Afleiding van kwartaalcijfers uit jaartotalen

Summary Quarterly figures may be required when only a sequence of annual totals is available. This article presents two reasonable methods for “interpolating” quarterly figures by means of appropriate minimizing procedures. The first method relates to minimizing the sum of squared second differences of the quarterly figures, the other one is a linear programming procedure which refers to the absolute second differences. Both methods are illustrated by a numerical example, the first one giving somewhat better results.     

The London Business School with Gower Publishing

The world economy is just starting to emerge from the second trough of a "W-shaped" recession. Compared with the experience after the first oil shock, when industrial production fell by 12 per cent, bringing inflation quickly down from 14 per cent into single figures. the 1980 world recession was mild. Between the first and third quarters industrial output fell 5 per cent; it recovered in the fourth quarter and inflation stopped falling. As a result governments - and this is especially true of the United States - look "another bite at the cherry": monetary policy was tightened and interest rates rose. The effect over the last six months has been to produce a second dip in output. The renewed attack on inflation has, however, been successful and inflation is now well in single figures and falling. Consequently a general easing of policy is evident and a recovery of output in the second half of 1982 and into 1983 remains our forecast.     

On forecasting cointegrated seasonal time series

We analyze periodic and seasonal cointegration models for bivariate quarterly observed time series in an empirical forecasting study. We include both single equation and multiple equation methods for those two classes of models. A VAR model in first differences, with and without cointegration restrictions, and a VAR model in annual differences are also included in the analysis, where they serve as benchmark models. Our empirical results indicate that the VAR model in first differences without cointegration is best if one-step ahead forecasts are considered. For longer forecast horizons however, the VAR model in annual differences is better. When comparing periodic versus seasonal cointegration models, we find that the seasonal cointegration models tend to yield better forecasts. Finally, there is no clear indication that multiple equations methods improve on single equation methods.     

Winning methods for forecasting tourism time series

We constituted Team “Lee C. Baker”, which won the online tourism forecasting competition. Our forecasts had the smallest MASE for the first part of the competition involving 518 annual time series, and the second smallest MASE for the second part of the competition involving 427 quarterly time series and 366 monthly time series. In this article, we briefly describe the methods we used.     

The information content of earnings and turnover announcements in France

Annual net income is seen by shareholders as the most important figure, since it is, for individual accounts, the basis of appropriation of profit by the shareholders' general meeting. However, firms publish interim figures at a higher frequency to allow investors to react quickly to frequently updated information that gives them a more accurate view of the evolution of the firm. In the United States, according to regulation S-X, the interim quarterly reports must disclose the net income. In France, firms must publish their quarterly turnovers and their half-year income statements in addition to their annual accounts. Whereas American publications are practically homogeneous in nature in terms of earnings disclosure, the information content of French quarterly reports differs from that of the half-year and annual accounts. Such French irregularities in information content mean that interim and annual announcements do not have the same value for the shareholders. Because of the heterogeneous nature and the time frame of the disclosed information, one may wonder if French market reactions to the announcements of interim publications exhibit significantly different characteristics from those observed for quarterly releases in other countries. This can only be appreciated through a differential analysis of French market response to interim and annual announcements.     

Estimating structural changes in regression quantiles

This paper considers the estimation of multiple structural changes occurring at unknown dates in one or multiple conditional quantile functions. The analysis covers time series models as well as models with repeated cross-sections. We estimate the break dates and other parameters jointly by minimizing the check function over all permissible break dates. The limiting distribution of the estimator is derived and the coverage property of the resulting confidence interval is assessed via simulations. A procedure to determine the number of breaks is also discussed. Empirical applications to the quarterly US real GDP growth rate and the underage drunk driving data suggest that the method can deliver more informative results than the analysis of the conditional mean function alone.     

Predictions of annual earnings using quarterly earnings,annual earnings and dividend payout ratios

This study empirically examines two issues related to forecasting annual accounting earnings. The first issue studied is the improvement in forecasts of annual earnings that can be obtained by including information about dividend payout along with the past earnings series in forecasting models. The second issue deals with the comparative ability of quarterly earnings time series models and annual earnings time series models to predict annual earnings. The results of this study indicate that considerable improvement in predictive ability can be obtained by expanding the information set to include the dividend payout ratio series. The empirical analysis also indicates that time series models developed using annual earnings generate more accurate predictions of annual earnings than do models developed using quarterly earnings.     

