the reserve and set proper investment strategy.

scholarly journals On Model Comparison: Application of Savage-Dickey Density Ratio to Bayes Factor

2020 ◽  
pp. 25-46
Author(s):  
Olawale B. Akanbi ◽  
Olusanya E. Olubusoye ◽  
Samuel A. Babatunde
Keyword(s):  
Linear Regression ◽  
Gibbs Sampling ◽  
Posterior Distribution ◽  
Model Comparison ◽  
Bayes Factor ◽  
Density Ratio ◽  
Sampling Technique ◽  
Competing Models ◽  
Normal Gamma Prior ◽  
Joint Posterior Distribution

Bayes factor is a major Bayesian tool for model comparison especially when the model priors are the same. In this paper, the Savage-Dickey Density Ratio (SDDR) is used to derive the Bayes factor to select a model from two competing models under consideration in a normal linear regression with an independent normal-gamma prior. The Gibbs sampling technique for the joint posterior distribution with equal prior precision for both the unrestricted and restricted models is used to obtain the model estimates. The result shows that the Bayes factor gave more support to the unrestricted model against the restricted and was consistent irrespective of changes in sample size.

Model of the influence of Internet finance on monetary policy based on gibbs sampling and vector autoregression

2020 ◽  
pp. 1-11
Author(s):  
Hui Wang ◽  
Huang Shiwang
Keyword(s):  
Monetary Policy ◽  
Gibbs Sampling ◽  
Posterior Distribution ◽  
Estimation Method ◽  
Financial Supervision ◽  
Var Model ◽  
Model Parameters ◽  
State Variables ◽  
Frequency Data ◽  
Internet Finance

The various parts of the traditional financial supervision and management system can no longer meet the current needs, and further improvement is urgently needed. In this paper, the low-frequency data is regarded as the missing of the high-frequency data, and the mixed frequency VAR model is adopted. In order to overcome the problems caused by too many parameters of the VAR model, this paper adopts the Bayesian estimation method based on the Minnesota prior to obtain the posterior distribution of each parameter of the VAR model. Moreover, this paper uses methods based on Kalman filtering and Kalman smoothing to obtain the posterior distribution of latent state variables. Then, according to the posterior distribution of the VAR model parameters and the posterior distribution of the latent state variables, this paper uses the Gibbs sampling method to obtain the mixed Bayes vector autoregressive model and the estimation of the state variables. Finally, this article studies the influence of Internet finance on monetary policy with examples. The research results show that the method proposed in this article has a certain effect.

Heuristic biases in investment decision-making and perceived market efficiency

2018 ◽  
Vol 10 (1) ◽  
pp. 85-110 ◽  
Author(s):  
Syed Zulfiqar Ali Shah ◽  
Maqsood Ahmad ◽  
Faisal Mahmood
Keyword(s):  
Financial Markets ◽  
Market Efficiency ◽  
Negative Impact ◽  
Investment Decisions ◽  
Sampling Technique ◽  
Investment Strategy ◽  
Individual Investors ◽  
Content Type ◽  
Specific Investment ◽  
The Relationship

Purpose This paper aims to clarify the mechanism by which heuristics influences the investment decisions of individual investors, actively trading on the Pakistan Stock Exchange (PSX), and the perceived efficiency of the market. Most studies focus on well-developed financial markets and very little is known about investors’ behaviour in less developed financial markets or emerging markets. The present study contributes to filling this gap in the literature. Design/methodology/approach Investors’ heuristic biases have been measured using a questionnaire, containing numerous items, including indicators of speculators, investment decisions and perceived market efficiency variables. The sample consists of 143 investors trading on the PSX. A convenient, purposively sampling technique was used for data collection. To examine the relationship between heuristic biases, investment decisions and perceived market efficiency, hypotheses were tested by using correlation and regression analysis. Findings The paper provides empirical insights into the relationship of heuristic biases, investment decisions and perceived market efficiency. The results suggest that heuristic biases (overconfidence, representativeness, availability and anchoring) have a markedly negative impact on investment decisions made by individual investors actively trading on the PSX and on perceived market efficiency. Research limitations/implications The primary limitation of the empirical review is the tiny size of the sample. A larger sample would have given more trustworthy results and could have empowered a more extensive scope of investigation. Practical implications The paper encourages investors to avoid relying on heuristics or their feelings when making investments. It provides awareness and understanding of heuristic biases in investment management, which could be very useful for decision makers and professionals in financial institutions, such as portfolio managers and traders in commercial banks, investment banks and mutual funds. This paper helps investors to select better investment tools and avoid repeating expensive errors, which occur due to heuristic biases. They can improve their performance by recognizing their biases and errors of judgment, to which we are all prone, resulting in a more efficient market. So, it is necessary to focus on a specific investment strategy to control “mental mistakes” by investors, due to heuristic biases. Originality/value The current study is the first of its kind, focusing on the link between heuristics, individual investment decisions and perceived market efficiency within the specific context of Pakistan.

