Testing linearity against nonlinear moving average models

Lagrange multiplier (LM) test statistics are derived for testing a linear moving average model against an asymmetric moving average model and an LM type test against an additive smooth transition moving average model. The latter model is introduced in the paper. The small sample performance of the proposed tests are evaluated in a Monte Carlo study and compared to Wald and likelihood ratio statistics. The size properties of the Lagrange multiplier test are better than those of other tests.     

Family support services buffer the effect of child welfare notifications on child removals

Previous studies have found mixed results about the effects of family support services within the child protection system. The purpose of the study is to examine whether family support services are associated with the need for child removals at the community level. The material of the study consists of Finnish municipalities (N = 292) and their child protection indicators. Linear regression analysis was conducted to analyse the associations between the dependent variable (child removals) and the main predictors (child welfare notifications and family support services). It was found that family support services are associated with child removals. The more children there are in family support services, the more there are also child removals in a municipality. The key finding of the analysis is that a higher rather than lower proportion of clients in family support services buffers the increased effect of demand (child welfare notifications) on child removals better. In this sense, the demand for child removals is not only associated with the characteristics of children and families but may be partly explained by the role of family support services in a municipality. The present study underlines the significance of a system-level approach to child protection.     

Structural Change and the Kaldor Facts in a Growth Model With Relative Price Effects and Non‐Gorman Preferences

U.S. data reveal three facts: (1) the share of goods in total expenditure declines at a constant rate over time, (2) the price of goods relative to services declines at a constant rate over time, and (3) poor households spend a larger fraction of their budget on goods than do rich households. I provide a macroeconomic model with non‐Gorman preferences that rationalizes these facts, along with the aggregate Kaldor facts. The model is parsimonious and admits an analytical solution. Its functional form allows a decomposition of U.S. structural change into an income and substitution effect. Estimates from micro data show each of these effects to be of roughly equal importance.     

A Novel Time Series Approach to Bridge Coding Changes with a Consistent Solution Across Causes of Death

Revisions of the International Classification of Diseases (ICD) can lead to biases in cause-specific mortality levels and trends. We propose a novel time series approach to bridge ICD coding changes which provides a consistent solution across causes of death. Using a state space model with interventions, we performed time series analysis to cause-proportional mortality for ICD9 and ICD10 in the Netherlands (1979–2010), Canada (1979–2007) and Italy (1990–2007) on chapter level. A constraint was used to keep the sum of cause-specific interventions zero. Comparability ratios (CRs) were estimated and compared to existing bridge coding CRs for Italy and Canada. A significant ICD9 to ICD10 transition occurred among 13 cause of death groups in Italy, 7 in Canada and 3 in the Netherlands. Without the constraint, all-cause mortality after the classification change would be overestimated by 0.4 % (NL), 0.03 % (Canada) and 0.2 % (Italy). The time series CRs were in the same direction as the bridge coding CRs but deviated more from 1. A smooth corrected trend over the ICD-transition resulted from applying the time series approach. Comparing the time series CRs for Italy (2003), Canada (1999) and the Netherlands (1995) revealed interesting commonalities and differences. We demonstrated the importance of adding the constraint, the validity of our methodology and its advantages above earlier methods. Applying the method to more specific causes of death and integrating medical content to a larger extent is advocated.     

A comparison of mixed and minimax estimators of linear models

In this paper the stochastic properties of two estimators of linear models, mixed and minimax, based on different types of prior information, are compared using quadratic risk as the criterion for superiority. A necessary and sufficient condition for the minimax estimator to be superior to the comparable mixed estimator is derived as well as a simpler necessary but not sufficient condition.     

Superiority comparisons of heterogeneous linear estimators

In this paper conditions for strong and weak superiority of a heterogeneous linear estimator over another are derived. The general results are applied to some special cases: in particular, two restricted least squares estimators are compared using the superiority conditions obtained. The weak superiority criterion is used as a basis in forming a generalization of an optimal se-quence of tests (Anderson, 1962) for searching for the best estimator when the alternative linear restrictions form a nested se-quence of hypotheses. An application of this is the determination of the correct length of lag and appropriate degree of polynomial in the estimation of polynomial distributed lag models.