Time-varying Explanatory Variables Research Articles

Unexpected Increase in Benzodiazepine Prescriptions Related to the Introduction of an Electronic Prescribing Tool: Evidence from Multicenter Hospital Data.

Electronic Prescribing tools (e-prescribing) have shown several benefits in terms of prescribing process adequacy and health care quality in hospital settings. We hypothesize however, that an undesired effect of digitalisation, due to the easier and faster prescribing process allowing patients to skip face-to-face conversations with patients and nurses, is that it could facilitate the prescription of medications at high risk of overuse or abuse, such as benzodiazepines (BZDs). We conducted a panel data study to investigate, the impact of the introduction of an e-prescribing system on new BZD prescriptions in hospitalised patients in a network of five teaching hospitals. During the observation period 1 July 2014–30 April 2019, 43,320 admissions were analysed. A fixed-effects model was adopted to estimate the effect of e-prescribing on new BZD prescriptions. E-prescribing implementation was associated with a significant increase of new BZD prescriptions: absolute +1.5%, and relative +43% (p < 0.001). The effect was similar in males and females (respectively, absolute +2.3%, relative +65% (p < 0.001); absolute +1.8%, relative +58% (p = 0.01)) and in patients ≥70 years old (absolute +1.6%, relative +59% (p < 0.001)). After controlling for time-varying explanatory variables, the implementation of the e-prescribing tool showed similar significant effects. E-prescribing implementation was associated with a significant increase of new in-hospital BZD prescriptions. For classes of drugs at risk of overuse or abuse, e-prescribing should be used cautiously, to minimize the risk of over-prescriptions. Further research in other settings and countries is needed to analyse causal interactions between e-prescribing and BZD prescriptions in the hospital setting, and to promote the ultimate goal of high-value care.

A control function approach to estimating switching regression models with endogenous explanatory variables and endogenous switching

We derive simple, multi-step estimation methods for a linear model with heterogeneous coefficients when there are both continuous and discrete endogenous explanatory variables. We consider both cross-sectional and panel data settings. When we extend our model to panel data, we use the Chamberlain–Mundlak device to allow heterogeneity to be correlated with time-varying explanatory variables. We apply the panel data methods we propose to estimation of a housing budget share equation where a homeownership dummy variable plays the role of the endogenous regime, and total expenditure plays the role of a continuous endogenous explanatory variable. We find that the constant coefficient model seems sufficient, and that the estimation methods we propose produce rather plausible estimates of the model parameters.

A New View on Panel Econometrics: Is Probit Feasible after All?

Mundlak (1978) proposed the addition of time averages to the usual panel equation in order to remove the fixed effects bias. We extend this Mundlak equation further by replacing the time-varying explanatory variables by the corresponding deviations from the averages over time, while keeping the time averages in the equation. It appears that regression on this extended equation provides simultaneously the within- and the in- between- estimator, while the pooled data estimator is a weighted average of the within and in-between estimator. In Section 3 we introduce observed and unobserved fixed effects In Section 4 we demonstrate that in this extended setup Probit - estimation on panel data sets does not pose a specific problem. The usual software will do. In Section 5 we give an empirical example.

Survival analysis of time‐to‐event data in respiratory health research studies

This article provides a review of techniques for the analysis of survival data arising from respiratory health studies. Popular techniques such as the Kaplan-Meier survival plot and the Cox proportional hazards model are presented and illustrated using data from a lung cancer study. Advanced issues are also discussed, including parametric proportional hazards models, accelerated failure time models, time-varying explanatory variables, simultaneous analysis of multiple types of outcome events and the restricted mean survival time, a novel measure of the effect of treatment.

Ambient Temperature During Gestation and Cold-Related Adult Mortality in a Swedish Cohort, 1915 to 2002

For all climatic regions, mortality due to cold exceeds mortality due to heat. We examine whether cold-related mortality in adulthood varies positively with unusually benign ambient temperature during gestation, using data on over 13,500 Swedes from the Uppsala Birth Cohort Study born in 1915-1929 and followed until 2003. We link daily thermometer temperatures in Uppsala (1914 to 2002) to subjects, from their estimated date of conception onwards. We estimate survival models with time-varying explanatory variables, focusing on the two leading causes of cold-related death in adulthood: ischaemic heart disease (IHD) and stroke. An increase in the prevalence of warm temperatures during gestation leads to a significantly higher rate of mortality due to cold-related IHD. However, we do not find such a relation for cold-related stroke mortality. Additional analyses show that birthweight percentile or gestational age do not mediate discovered findings. The IHD results indicate that ambient temperature during gestation – independent of birth month – modifies the relation between cold and adult mortality.