A forecasting comparison of some var techniques

Higher dimensional multivariate time series models suffer from the problem of over-parametrisation which impairs their forecasting performance. Starting from such unrestricted vector autoregressive models the paper discusses two ways to cope with this difficulty. The first approach reduces the number of free parameters by applying a subset modelling strategy. The second approach takes a Bayesian point of view by formulating ‘priors’ which are then combined with sample information, but leaving the original specification unaltered. Using Austrian quarterly macroeconomic time series a comparative study is undertaken by running alternative forecasting exercises. Both methods improve out-of-sample forecasting performance substantially at the cost of some bias in ex-post simulations. Comparing the ex-ante predictions of the two approaches, the former does better at short horizons whereas the latter gains as the forecast horizon lengthens.     

Europe

The Eurozone economy may have lost some momentum in the course of Q2, based on business surveys for the period. Admittedly, the falls in the composite PMI in May and June were pretty small and on past form the PMIs still point to slightly stronger quarterly GDP growth than the 0.2% recorded in Q1. But given the sharp falls in German, French, Italian and Spanish industrial production in May and subdued Eurozone retail sales figures for the first two months of the quarter we suspect that Eurozone GDP growth is unlikely to have picked up in Q2…     

Een toepassing van "importance sampling"

Summary  This paper describes an experiment with "importance sampling", to show how much reduction of the computation time and sample size can be achieved in comparison with the usual Monte Carlo method. A comparison is made between each of the three methods of "importance sampling" and the usual Monte Carlo method by the determination of the expression

Of the three methods A, B and C the first one uses the shifted exponential distribution, the second one uses the gamma distribution, and the third one uses the exponential distribution with modified parameter. These three methods have all smaller variances, ranges and sample sizes than the usual Monte Carlo method. Their order of preference is A, B, C. With respect to computing time only the method A is significantly better. So only the method A is an improvement in respect of both the sample size and the computing time.     

A UNIFYING VIEW ON MULTI‐STEP FORECASTING USING AN AUTOREGRESSION

Abstract This paper unifies two methodologies for multi‐step forecasting from autoregressive time series models. The first is covered in most of the traditional time series literature and it uses short‐horizon forecasts to compute longer‐horizon forecasts, while the estimation method minimizes one‐step‐ahead forecast errors. The second methodology considers direct multi‐step estimation and forecasting. In this paper, we show that both approaches are special (boundary) cases of a technique called partial least squares (PLS) when this technique is applied to an autoregression. We outline this methodology and show how it unifies the other two. We also illustrate the practical relevance of the resultant PLS autoregression for 17 quarterly, seasonally adjusted, industrial production series. Our main findings are that both boundary models can be improved by including factors indicated from the PLS technique.     

Een toepassing van “importance sampling”

Summary This paper describes an experiment with “importance sampling”, to show how much reduction of the computation time and sample size can be achieved in comparison with the usual Monte Carlo method. A comparison is made between each of the three methods of “importance sampling” and the usual Monte Carlo method by the determination of the expression Of the three methods A, B and C the first one uses the shifted exponential distribution, the second one uses the gamma distribution, and the third one uses the exponential distribution with modified parameter. These three methods have all smaller variances, ranges and sample sizes than the usual Monte Carlo method. Their order of preference is A, B, C. With respect to computing time only the method A is significantly better. So only the method A is an improvement in respect of both the sample size and the computing time.     

年报与第一季报披露时间间隔异常上市公司盈利状况研究——来自上市公司2004年至2008年第一季度的经验证据

我国上市公司年报与第一季报的披露时间底线同为4月30号,存在上市公司年报与第一季报披露时间间隔异常的现象,本文以2004年至2008年第一季度连续存在的上市公司为样本,比较分析了年报与第一季报披露时间间隔在一周之内的上市公司,与披露时间间隔在一周之外的上市公司的盈利水平和盈利质量。结果表明,年报与第一季报披露时间间隔异常的上市公司的盈利水平和盈利质量都显著低于其他上市公司,并对这类上市公司的监管和投资策略提出了建议。     

A note on the value of combining short-term earnings forecasts: A test of Granger and Ramanathan

This paper conducts an empirical analysis of the approaches to obtaining linear combinations of forecasts. Simulated quarterly earnings were modeled using three ARIMA models. One-quarter ahead forecasts were then developed. These forecasts were combined using alternative approaches. The most accurate forecasts were obtained by adding a constant term and not constraining the weights to add up to one. The differences in the accuracy rankings were found to be statistically significant. The results are similar to those obtained by Granger and Ramanathan (1984).     