Portfolio revision and optimal diversification strategy choices

2014 ◽  
Vol 10 (4) ◽  
pp. 537-564
Author(s):  
Mourad Mroua ◽  
Fathi Abid
Keyword(s):  
Transaction Costs ◽  
Tracking Error ◽  
Investment Strategy ◽  
Selection Strategy ◽  
Revision Procedure ◽  
Content Type ◽  
Efficient Frontiers ◽  
Statistical Inferences ◽  
Diversification Strategies ◽  
International Portfolios

Purpose – Since equity markets have a dynamic nature, the purpose of this paper is to investigate the performance of a revision procedure for domestic and international portfolios, and provides an empirical selection strategy for optimal diversification from an American investor's point of view. This paper considers the impact of estimation errors on the optimization processes in financial portfolios. Design/methodology/approach – This paper introduces the concept of portfolio resampling using Monte Carlo method. Statistical inferences methodology is applied to construct the sample acceptance regions and confidence regions for the resampled portfolios needing revision. Tracking error variance minimization (TEVM) problem is used to define the tracking error efficient frontiers (TEEF) referring to Roll (1992). This paper employs a computation method of the periodical after revision return performance level of the dynamic diversification strategies considering the transaction cost. Findings – The main finding is that the global portfolio diversification benefits exist for the domestic investors, in both the mean-variance and tracking error analysis. Through TEEF, the dynamic analysis indicates that domestic dynamic diversification outperforms international major and emerging diversification strategies. Portfolio revision appears to be of no systematic benefit. Depending on the revision of the weights of the assets in the portfolio and the transaction costs, the revision policy can negatively affect the performance of an investment strategy. Considering the transaction costs of portfolios revision, the results of the return performance computation suggest the dominance of the global and the international emerging markets diversification over all other strategies. Finally, an assessment between the return and the cost of the portfolios revision strategy is necessary. Originality/value – The innovation of this paper is to introduce a new concept of the dynamic portfolio management by considering the transaction costs. This paper investigates the performance of a revision procedure for domestic and international portfolios and provides an empirical selection strategy for optimal diversification. The originality of the idea consists on the application of a new statistical inferences methodology to define portfolios needing revision and the use of the TEVM algorithm to define the tracking error dynamic efficient frontiers.

Earnings management when firms face mandatory contributions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yiyi Qin ◽  
Jun Cai ◽  
Steven Wei
Keyword(s):  
Earnings Management ◽  
Negative Impact ◽  
Investment Strategy ◽  
Fiscal Year ◽  
Management Behavior ◽  
Indicator Variable ◽  
Stock Market Returns ◽  
Content Type ◽  
Early Results ◽  
Reported Earnings

