Heterogeneity and Microeconometrics Modelling

Presented at the 2005 Econometric Society World Congress Plenary Session on "Modelling Heterogeneity". We survey the treatment of heterogeneity in applied microeconometrics analyses. There are three themes. First, there is usually much more heterogeneity than empirical researchers allow for. Second, the inappropriate treatment of heterogeneity can lead to serious error when estimating outcomes of interest. Finally, once we move away from the traditional linear model with a single 'fixed effect', it is very difficult to account for heterogeneity and fit the data and maintain coherence with theory structures. The latter task is one for economists: "heterogeneity is too important to be left to the statisticians". The paper concludes with a report of our own research on dynamic discrete choice models that allow for maximal heterogeneity.heterogeneity; applied microeconometrics; fixed effects; dyanamic discrete choice

Dynamic binary outcome models with maximal heterogeneity

Most econometric schemes to allow for heterogeneity in micro behaviour have two drawbacks: they do not fit the data and they rule out interesting economic models. In this paper we consider the time homogeneous first order Markov (HFOM) model that allows for maximal heterogeneity. That is, the modelling of the heterogeneity does not impose anything on the data (except the HFOM assumption for each agent) and it allows for any theory model (that gives a HFOM process for an individual observable variable). `Maximal' means that the joint distribution of initial values and the transition probabilities is unrestricted. We establish necessary and sufficient conditions for the point identification of our heterogeneity structure and show how it depends on the length of the panel. A feasible ML estimation procedure is developed. Tests for a variety of subsidiary hypotheses such as the assumption that marginal dynamic effects are homogeneous are developed. We apply our techniques to a long panel of Danish workers who are very homogeneous in terms of observables. We show that individual unemployment dynamics are very heterogeneous, even for such a homogeneous group. We also show that the impact of cyclical variables on individual unemployment probabilities differs widely across workers. Some workers have unemployment dynamics that are independent of the cycle whereas others are highly sensitive to macro shocks.Discrete choice, Markov processes, Nonparametric identification, Unemployment dynamics

State Dependence and Heterogeneity in Health Using a Bias Corrected Fixed Effects Estimator

This paper considers the estimation of a dynamic ordered probit of self-assessed health status with two fixed effects: one in the linear index equation and one in the cut points. The two fixed effects allow us to robustly control for heterogeneity in unobserved health status and in reporting behaviour, even though we can not separate both sources of heterogeneity. The contributions of this paper are twofold. First it contributes to the literature that studies the determinants and dynamics of Self-Assessed Health measures. Second, this paper contributes to the recent literature on bias correction in nonlinear panel data models with fixed effects by applying and studying the finite sample properties of two of the existing proposals to our model. The most direct and easily applicable correction to our model is not the best one, and has important biases in our sample sizes.Dynamic ordered probit, fixed effects, self-assessed health, reporting bias, panel data, unobserved heterogeneity, incidental parameters, bias correction

Dynamic Binary Outcome Models with Maximal Heterogeneity

Most econometric schemes to allow for heterogeneity in micro behaviour have two drawbacks: they do not fit the data and they rule out interesting economic models. In this paper we consider the time homogeneous first order Markov (HFOM) model that allows for maximal heterogeneity. That is, the modelling of the heterogeneity does not impose anything on the data (except the HFOM assumption for each agent) and it allows for any theory model (that gives a HFOM process for an individual observable variable). 'Maximal' means that the joint distribution of initial values and the transition probabilities is unrestricted. We establish necessary and sufficient conditions for the point identification of our heterogeneity structure and show how it depends on the length of the panel. A feasible ML estimation procedure is developed. Tests for a variety of subsidiary hypotheses such as the assumption that marginal dynamic effects are homogeneous are developed. We apply our techniques to a long panel of Danish workers who are very homogeneous in terms of observables. We show that individual unemployment dynamics are very heterogeneous, even for such a homogeneous group. We also show that the impact of cyclical variables on individual unemployment probabilities differs widely across workers. Some workers have unemployment dynamics that are independent of the cycle whereas others are highly sensitive to macro shocks.discrete choice; Markov processes; nonparametric identification; unemployment dynamics