Energy consumption and economic growth

The present paper discusses methods originally proposed by Adams and Miovic in 1968 (then refined and used later by many other authors) for calculating the output elasticity of useful energy consumption (?). We first show that this methodological approach is quite dubious. Better alternative methods of estimation are then proposed. We also stress that, since the marginal rate of interfuel substitution depends on the GDP functional form, the simulataneous use of several functional forms of GDP in energy studies leads obviously to misleading interpretations. Using thermal efficiency coefficients and OECD countries figures for the 1959–73 period, we finally found that ? did steadily fall from high values to values which are still higher than one.     

Eurozone

The recent flow of economic data supports the view that Q1 will have seen stronger quarterly GDP growth than Q4's 0.4% gain. Indeed, given the composite PMI's healthy end to Q1, the strength seen in Q1 may continue next quarter. As a result, we have upgraded our forecast for GDP growth in Q2 and now pencil in a second consecutive quarterly gain of 0.5%.     

Common cycles in seasonal non-stationary time series

This paper extends the notion of common cycles to quarterly time series having unit roots both at the zero and seasonal frequencies. It is shown that common cycles are present in the Hylleberg–Engle–Granger–Yoo decomposition of these series when there exists a linear combination of their seasonal differences which follows an MA process of order, at most, three. The pitfalls of seasonal adjustment for common cycles analysis are also documented. Inference on common cycles in seasonally cointegrated series is derived from existing statistical methods for codependence. Concepts and methods are illustrated with an empirical analysis of the comovements between consumption and output using Italian data. Copyright © 1999 John Wiley & Sons, Ltd.     

A multi‐level panel STAR model for US manufacturing sectors

We introduce a multi‐level smooth transition model for a panel of time series, which can be used to examine the presence of common nonlinear business cycle features across many variables. The model is positioned in between a fully pooled model, which imposes such common features, and a fully heterogeneous model, which allows for unrestricted nonlinearity. We introduce a second‐stage model linking the parameters that determine the timing of the switches between business cycle regimes to observable explanatory variables, thereby allowing for lead–lag relationships across panel members. We discuss representation, estimation by concentrated simulated maximum likelihood and inference. We illustrate our model using quarterly industrial production in 19 US manufacturing sectors, and document that there are subtle differences across sectors in leads and lags for switches between business cycle recessions and expansions. Copyright © 2005 John Wiley & Sons, Ltd.     

The use of monthly and quarterly data in an ARMA model

This paper deals with specification, prediction and length of interval between the observations in an ARMA model. An AR(1) model is found to be suitable for a specific monthly time series. From this series we construct two types of quarterly series and derive the corresponding ARMA models. The theoretical parameter values of the quarterly models, given the monthly model, are compared with the values found empirically when no monthly series exists. By using the variance of the predictor error, we assess the performance of all specifications in predicting up to one year ahead. We show that while the monthly model performs best in theory, the values computed directly from the estimates prove in our empirical example the quarterly models to be preferable in most cases where we are to predict more than one quarter ahead.     

Economic and spatial determinants of interregional migration in Kazakhstan

In this paper we analyze economic and spatial determinants of interregional migration in Kazakhstan using quarterly panel data on region to region migration in 2008–2010. The main contribution of the paper relates to the ethnic divide in Kazakhstan, which is the home of a big ethnic Russian community, adding to the scarce literature on ethnic differences in migration patterns. Against this background we investigate whether the determinants of interregional mobility depend on ethnicity. In line with traditional economic theory we find that migration is determined by economic factors, first of all wage. As predicted by gravity arguments, mobility is larger between more populated regions and distance has a strong negative impact on migration, indicating high migration-related costs. Most likely caused by stronger family ties and kinship bonds, the deterring effect of distance is higher for ethnic Kazakhs.