Avoiding biased versions of Wooldridge’s simple solution to the initial conditions problem

Wooldridge (2005) provided a simple and elegant solution to the initial conditions problem for dynamic nonlinear unobserved-effects models. His original auxiliary model includes the time-varying explanatory variables at each period. Unfortunately, a popular constrained version that includes within-means of the explanatory variables can be severely biased. We show that there are several ways to avoid this problem.

Which System GMM to Use? A Simulation Exercise to Identify the Ideal Estimator for Data of Different Sizes

Economists analyzing models using panel data often have to estimate the coefficients of time-invariant explanatory variables in their model. Although system Generalized Method of Moments (GMM) is the method of choice for many economists to estimate coefficients in their models, very little is known about which system GMM is best suited to provide valid estimates of coefficients of time-invariant variables under different sample sizes. I address this research question by running a series of Monte Carlo simulations on some of the more popular system GMMs (Arellano and Bover, 1995; Blundell and Bond, 1998). I find that Arellano and Bover (1995) system GMM provides the best estimates of the coefficient on time-invariant variable when the number of observation is large relative to the time periods available in the data, regardless of whether the time-varying explanatory variables are predetermined or exogenous. When the sample size is small relative to time period available in the dataset and the explanatory variables are predetermined, I find that the long-difference moment condition and estimation technique of Hahn et al (2007), combined with the time-mean moment condition of Arellano and Bover (1995) provides the best estimates of the coefficients on the time-invariant variable. I also find that this estimator provides valid estimates of the coefficients of time-varying variables under such circumstances.

Handling Underdispersion in Calibrating Safety Performance Function at Urban, Four-Leg, Signalized Intersections

Poisson basic assumption of equidispersion is often too much restrictive for crash count data; in fact this type of data has been found to often exhibit overdispersion. Underdispersion has been less commonly observed, and this is the reason why it has been less convenient to model directly than overdispersion. Overdispersion and underdispersion are not the only issues that can be a potential source of error in specifying statistical models and that can lead to biased crash-frequency predictions; these issues can derive from data properties (temporal and spatial correlation, time-varying explanatory variables, etc.) or from methodological approach (omitted variables, functional form selection, etc.). This article focuses on the potential of the Conway-Maxwell (COM-Poisson) model in handling underdispersion that arose in the development of a Safety Performance Function for urban four-leg signalized intersections; other issues, as temporal data correlation, have been intentionally eluded to test the best way of handling underdispersion. Results confirmed that the COM-Poisson model properly handled crash data set for which neither Poisson nor negative binomial model were able to account for dispersion phenomenon; they also showed that the COM-Poisson model provided a good statistical performance and a better goodness-of-fit than the quasi-Poisson and the traditional Poisson model.

STOCHASTIC DYNAMIC SIMULATION OF FRUIT ABORTION: A CASE STUDY OF SWEET PEPPER

Abortion of reproductive organs diminishes yields in many crops. In indeterminate greenhouse crops, alternating periods of fruit abortion and fruit set exist, resulting in fluctuations in fruit yield. Factors affecting the level of abortion are e.g., the supply and demand for assimilates (source and sink strength, respectively), temperature and cultivar. However, simulation of fruit abortion is still a weak part of crop simulation models. Variation in fruit abortion exists between plants, which results in differences in the timing and the number of set fruits. Therefore, simulating fruit abortion with variation could give more realistic simulation results. The probability of a fruit to abort should be related to factors like source strength and sink strength. The more favourable the circumstances are for fruit abortion, e.g., low source strength or high sink strength, the more likely it is that the fruit aborts. Survival analysis estimates parameters quantifying the influence of explanatory variables on the abortion rate. Time-varying explanatory variables can be used in the analysis. In a case study, we used survival analysis to analyse a data set with observations on flowering, fruit abortion and fruit harvest for sweet pepper. Source and sink strength were used as explanatory variables. The resulting equation determining the probability of abortion per day was implemented in a simple simulation model to simulate fruit set. The model output, as an average of 100 plants, showed similar timing in the fluctuations in fruit set as the observations, although the amplitude of the fluctuations was in some cases underestimated. The percentage fruit set was simulated correctly

Duration Dependence and Timevarying Variables in Discrete Time Duration Models

This paper considers estimation of a dynamic discrete choice model with second order state dependence in the presence of strictly exogenous time-varying explanatory variables. We propose a new method for estimating such models, and a small Monte Carlo study suggests that the method performs well in practice. The method is used to test for duration dependence in labour market spells for young people in France. The novelty in the application is that we are able to control for time-varying explanatory variables. In a discrete time duration model, duration dependence will result in second order state dependence, and the paper therefore also adds to the literature on estimation of duration models with unobserved heterogeneity.

