Decision Making under Uncertainty and Bounded Rationality

In an attempt to capture the complexity of the economic system many economists were led to the formulation of complex nonlinear rational expectations models that in many cases can not be solved analytically. In such cases, numerical methods need to be employed. In chapter one I review several numerical methods that have been used in the economic literature to solve non-linear rational expectations models. I provide a classification of these methodologies and point out their strengths and weaknesses. I conclude by discussing several approaches used to measure accuracy of numerical methods. In the presence of uncertainty, the multistage stochastic optimization literature has advanced the idea of decomposing a multiperiod optimization problem into many subproblems, each corresponding to a scenario. Finding a solution to the original problem involves aggregating in some form the solutions to each scenario and hence its name, scenario aggregation. In chapter two, I study the viability of scenario aggregation methodology for solving rational expectation models. Specifically, I apply the scenario aggregation method to obtain a solution to a finite horizon life cycle model of consumption. I discuss the characteristics of the methodology and compare its solution to the analytical solution of the model. A growing literature in macroeconomics is tweaking the unbounded rationality assumption in an attempt to find alternative approaches to modeling the decision making process, that may explain observed facts better or easier. Following this line of research, in chapter three, I study the impact of bounded rationality on the level of precautionary savings in a finite horizon life-cycle model of consumption. I introduce bounded rationality by assuming that the consumer does not have either the resources or the sophistication to consider all possible future events and to optimize accordingly over a long horizon. Consequently, he focuses on choosing a consumption plan over a short span by considering a limited number of possible scenarios. While under these assumptions the level of precautionary saving in many cases is below the level that a rational expectations model would predict, there are also parameterizations of the model for which the reverse is true

Tobacco Smoking Among School Personnel in Romania, Teaching Practices and Resources Regarding Tobacco Use Prevention

The study was conducted to collect baseline information on tobacco use, knowledge and attitudes of school personnel toward tobacco, to evaluate the existence and effectiveness of tobacco control policies in schools, and to assess training and materials requirements for implementing tobacco prevention and control interventions. All schools from Romania containing 6th, 7th, and 8th grade that contained 40 or more students were included in the sampling frame. 50 schools were sampled to participate in the study. All school personnel in the selected schools were eligible to participate in the survey. The survey procedure was designed to ensure confidentiality and voluntary participation. We found that more than two thirds of school personnel had ever smoked tobacco, with men significantly outnumbering women, and more than one thirds of them are current smokers. Also, it appears that school policies regarding tobacco use are not being translated into effective measures for implementation. Despite school policies, tobacco products could still be purchased either within school premises or close by. An important aspect brought out is the need to train teachers on these issues, and the importance of providing specific teaching and learning material on related topics. Only a small proportion of teachers had ever received such training. This is an area where is a need to build infrastructure as well as put capacity building measures in place. The study conducted with the methodology provided by CDC offers comparable data at international level and, also a national start point in the process of monitoring tobacco use among school personnel

Implications of Radiosensitizer and Radioprotector Factors in Refining the Dose-Volume Constraints and Radiobiological Models

Radiotherapy is a cornerstone of the modern treatment of many types of cancer, having both curative and palliative roles. It is estimated that more than half of cancer patients will need radiation therapy in the course of evolution. The goal of radiotherapy is to maximize tumor control, reducing adverse effects on normal tissues in close proximity at the same time. Improving the therapeutic ratio is the main goal of the efforts made to improve the technique and accuracy of the radiotherapy by using the targeting of the tumor volume with the help of the imaging guide and the dose conformation around the target volume. The use of the multi-leaf collimator (MLC) allowed a better coverage of the target volume in the irradiation field, thus reducing the unnecessary irradiation of healthy tissues. The use of radioprotective agents and radiosensitizers is another strategy to maximize the effect of radiotherapy. Recently, interest has focused on the design of irradiation protocols that exploit the differences in biology in terms of the response to irradiation between tumor cells and normal tissues

Delta-radiomics Entropy Based on Tumor Heterogeneity Concept – Response Predictor to Irradiation for Unresectable/recurrent Glioblastoma