Estimation of Dynamic Nonlinear Random Effects Models with Unbalanced Panels

This paper presents estimation methods for dynamic nonlinear models with correlated random effects (CRE) when having unbalanced panels. Unbalancedness is often encountered in applied work and ignoring it in dynamic nonlinear models produces inconsistent estimates even if the unbalancedness process is completely at random. We show that selecting a balanced panel from the sample can produce efficiency losses or even inconsistent estimates of the average marginal effects. We allow the process that determines the unbalancedness structure of the data to be correlated with the permanent unobserved heterogeneity. We discuss how to address the estimation by maximizing the likelihood function for the whole sample and also propose a Minimum Distance approach, which is computationally simpler and asymptotically equivalent to the Maximum Likelihood estimation. Our Monte Carlo experiments and empirical illustration show that the issue is relevant. Our proposed solutions perform better both in terms of bias and RMSE than the approaches that ignore the unbalancedness or that balance the sample.The authors gratefully acknowledge research funding from the Spanish Ministry of Education, Grants ECO2012-31358, ECO2015-65204-P, ECO2017-87069-P, MDM 2014-0431 and Comunidad de Madrid, MadEco-CM (S2015/HUM-3444)

C/EBPβ Regulates TFAM Expression, Mitochondrial Function and Autophagy in Cellular Models of Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative disorder that results from the degeneration of dopaminergic neurons in the substantia nigra pars compacta (SNpc). Since there are only symptomatic treatments available, new cellular and molecular targets involved in the onset and progression of this disease are needed to develop effective treatments. CCAAT/Enhancer Binding Protein β (C/EBPβ) transcription factor levels are altered in patients with a variety of neurodegenerative diseases, suggesting that it may be a good therapeutic target for the treatment of PD. A list of genes involved in PD that can be regulated by C/EBPβ was generated by the combination of genetic and in silico data, the mitochondrial transcription factor A (TFAM) being among them. In this paper, we observed that C/EBPβ overexpression increased TFAM promoter activity. However, downregulation of C/EBPβ in different PD/neuroinflammation cellular models produced an increase in TFAM levels, together with other mitochondrial markers. This led us to propose an accumulation of non-functional mitochondria possibly due to the alteration of their autophagic degradation in the absence of C/EBPβ. Then, we concluded that C/EBPβ is not only involved in harmful processes occurring in PD, such as inflammation, but is also implicated in mitochondrial function and autophagy in PD-like conditions.This research was supported by the “MINECO” (SAF2017-85199-P to A.P.C.), UCM-Santander (PR44/21-29931 to J.A.M.-G.) and the Health Institute “Carlos III” ( PI18/00118 to E.C. and PI21/00183 to F.B.). CIBERNED is funded by the Health Institute “Carlos III”. F.B. is a Miguel Servet Fellow (CP20/0007)

Natural and Undetermined Sudden Death: Value of Post-Mortem Genetic Investigation

Background: Sudden unexplained death may be the first manifestation of an unknown inherited cardiac disease. Current genetic technologies may enable the unraveling of an etiology and the identification of relatives at risk. The aim of our study was to define the etiology of natural deaths, younger than 50 years of age, and to investigate whether genetic defects associated with cardiac diseases could provide a potential etiology for the unexplained cases. Methods and Findings: Our cohort included a total of 789 consecutive cases (77.19% males) <50 years old (average 38.6±12.2 years old) who died suddenly from non-violent causes. A comprehensive autopsy was performed according to current forensic guidelines. During autopsy a cause of death was identified in most cases (81.1%), mainly due to cardiac alterations (56.87%). In unexplained cases, genetic analysis of the main genes associated with sudden cardiac death was performed using Next Generation Sequencing technology. Genetic analysis was performed in suspected inherited diseases (cardiomyopathy) and in unexplained death, with identification of potentially pathogenic variants in nearly 50% and 40% of samples, respectively. Conclusions: Cardiac disease is the most important cause of sudden death, especially after the age of 40. Close to 10% of cases may remain unexplained after a complete autopsy investigation. Molecular autopsy may provide an explanation for a significant part of these unexplained cases. Identification of genetic variations enables genetic counseling and undertaking of preventive measures in relatives at risk

