A Test of Non Null Hypothesis for Linear Trends in Proportions

Usual tests for trends stand under null hypothesis. This article presents a test of non null hypothesis for linear trends in proportions. A weighted least squares method is used to estimate the regression coefficient of proportions. A non null hypothesis is defined as its expectation equal to a prescribed regression coefficient margin. Its variance is used to construct an equation of basic relationship for linear trends in proportions along the asymptotic normal method. Then follow derivations for the sample size formula, the power function, and the test statistic. The expected power is obtained from the power function and the observed power is exhibited by Monte Carlo method. It reduces to the classical test for linear trends in proportions on setting the margin equal to zero. The agreement between the expected and the observed power is excellent. It is the non null hypothesis test matched with the classical test and can be applied to assess the clinical significance of trends among several proportions. By contrast, the classical test is restricted in testing the statistical significance. A set of data from a website is used to illustrate the methodology.     

Penalized least squares smoothing of two-dimensional mortality tables with imposed smoothness

This paper presents a method to estimate mortality trends of two-dimensional mortality tables. Comparability of mortality trends for two or more of such tables is enhanced by applying penalized least squares and imposing a desired percentage of smoothness to be attained by the trends. The smoothing procedure is basically determined by the smoothing parameters that are related to the percentage of smoothness. To quantify smoothness, we employ an index defined first for the one-dimensional case and then generalized to the two-dimensional one. The proposed method is applied to data from member countries of the OECD. We establish as goal the smoothed mortality surface for one of those countries and compare it with some other mortality surfaces smoothed with the same percentage of two-dimensional smoothness. Our aim is to be able to see whether convergence exists in the mortality trends of the countries under study, in both year and age dimensions.     

Quasi-likelihood ratio tests for cointegration,cobreaking, and cotrending

We consider a set of variables with two types of nonstationary features, stochastic trends and broken linear trends. We develop tests that can determine whether there is a linear combination of these variables under which the nonstationary features can be canceled out. The first test can determine whether stochastic trends can be eliminated and thus whether cointegration holds, regardless of whether structural breaks in linear trends are eliminated. The second test can determine whether both stochastic trends and breaks in linear trends are simultaneously removed and thus whether cointegration and cobreaking simultaneously hold. The third test can determine whether not only breaks in linear trends but also linear trends themselves are eliminated along with stochastic trends and thus whether both cointegration and cotrending hold.     

A score test for monotonic trend in hazard rates for grouped survival data in stratified populations

Models for monotone trends in hazard rates for grouped survival data in stratified populations are introduced, and simple closed form score statistics for testing the significance of these trends are presented. The test statistics for some of the models understudy are shown to be independent of the assumed form of the function which relates the hazard rates to the sets of monotone scores assigned to the time intervals. The procedure is applied to test monotone trends in the recovery rates of erythematous response among skin cancer patients and controls that have been irradiated with a ultraviolent challenge.     

Systematic designs permitting deteramination of trends in several attributes each unaffected by the others

A systematc design using orthogonal linear contrasts permits the estimation of real linear trends in data unaffected by linear trend in an instrument on which the data are measured. The method is extended to the estimation of more than two linear trends and to trends of both first and higter degrees. Some estimated efficiencies under random nonorthogonal contrasts are given for comparison. Sample calculations on artificial data illustrate the improvement obtained with these desings. A summary of the results obtained on a large body of real data is presented.     

The demographic transformation--a challenge to labor market and social policies

The implications of future demographic trends for labor market and social policies in the Federal Republic of Germany are discussed. The focus is on how trends such as demographic aging and declining fertility will affect the labor market, employment, and old-age security.     

Effects of future population trends on the labor market--analyses based on projections prepared by the Institute for Research on the Labor Market and Occupations

The effects of future population trends, such as demographic aging, declining fertility, and changes in migration, on the labor market in the Federal Republic of Germany are analyzed up to the year 2000. The study is based on projections prepared by the Institute for Research on the Labor Market and Occupations. Topics discussed include demographic trends as a cause of current unemployment, labor market phases and demographic trends since 1950, the projection model used, age-specific projections of the potential labor force, and labor market projections.     