Although adjuvant radiotherapy in combination with Temozolomide administration has clearly demonstrated the benefit in improving the prognosis of patients with multiforme glioblastoma, radiotherapy as only treatment or in combination with systemic treatment is one of the best supportive in unresectable cases. For recurrent cases, the salvage radiotherapy option (re-irradiation) can be chosen in carefully selected cases so that the benefit is greater than the toxicities. Radiomics, a new subdomain of artificial intelligence (AI), relies on advanced analysis in high-resolution medical imaging to establish diagnostic, prognostic and predictive models for clinical medicine. The variation of the delta-radiomics parameters analyzed within a tumor volume may be via tumor heterogeneity indirectly correlated with the response to treatment. The aim of the study is to propose a delta-radiomic based on entropy algorithm to allow the non-invasive pre-therapeutic identification of patients with unresectable or recurrent multiform glioblastoma who will benefit from irradiation and/or salvage re-irradiation

Cetuximab-Taxanes-Platinum-Fluorouracil/Capecitabine (C-TPF/C-TPX) – a Feasible Option for Recurrent HNSCC with Negative Prognostic Factors. Literature Review with a Case Presentation

Concurrent chemo-radiotherapy with Cisplatin is the standard treatment for locally advanced non-metastatic squamous cell carcinoma of the head and neck (HNSCC), but induction chemotherapy (IC) followed by chemo-irradiation, even controversial is a widely accepted option, especially in high-risk cases. A regimen including triple association (platinum-taxanes-fluorouracil) is generally considered superior in efficacy, but may be associated with severe toxicity. In the case of recurrence, the options are limited and the prognosis is generally unfavorable. Chemotherapy alone or in combination with an anti-EGFR monoclonal antibody (Cetuximab), immunotherapy or re-irradiation for selected cases are feasible options in loco-regional or metastatic relapse. We present a case of nasopharyngeal cancer (NPC), with negative prognostic and predictive factors multimodally treated with an intensive chemotherapy regimen associating Cetuximab with a median survival higher than the median value reported in most studies. Replacing 5 Floururacil with Capecitabine and Cisplatin with Carboplatin may be an option to increase treatment tolerance and should be evaluated in randomized trials. The use of induction chemotherapy as a “new standard” before radio-chemotherapy for cases with negative prognostic factors should also be the subject of future studies. Re-challenge with platinum is also an option that needs to be re-evaluated

Skin Cancers of the Head and Neck Region: the Real World Epidemiological and Therapeutic Data from the Cancer Registry of Dolj County

The majority of skin cancers of the head and neck are represented by basal cell carcinoma (BCC) and cutaneous squamosal cell carcinoma (CSCC), both non-melanoma skin cancers. Identified in the early stages, the cure rate is considered high. Sun protection and early identification of suspicious lesions are the optimal strategies for these cancers to be associated with higher response rates and favorable cosmetic results. Even if the incidence is lower, 10% to 25% of melanomas could also be identified in the head and neck region. For advanced stages or for cases ineligible for optimal surgical treatment, the multimodal approach including adjuvant radiotherapy, chemotherapy, biological therapy or immunotherapy must be decided in a multidisciplinary team. We set out to retrospectively evaluate the data of patients with skin tumors in the head and neck region included in the cancer registry of Dolj county between January 2000 and December 2019. Seventy-three patients were subsequently identified who met the inclusion criteria. The median age of the patients was 73 years (46 to 98). Forty-six cases of these were BCC, 15 CSCC cases, 1 adenoid cystic carcinoma case, 1 malignant melanoma case and one case without histopathological confirmation. The ratio between BCC and CSCC in our study is 3:1, in concordance with the ratio identified in the literature. The vast majority of cases come from the urban environment, surgery being the main treatment, especially for the early stages. Adjuvant radiotherapy was administered both in cases of BCC and epidermoid carcinoma. Adjuvant polychemotherapy, interferon therapy and re-irradiation have also been used. Considering the main risk factor, exposure to the sun, it is possible that the predominance of cases from cities is caused by underreporting of these types of cancer in rural communities where the main occupation is agriculture, associated with sun exposure, but also by a lower addressability or non-compliance with the inclusion in the oncological monitoring programs. The current existence of some modern oncological therapies, including immunotherapy for CSS and malignant melanoma, justifies a better monitoring and inclusion of these cases in multidisciplinary evaluation. Superficial radiotherapy, which has now become less accessible in our country, due to the implementation of radiological safety rules and the conversion of equipment from the former Soviet Union cobalt and superficial X-ray radiotherapy device to modern liniac accelerators focused on modern techniques radiotherapy is necessary to reduce the risk of recurrence in the case of resection with inadequate margins of non-melanoma skin tumors