Estimation of a Transformation Model with Truncation, Interval Observation and Time-Varying Covariates

Abrevaya (1999b) considered estimation of a transformation model in the presence of left-truncation. This paper observes that a cross-sectional version of the statistical model considered in Frederiksen, Honore, and Hu (2007) is a generalization of the model considered by Abrevaya (1999b) and the generalized model can be estimated by a pairwise comparison version of one of the estimators in Frederiksen, Honore, and Hu (2007). Specifically, our generalization will allow for discretized observations of the dependent variable and for piecewise constant time-varying explanatory variables.

A Strategy for Assessing the Impact of Time-Varying Family Risk Factors on High School Dropout

Human behavior is dynamic, influenced by changing situations over time. Yet the impact of the dynamic nature of important explanatory variables on outcomes has only recently begun to be estimated in developmental models. Using a risk factor perspective, this article demonstrates the potential benefits of regressing time-varying outcome measures on time-varying explanatory measures in longitudinal models. The authors apply event history analysis techniques to demonstrate a methodological strategy that accounts for changes over time in two family risk factors for high school graduation. In a sample of 686 low-income youth attending school in an urban district, the authors found that maternal employment status and income are significant predictors of high school graduation only when conceptualized and measured as time-varying influences. The implications for policy and practice and, from a methodological perspective, for the use of time-varying explanatory variables in event models are discussed.

The third birth in Sweden.

"This paper considers the formulation, estimation and interpretation of microdynamic models of fertility. Our model explains parity choices, sterility, childlessness, interbirth intervals and initiation of pregnancy within a unified framework. We develop a general methodology for estimating the determinants of transition times to births of different orders. Our procedure incorporates time-varying explanatory variables and unobservables. We present conditions that justify conventional formulae relating hazards to survivor functions when time-varying variables enter hazards. We also consider the validity of widely-used piecemeal estimation strategies that focus on one birth at a time. We consider methods for selecting a best model among a class of non-nested models. Two criteria are set forth and used to evaluate the determinants of third births in Sweden."

Efficient Estimation Using Panel Data

IN AN IMPORTANT RECENT PAPER, Hausman and Taylor (1981)-hereafter HT-considered the instrumental-variable estimation of a regression model using panel data, when the individual effects may be correlated with a subset of the explanatory variables. They provided a simple consistent estimator and an efficient estimator. More recently, Amemiya and MaCurdy (1986)-hereafter AM-have suggested an alternative estimator which is more efficient than the HT estimator, under certain conditions and given stronger assumptions than HT made. However, the relationship between the HT and AM papers is less clear than it might be, in part because of notational differences between the two papers. In this paper we clarify the relationship between the HT and AM estimators, and we show that the difference between these estimators lies in the treatment of the time-varying explanatory variables which are uncorrelated with the effects: HT use each such variable as two instruments (means and deviations from means), while AM use such variables as T + 1 instruments (as deviations from means and also separately for each of the T available time periods). This enables us to make clear the conditions under which the AM estimator is more efficient than the HT estimator. We also present each estimator in a form which allows it to be calculated using standard instrumental-variables (two-stage least squares) software. Following the AM path one step further, we then define a third (BMS) estimator which, under yet stronger assumptions, is more efficient than the AM estimator. Both HT and AM use as instruments the deviations from means of the time-varying variables which are correlated with the effects. A more efficient estimator may be obtained by using separately the (T - 1) linearly independent values of these deviations from individual means. Consistency requires that these be legitimate instruments, and whether this is so depends on why these time-varying variables are correlated with the effects. For example, if such correlation arises solely because of a time-invariant component which is removed in taking deviations from individual means, these instruments are legitimate.

The Impact of Exogenous Child Mortality on Fertility: A Waiting Time Regression with Dynamic Regressors

In this paper [the authors] develop and implement an econometric methodology estimating a family-specific exogenous component of life-expectancy in order to determine the responsiveness of fertility to exogenous changes in child mortality. [They] use a generalized waiting time regression model applied to length of life which is viewed as the output of a production process. [They] allow for family-specific heterogeneity in duration of life and for time-varying explanatory variables. The heterogeneity component retrieved from the production function estimation is used to estimate the impact of exogenous child mortality on a measure of fertility. The data concern 1938 children from 311 families included in the 1976 Malaysian Family Life Survey. This paper was previously published in Econometrica (Chicago Ill.) Vol. 51 No. 3 May 1983 pp. 731-49. (EXCERPT)