Estimating time to detect time trends in continuous cropping

SUMMARY In long-term field trials comparing different sequences of crops and husbandry practices, the identification and understanding of trends in productivity over time is an important issue of sustainable crop production. This paper presents a statistical technique for the estimation of time trends in yield variables of a seasonal annual crop under continuous cropping. The estimation procedure incorporates the correlation structure, which is assumed to follow first-order autocorrelation in the errors that arise over time on the same plot. Because large differences in annual rainfall have a major effect on crop performance, rainfall has been allowed for in the estimation of the time trends. Expressions for the number of years (time) required to detect statistically significant time trends have been obtained. Illustrations are based on a 7-year data set of grain and straw yields from a trial in northern Syria. Although agronomic interpretation is not intended in this paper, the barley yield data indicated that a significant time trend can apparently be detected even in a suboptimal data set of 7 years' duration.     

Linear and segmented trends in sea surface temperature data

This paper deals with the analysis of the MET Office Hadley Centre's sea surface temperature data set (HadSST3) by using long-range dependence techniques. We incorporate linear and segmented trends using fractional integration, and thus permitting long memory behavior in the detrended series. The results indicate the existence of warming trends in the three series examined (Northern and Southern Hemispheres along with global temperatures), with orders of integration which are in the range (0.5, 1) and thus implying nonstationary long memory and mean reverting behavior. This is innovative compared with other works that assume short memory behavior in the detrended series. Allowing for segmented trends two features are observed: increasing values in the degree of dependence of the series across time and significant warming trends from 1940 onwards.     

Long memory versus structural breaks: An overview

We discuss the increasing literature on misspecifying structural breaks or more general trends as long-range dependence. We consider tests on structural breaks in the long-memory regression model as well as the behaviour of estimators of the memory parameter when structural breaks or trends are in the data but long memory is not. Methods for distinguishing both of these phenomena are proposed. The financial support of Volkswagenstiftung is gratefully acknowledged.     

On trends and constants in periodic autoregressions

Periodic autoregressions are characterised by autoregressive structures that vary with the season. If a time series is periodically integrated, one needs a seasonally varying differencing filter to remove the stochastic trend. When the periodic regression model contains constants and trends with unrestricted parameters, the data can show diverging seasonal deterministic trends. In this paper we derive explicit expressions for parameter restrictions that result in common deterministic trends under periodic trend stationarity and periodic integration.     

On trends and constants in periodic autoregressions

Periodic autoregressions are characterised by autoregressive structures that vary with the season. If a time series is periodically integrated, one needs a seasonally varying differencing filter to remove the stochastic trend. When the periodic regression model contains constants and trends with unrestricted parameters, the data can show diverging seasonal deterministic trends. In this paper we derive explicit expressions for parameter restrictions that result in common deterministic trends under periodic trend stationarity and periodic integration.     

Single contrast tests for detecting trends in binomial proportions

The present study is related to the detection of trends in binomial proportions. Most of the tests usually adopted to detect trends in this regard are either based on scores, the choice of which is subjective, or are difficult to administer because of being complicated. As a remedy, two single contrast tests are proposed for this purpose. Performance measures of the proposed tests are obtained numerically and are compared with those of some of the existing competitors.     

The role of the drift in I(2) systems

Summary This paper discussed the role of the drift in vector autoregressive processes allowing for integrated components up to order 2. It is shown how the drift can generate linear and quadratic deterministic trends. A two-stage statistical analysis of the system in the presence of quadratic trends is proposed. The analysis of the system allows to define a consistent sequence of tests on the numbers of common components integrated of a given order, called the integration indices of the system. The relevant asymptotic distributions are non-standard, belong to the Limiting Gaussian Functional family and are tabulated by simulation. The proposed procedure can also be consistently combined with other procedures proposed by the author for the cases of a linear trend and of no deterministic trends in the system. Invited paper at the Conference held in Bologna, Italy, 27–28 May 1993, on “Statistical Tests: Methodology and Econometric Applications”. Partial financial support is acknowledged from Italian MURST grants 40% and 60%.     