Will the US Economy Recover in 2010? A Minimal Spanning Tree Study

We calculated the cross correlations between the half-hourly times series of the ten Dow Jones US economic sectors over the period February 2000 to August 2008, the two-year intervals 2002--2003, 2004--2005, 2008--2009, and also over 11 segments within the present financial crisis, to construct minimal spanning trees (MSTs) of the US economy at the sector level. In all MSTs, a core-fringe structure is found, with consumer goods, consumer services, and the industrials consistently making up the core, and basic materials, oil and gas, healthcare, telecommunications, and utilities residing predominantly on the fringe. More importantly, we find that the MSTs can be classified into two distinct, statistically robust, topologies: (i) star-like, with the industrials at the center, associated with low-volatility economic growth; and (ii) chain-like, associated with high-volatility economic crisis. Finally, we present statistical evidence, based on the emergence of a star-like MST in Sep 2009, and the MST staying robustly star-like throughout the Greek Debt Crisis, that the US economy is on track to a recovery.Comment: elsarticle class, includes amsmath.sty, graphicx.sty and url.sty. 68 pages, 16 figures, 8 tables. Abridged version of the manuscript presented at the Econophysics Colloquim 2010, incorporating reviewer comment

Growth Determinants Revisited

This paper revisits the cross-country growth empirics debate using a novel Limited Information Bayesian Model Averaging framework to address model uncertainty in the context of a dynamic growth model in panel data with endogenous regressors. Our empirical findings suggest that once model uncertainty is accounted for there is strong evidence that initial income, investment, life expectancy, and population growth are robustly correlated with economic growth. We also find evidence that debt, openness, and inflation are robust growth determinants. Overall, the set of our robust growth determinants differs from those identified by other studies that incorporate model uncertainty, but ignore dynamics and/or endogeneity. This underscores the importance of accounting for model uncertainty and endogeneity in the investigation of growth determinants.Economic growth;Economic conditions;Economic models;Human capital;probability, econometrics, probabilities, equation, growth model, logarithm, statistics, dynamic panel, difference equation, growth regressions, sample size, dynamic panel data, standard error, random error, growth regression, financial statistics, surveys, equations, cross section analysis, dynamic panel data models, hypothesis testing, linear regression, linear regression model, normal distribution, random variable, samples, cross section analyses, econometric analysis of cross section, cross-country growth regression, cross-country growth regressions, country growth regressions, bayes factors, dynamic panels, nested hypotheses, econometric estimates, country growth regression, instrumental variables, monte carlo simulations, computation, sample sizes, growth models, statistical significance, panel data econometrics, sampling, instrumental variable, regression model, survey, statistical theory, bayesian information criterion, dummy variables, number of variables, standard deviation, bayes factor, econometric analysis, correlation, difference ? equation

Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods

Bayesian Model Averaging (BMA) provides a coherent mechanism to address the problem of model uncertainty. In this paper we extend the BMA framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation results suggest that asymptotically our methodology performs well both in Bayesian model selection and averaging. In particular, LIBMA recovers the data generating process very well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to the true values. These findings suggest that our methodology is well suited for inference in dynamic panel data models with short time periods in the presence of endogenous regressors under model uncertainty.Economic models;probability, probabilities, econometrics, sample size, equation, dynamic panel, linear regression, dynamic panel data, statistics, bayes factors, random error, equations, dynamic panel data models, hypothesis testing, simulation results, sample sizes, normal distribution, dynamic panels, correlation, monte carlo simulations, random variable, linear regression model, sampling, gamma distribution, bayes factor, sample mean, correlations, nested hypotheses, regression models, cross-country growth regressions, samples, econometric study, number of regressors, bayesian analyses, country growth regressions, calculus, growth regression, covariance, predictions, prediction, regression model, growth regressions, random process, difference equation, factor analysis, linear regression models, bayesian analysis, forecasting, random variables, consistent estimate, linear models, probability distribution, experimental data, cross section analysis

Bayesian model averaging for dynamic panels with an application to a trade gravity model

<p>We extend the Bayesian Model Averaging (BMA) framework to dynamic panel data models with endogenous regressors using a Limited Information Bayesian Model Averaging (LIBMA) methodology. Monte Carlo simulations confirm the asymptotic performance of our methodology both in BMA and selection, with high posterior inclusion probabilities for all relevant regressors, and parameter estimates very close to their true values. In addition, we illustrate the use of LIBMA by estimating a dynamic gravity model for bilateral trade. Once model uncertainty, dynamics, and endogeneity are accounted for, we find several factors that are robustly correlated with bilateral trade. We also find that applying methodologies that do not account for either dynamics or endogeneity (or both) results in different sets of robust determinants.</p