PurposeIn this paper, we aim to answer two questions. First, whether firms manipulate reported earnings via pension assumptions when facing mandatory contributions. Second, whether firms alter their earnings management behavior when the Financial Accounting Standard Board (FASB) mandates disclosure of pension asset composition and a description of investment strategy under SFAS 132R.Design/methodology/approachOur basic approach is to run linear regressions of firm-year assumed returns on the log of pension sensitivity measures, controlling for current and lagged actual returns from pension assets, fiscal year dummies and industry dummies. The larger the pension sensitivity ratios, the stronger the effects from inflated ERRs on reported earnings. We confirm the early results that the regression slopes are positive and highly significant. We construct an indicator variable DMC to capture the mandatory contributions firms face and another indicator variable D132R to capture the effect of SFAS 132R. DMC takes the value of one for fiscal years during which an acquisition takes place and zero otherwise. D132R takes the value of one for fiscal years after December 15, 2003 and zero otherwise.FindingsOur sample covers the period from June 1992 to December 2017. Our key results are as follows. The estimated coefficient (t-statistic) on DMC is 0.308 (6.87). Firms facing mandatory contributions tend to set ERRs at an average 0.308% higher. The estimated coefficient (t-statistic) on D132R is −2.190 (−13.70). The new disclosure requirement under SFAS 132R constrains all firms to set ERRs at an average 2.190% lower. The estimate (t-statistic) on the interactive term DMA×D132R is −0.237 (−3.29). When mandatory contributions happen during the post-SFAS 132R period, firms tend to set ERRs at 0.237% lower than they would do otherwise in the pre-SFAS 132R period.Originality/valueWhen firms face mandatory contributions, typically firm experience negative stock market returns. We examine whether managers manage earnings to mitigate such negative impact. We find that firms inflate assumed returns on pension assets to boost their reported earnings when facing mandatory contributions. We also find that managers alter earnings management behavior, in the case of mandatory contributions, following the introduction of new pension disclosure standards under SFAS 132R that become effective on December 15, 2003. Under the new SFAS 132R requirement, firms need to disclose asset allocation and describe investment strategies. This imposes restrictions on managers' discretion in making ERR assumptions, since now the composition of pension assets is a key determinant of the assumed expected rate of return on pension assets. Firms need to justify their ERRs with their asset allocations.

A new age-period-cohort model for cancer surveillance research

2018 ◽  
Vol 28 (10-11) ◽  
pp. 3363-3391 ◽  
Author(s):  
Philip S Rosenberg
Keyword(s):  
Group Versus ◽  
R Package ◽  
New Age ◽  
Variable Number ◽  
Rate Of Change ◽  
Cancer Surveillance ◽  
Cohort Model ◽  
Cross Sectional ◽  
New Model ◽  
Regression Parameters

We develop a new age-period-cohort model for cancer surveillance research; the theory and methods are broadly applicable. In the new model, cohort deviations are weighted to account for the variable number of periods that each cohort is observed. Weighting ensures that the fitted rates can be naturally expressed as a function of age × a function of period × a function of cohort. Furthermore, the age, period, and cohort deviations are split into orthogonal quadratic components plus higher-order terms. These decompositions enable powerful combination significance tests of first- and second-order age, period, and cohort effects. The regression parameters of the orthogonal quadratic polynomials (global curvatures) quantify how fast on average the trends in the rates are changing. Importantly, the global curvature for cohort determines the least squares slope of the expected annual percentage changes by age group versus age (local drifts), thereby providing a powerful one-degree-of-freedom test of age-period interactions. We introduce new estimable functions, including age gradients that quantify the rate of change of the longitudinal and cross-sectional age curves at each attained age, and gradient shifts that quantify how the cross-sectional age trend varies by period. We illustrate the new model using nationally representative multiple myeloma incidence. Comprehensive proofs are given in technical appendices. We provide an R package.

Modeling schedule overrun and cost escalation percentages of highway projects using fuzzy approach

2017 ◽  
Vol 24 (5) ◽  
pp. 809-827 ◽  
Author(s):  
Ahmed Ebrahim Abu El-Maaty ◽  
Amr M. El-Kholy ◽  
Ahmed Yousry Akal
Keyword(s):  
Linear Regression ◽  
Design Methodology ◽  
Fuzzy Theory ◽  
Linear Regression Analysis ◽  
Fuzzy Approach ◽  
Analysis Method ◽  
Highway Projects ◽  
Content Type ◽  
Theoretical And Numerical Analysis ◽  
Better Than