Genetic landscape of 6089 inherited retinal dystrophies affected cases in Spain and their therapeutic and extended epidemiological implications

Inherited retinal diseases (IRDs), defined by dysfunction or progressive loss of photoreceptors, are disorders characterized by elevated heterogeneity, both at the clinical and genetic levels. Our main goal was to address the genetic landscape of IRD in the largest cohort of Spanish patients reported to date. A retrospective hospital-based cross-sectional study was carried out on 6089 IRD affected individuals (from 4403 unrelated families), referred for genetic testing from all the Spanish autonomous communities. Clinical, demographic and familiar data were collected from each patient, including family pedigree, age of appearance of visual symptoms, presence of any systemic findings and geographical origin. Genetic studies were performed to the 3951 families with available DNA using different molecular techniques. Overall, 53.2% (2100/3951) of the studied families were genetically characterized, and 1549 different likely causative variants in 142 genes were identified. The most common phenotype encountered is retinitis pigmentosa (RP) (55.6% of families, 2447/4403). The most recurrently mutated genes were PRPH2, ABCA4 and RS1 in autosomal dominant (AD), autosomal recessive (AR) and X-linked (XL) NON-RP cases, respectively; RHO, USH2A and RPGR in AD, AR and XL for non-syndromic RP; and USH2A and MYO7A in syndromic IRD. Pathogenic variants c.3386G > T (p.Arg1129Leu) in ABCA4 and c.2276G > T (p.Cys759Phe) in USH2A were the most frequent variants identified. Our study provides the general landscape for IRD in Spain, reporting the largest cohort ever presented. Our results have important implications for genetic diagnosis, counselling and new therapeutic strategies to both the Spanish population and other related populations.This work was supported by the Instituto de Salud Carlos III (ISCIII) of the Spanish Ministry of Health (FIS; PI16/00425 and PI19/00321), Centro de Investigación Biomédica en Red Enfermedades Raras (CIBERER, 06/07/0036), IIS-FJD BioBank (PT13/0010/0012), Comunidad de Madrid (CAM, RAREGenomics Project, B2017/BMD-3721), European Regional Development Fund (FEDER), the Organización Nacional de Ciegos Españoles (ONCE), Fundación Ramón Areces, Fundación Conchita Rábago and the University Chair UAM-IIS-FJD of Genomic Medicine. Irene Perea-Romero is supported by a PhD fellowship from the predoctoral Program from ISCIII (FI17/00192). Ionut F. Iancu is supported by a grant from the Comunidad de Madrid (CAM, PEJ-2017-AI/BMD7256). Marta del Pozo-Valero is supported by a PhD grant from the Fundación Conchita Rábago. Berta Almoguera is supported by a Juan Rodes program from ISCIII (JR17/00020). Pablo Minguez is supported by a Miguel Servet program from ISCIII (CP16/00116). Marta Corton is supported by a Miguel Servet program from ISCIII (CPII17/00006). The funders played no role in study design, data collection, data analysis, manuscript preparation and/or publication decisions

Heterogeneity in dynamic discrete choice models.

We consider dynamic discrete choice models with heterogeneity in both the levels parameter and the state dependence parameter. We first analyse the purchase of full fat milk using a long consumer panel (T &gt; 100) on many households. The large T nature of the panel allows us to consistently estimate the parameters of each household separately. This analysis indicates strongly that the levels and the state dependence parameter are heterogeneous and dependently distributed. This empirical analysis motivates the theoretical analysis which considers the estimation of dynamic discrete choice models on short panels, allowing for more heterogeneity than is usually accounted for. The theoretical analysis considers a simple two state, first order Markov chain model without covariates in which both transition probabilities are heterogeneous. Using such a model we are able to derive small sample analytical results for bias and mean squared error. We discuss the maximum likelihood approach, a novel bias corrected version of the latter and we also develop a new estimator that minimises the integrated mean square error, which we term MIMSE. The attractions of the latter estimator are that it is very easy to compute, it is always identified and it converges to maximum likelihood as T becomes large so that it inherits all of the desirable large sample properties of MLE. Our main finding is that in almost all short panel contexts the MIMSE significantly outperforms the other two estimators in terms of mean squared error