Demographic and social changes in the Katowice voivodship

"Since [the] late nineteen-eighties, two important trends in demographic and social urbanisation have been visible. The first is diminution of [the] influence of demographic influences which originate outside the voivodship, the second is a stabilisation (regress in some cases) of some urban centres. The latter is due to a nation-wide demographic transformation as well as to a growth in emigration from traditional urban centres of Katowice conurbation....[The] latest [official] surveys indicate that the named trends are characteristic for a majority of Polish cities over 100,000 since 1990." (EXCERPT)     

Trend estimation of multivariate time series with controlled smoothness

This paper extends the univariate time series smoothing approach provided by penalized least squares to a multivariate setting, thus allowing for joint estimation of several time series trends. The theoretical results are valid for the general multivariate case, but particular emphasis is placed on the bivariate situation from an applied point of view. The proposal is based on a vector signal-plus-noise representation of the observed data that requires the first two sample moments and specifying only one smoothing constant. A measure of the amount of smoothness of an estimated trend is introduced so that an analyst can set in advance a desired percentage of smoothness to be achieved by the trend estimate. The required smoothing constant is determined by the chosen percentage of smoothness. Closed form expressions for the smoothed estimated vector and its variance-covariance matrix are derived from a straightforward application of generalized least squares, thus providing best linear unbiased estimates for the trends. A detailed algorithm applicable for estimating bivariate time series trends is also presented and justified. The theoretical results are supported by a simulation study and two real applications. One corresponds to Mexican and US macroeconomic data within the context of business cycle analysis, and the other one to environmental data pertaining to a monitored site in Scotland.     

Inflation and the Divergence of Relative Prices: Evidence from a Cointegration Analysis

The relation between inflation and RPV plays a prominent role in explaining the costs of inflation. This study investigates whether the CPI subcategories drift apart more over a period of high inflation rates than during one of low inflation. The wider dispersion of the subcategories is reflected in an increasing number of common stochastic trends in the system of sub price indices. The results for US data as well as for cross-country comparisons indicate that the influence of inflation on the dispersion of relative prices cannot be revealed by counting cointegrating relations. Thus, the number of stochastic trends or cointegrating relations is not a reliable indicator for the distorting effect of inflation on the dispersion of relative prices.     

The concept of spatial order and other problems of regional research in demography

The concept of spatial demographic order is introduced and defined as the preferred spatial distribution of a given demographic variable. The results of a multivariate statistical analysis of demographic trends in rural and urban areas in Poland are presented to illustrate the convergence of such trends in the areas studied. The author concludes that the increasing similarity in demographic processes between rural and urban areas is due mainly to migration.     

Asymptotic Properties of OLS Estimates in Autoregressions with Bounded or Slowly Growing Deterministic Trends

ABSTRACT

We propose a general method of modeling deterministic trends for autoregressions. The method relies on the notion of L ₂-approximable regressors previously developed by the author. Some facts from the theory of functions play an important role in the proof. In its present form, the method encompasses slowly growing regressors, such as logarithmic trends, and leaves open the case of polynomial trends.     

Modelling spatial-temporal covariance structures in monocropping barley trials

In long-term trials, not only are individual plot errors correlated over time but there is also a consistent underlying spatial variability in field conditions. The current study sought the most appropriate covariance structure of errors correlated in three dimensions for evaluating the productivity and time-trends in the barley yield data from the monocropping system established in northern Syria. The best spatial-temporal model found reflected the contribution of autocorrelations in spatial and temporal dimensions with estimates varying with the yield variable and location. Compared with a control structure based on independent errors, this covariance structure improved the significance of the fertilizer effect and the interaction with year. Time-trends were estimated in two ways: by accounting the seasonal variable contribution in annual variability (Method 1), which is suitable for detecting significant trends in short data series; and by using the linear component of the orthogonal polynomial on time (year), which is appropriate for long series (Method 2). Method 1 strengthened time-trend detection compared with the method of Jones and Singh [J. Agri. Sci., Cambridge 135 (2000), pp. 251-259] which assumed independence of temporal errors. Most estimates of yield trends over time from fertilizer application were numerically greater than the corresponding linear trends estimated from orthogonal polynomials in time (Method 2), reflecting the effect of accounting for seasonal variables. Grain yield declined over time at the drier site in the absence of nitrogen or phosphorus application, but positive trends were observed fairly generally for straw yield and for grain yield under higher levels of fertilizer inputs. It is suggested that analyses of long-term trials on other crops and cropping systems in other agro-ecological zones could be improved by taking spatial and temporal variability into account in the data evaluation.