Purpose Modeling represents the art of translating problems from an application area into tractable mathematical formulations whose theoretical and numerical analysis provides insight, answers and guidance useful for the originating application. The purpose of this paper is to determine the causal causes of schedule overrun and cost escalation of highway projects in Egypt in order to be used as independents variables in mathematical models for predicting the percentages of schedule overrun and cost escalation of such projects in Egypt. Design/methodology/approach A survey of a randomly selected samples yielded responses from 40 owners, 15 consultants and 56 contractors. The survey includes 38 schedule overrun factors and 26 cost escalation factors. The effectiveness degree of the identified factors has been identified by the triangle fuzzy approach. Findings The results of the survey show that “contractor’s technical staff is insufficient and ineligible to accomplish the project” is the most important cause of schedule overrun, while the major cause of cost escalation is inadequate preparation of the project concerning planning and execution. Originality/value The main contribution of this study is predicting the percentages of schedule overrun and cost escalation of highway projects in Egypt. Through the application of the linear regression analysis method and statistical fuzzy theory, four predictive models have been developed and it has been noted that the linear regression-based model shows prediction accuracy better than statistical fuzzy-based model in predicting percentages of schedule overrun and cost escalation.

scholarly journals New Algorithms for L1 Norm Regression

2019 ◽  
Vol 1 (1) ◽  
pp. 1-18
Author(s):  
Bijan Bidabad
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Simple Linear Regression ◽  
L1 Norm ◽  
Median Problem ◽  
Regression Parameters ◽  
Multiple Regression Models ◽  
Weighted Median ◽  
Weighted Median Problem ◽  
New Algorithms

In this paper, we propose four algorithms for L1 norm computation of regression parameters, where two of them are more efficient for simple and multiple regression models.  However, we start with restricted simple linear regression and corresponding derivation and computation of the weighted median problem. In this respect, a computing function is coded.  With discussion on the m parameters model, we continue to expand the algorithm to include unrestricted simple linear regression, and two crude and efficient algorithms are proposed. The procedures are then generalized to the m parameters model by presenting two new algorithms, where the algorithm 4 is selected as more efficient. Various properties of these algorithms are discussed.

The big picture on supply chain integration – insights from a bibliometric analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Herbert Kotzab ◽  
Ilja Bäumler ◽  
Paul Gerken
Keyword(s):  
Supply Chain ◽  
Web Of Science ◽  
Phase 1 ◽  
R Package ◽  
Research Area ◽  
Supply Chain Integration ◽  
Research Front ◽  
Academic Journals ◽  
Content Type ◽  
Big Picture

Purpose Integration is a key element of supply chain management (SCM) and a lot of research has been executed within the field of supply chain integration (SCI). The purpose of this paper is to particularly identify the intellectual research front and foundation of SCI and how they developed over time. Design/methodology/approach The authors examined more than 1,700 peer-reviewed academic papers that were published between 1995 and 2019 in nearly 40 relevant peer-reviewed academic journals (all indexed in Web of Science). The authors analysed the structure of more than 55,000 individual references with the R-package bibliometrix and used VOSviewer for visualization. Findings The SCI research front is characterized by papers that show the effects of SCI on the firm performance, the consequences of SCI on SCM in general and present the enablers of SCI. The research front is embedded within the resource-based, transaction cost and contingency theory. The intellectual foundation refers to conceptual modelling, definitional clarification and integration dimensions. The research identifies Frohlich and Westbrook’s (2001) paper as the central reference for this research area. The dynamic evolution of the intellectual foundation of SCI changed from theorising in Phase 1 (1995–2006) towards empirical testing in Phase 2 (2007–2019). Research limitations/implications The results refer to the SCI discussion within a preselected number of peer-reviewed academic journals and to the data quality as provided by the Web of Science. Originality/value The study explored the research front and intellectual foundation of SCI. It reveals the most important papers and journals of this area by using bibliometric tools such as bibliometrix, biblioshiny and VOSviewer. The paper shows trends in research themes, theories and methodological developments.

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