A time-space dynamic panel data model with spatial moving average errors

This paper focuses on the estimation and predictive performance of several estimators for the time-space dynamic panel data model with Spatial Moving Average Random Effects (SMA-RE) structure of the disturbances. A dynamic spatial Generalized Moments (GM) estimator is proposed which combines the approaches proposed by Baltagi, Fingleton and Pirotte (2014) and Fingleton (2008). The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a forecasting approach is proposed and a linear predictor is derived. Using Monte Carlo simulations, we compare the short-run and long-run e¤ects and evaluate the predictive effficiencies of optimal and various suboptimal predictors using the Root Mean Square Error (RMSE) criterion. Last, our approach is illustrated by an application in geographical economics which studies the employment levels across 255 NUTS regions of the EU over the period 2001-2012, with the last two years reserved for prediction

Health Care Expenditure and Income: a Global Perspective

Abstrac

Testing for inconsistencies in the estimation of UK capital structure determinants

This article analyses the determinants of the capital structure of 1054 UK companies from 1991 to 1997, and the extent to which the influence of these determinants are affected by time-invariant firm-specific heterogeneity. Comparing the results of pooled OLS and fixed effects panel estimation, significant differences in the results are found. While the OLS results are generally consistent with prior literature, the results of our fixed effects panel estimation contradict many of the traditional theories of the determinants of corporate financial structure. This suggests that results of traditional studies may be biased owing to a failure to control for firm-specific, time-invariant heterogeneity. The results of the fixed effects panel estimation find larger companies to have higher levels of both long-term and short-term debt than do smaller firms, profitability to be negatively correlated with the level of gearing, although profitable firms tend to have more short-term bank borrowing than less profitable firms, and tangibility to positively influence the level of short-term bank borrowing, as well as all long-term debt elements. However, the level of growth opportunities appears to have little influence on the level of gearing, other than short-term bank borrowing, where a significant negative relationship is observed

Robust Estimation for Linear Panel Data Models

In different fields of applications including, but not limited to, behavioral, environmental, medical sciences and econometrics, the use of panel data regression models has become increasingly popular as a general framework for making meaningful statistical inferences. However, when the ordinary least squares (OLS) method is used to estimate the model parameters, presence of outliers may significantly alter the adequacy of such models by producing biased and inefficient estimates. In this work we propose a new, weighted likelihood based robust estimation procedure for linear panel data models with fixed and random effects. The finite sample performances of the proposed estimators have been illustrated through an extensive simulation study as well as with an application to blood pressure data set. Our thorough study demonstrates that the proposed estimators show significantly better performances over the traditional methods in the presence of outliers and produce competitive results to the OLS based estimates when no outliers are present in the data set

Representative bureaucracy: does female police leadership affect gender-based violence arrests?

Representative bureaucracy theory postulates that passive representation leads to active representation of minority groups. This article investigates the passive representation of female police officers at leadership levels and the active representation of women vis-a-vis gender-based violence arrest rates in the UK. Much of the extant research on representative bureaucracy is located at street level, with evidence showing that discretionary power of minority bureaucrats can lead to active representation. This article is focused on leadership levels of a public bureaucracy. The empirical research is based upon a panel dataset of female police officers as an independent variable and gender-based violence arrest rates as a dependent variable. The analysis reveals that there is little evidence of active representation of women by female police leadership

Persistent threats to validity in single‐group interrupted time series analysis with a cross over design

Rationale, aims and objectivesThe basic single‐group interrupted time series analysis (ITSA) design has been shown to be susceptible to the most common threat to validity—history—the possibility that some other event caused the observed effect in the time series. A single‐group ITSA with a crossover design (in which the intervention is introduced and withdrawn 1 or more times) should be more robust. In this paper, we describe and empirically assess the susceptibility of this design to bias from history.MethodTime series data from 2 natural experiments (the effect of multiple repeals and reinstatements of Louisiana’s motorcycle helmet law on motorcycle fatalities and the association between the implementation and withdrawal of Gorbachev’s antialcohol campaign with Russia’s mortality crisis) are used to illustrate that history remains a threat to ITSA validity, even in a crossover design.ResultsBoth empirical examples reveal that the single‐group ITSA with a crossover design may be biased because of history. In the case of motorcycle fatalities, helmet laws appeared effective in reducing mortality (while repealing the law increased mortality), but when a control group was added, it was shown that this trend was similar in both groups. In the case of Gorbachev’s antialcohol campaign, only when contrasting the results against those of a control group was the withdrawal of the campaign found to be the more likely culprit in explaining the Russian mortality crisis than the collapse of the Soviet Union.ConclusionsEven with a robust crossover design, single‐group ITSA models remain susceptible to bias from history. Therefore, a comparable control group design should be included, whenever possible.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136538/1/jep12668.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136538/2/jep12668_am.pd

Interactions between cigarette and alcohol consumption in rural China

The objective of this paper is to analyze interdependencies between cigarette and alcohol consumption in rural China, using panel data for 10 years (1994–2003) for rural areas of 26 Chinese provinces. There have been many studies in which cigarette and alcohol consumption have been considered separately but few to date for China on interactions between the consumption of these two products. Taxes are often recommended as a tool to reduce alcohol and cigarette consumption. If cigarettes and alcohol are complements, taxing one will reduce the consumption of both and thus achieve a double public health dividend. However, if they are substitutes, taxing one will induce consumers to increase consumption of the other, offsetting the public health benefits of the tax. Our results indicate that the demands for both cigarettes and alcohol are very sensitive to the price of alcohol, but not to the price of cigarettes or to income. This suggests that taxes on alcohol can have a double dividend. On the other hand, an increase in cigarette taxes may not be effective in curbing cigarette or alcohol consumption in rural China

Robust tests for time-invariant individual heterogeneity versus dynamic state dependence

We derive tests for persistent effects in a general linear dynamic panel data context. Two sources of persistent behavior are considered: time-invariant unobserved factors (captured by an individual random effect) and dynamic persistence or “state dependence” (captured by autoregressive behavior). We will use a maximum likelihood framework to derive a family of tests that help researchers learn whether persistence is due to individual heterogeneity, dynamic effect, or both. The proposed tests have power only in the direction they are designed to perform, that is, they are locally robust to the presence of alternative sources of persistence, and consequently, are able to identify which source of persistence is active. A Monte Carlo experiment is implemented to explore the finite sample performance of the proposed procedures. The tests are applied to a panel data series of real GDP growth for the period 1960–2005

Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions

In any economic analysis, regions or municipalities should not be regarded as isolated spatial units, but rather as highly interrelated small open economies. These spatial interrelations must be considered also when the aim is to forecast economic variables. For example, policy makers need accurate forecasts of the unemployment evolution in order to design short- or long-run local welfare policies. These predictions should then consider the spatial interrelations and dynamics of regional unemployment. In addition, a number of papers have demonstrated the improvement in the reliability of long-run forecasts when spatial dependence is accounted for. We estimate a heterogeneouscoefficients dynamic panel model employing a spatial filter in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment, as well as a spatial vector-autoregressive (SVAR) model. We compare the short-run forecasting performance of these methods, and in particular, we carry out a sensitivity analysis in order to investigate if different number and size of the administrative regions influence their relative forecasting performance. We compute short-run unemployment forecasts in two countries with different administrative territorial divisions and data frequency: Switzerland (26 regions, monthly data for 34 years) and Spain (47 regions, quarterly data for 32 years)