Improved forecasting with leading indicators: the principal covariate index

We propose a new method of leading index construction that combines the need for data compression with the objective of forecasting. This so-called principal covariate index is constructed to forecast growth rates of the Composite CoincidentIndex. The forecast performance is compared with an alternative index based on principal components and with the Composite Leading Index of the Conference Board. The results show that the new index, which takes the forecast objective explicitly into account, provides significant gains over other single-index methods, both in terms of forecast accuracy and in terms of predicting recession probabilities.business cycles;turning points;index construction;principal covariate;principal component;time series forecasting

Prediction beyond the survey sample: correcting for survey effects on consumer decisions.

Direct extrapolation of survey results on purchase intentions may give a biased view onactual consumer behavior. This is because the purchase intentions of consumers maybe affected by the survey itself. On the positive side, such effects can be incorporated ineconometric models to get reliable estimates of actual behavior of non-surveyed consumers,which often is the ultimate purpose of survey studies. This paper proposes a reasonablysimple methodology to correct for such possible survey effects and to get consistent pre-dictions beyond the survey sample. The potential merits of the method are illustrated bya supermarket survey on easy-to-prepare food products and related health issues. Thisindicates that the required corrections can be quite substantial and that predictions thatneglect survey effects can be seriously biased indeed.econometric models;consumer behavior;bias correction;purchase prediction;survey effects

Estimated Parameters Do Not Get the "Wrong Sign" Due To Collinearity Across Included Variables

Estimation results in linear regression models are sometimes in contrast with what was expected on the basis of a certain set of hypotheses or theory, in the sense that one or more parameters have the "wrong sign". One could be inclined to think that this is due to collinearity across explanatory variables, suggesting one should leave out one or more of the collinear variables. In this note we show that this is not a valid approach. Additionally, we show that "wrong signs" can occur because of correlations between included and omitted variables, so that "wrong signs" may occur if the model is not correctly specified. That is, if we find 'wrong signs" we should start questioning our model choice, not the data.parameter estimation;collinearity;misspecification

Immigrant gender convergence in education and on the labor market

Immigration tends to have a mitigating effect on the socioeconomic gender gap among immigrants. To explain this finding, we propose a gender convergence hypothesis that states that migration to a modern ‘open’ society offers women the opportunity to improve their position relative to that of men. In such a society, there are (almost) equal chances to participate in education and paid labor. The equalizing effect will be larger if the immigrants come from less developed regions, since women then have more room to improve their position. However, there may also be countervailing cultural powers within the immigrant group. The gender convergence hypothesis proposed here is tested for immigrants in the Netherlands. Using survey data, we investigate the educational and labor market position of Turkish, Moroccan, Surinamese, and Antillean males and females. We find convergent trends, particularly among Moroccan immigrants who come from less developed regions in their country of origin and who meet less cultural in-group barriers than, for example, Turkish immigrants.

Correcting for Survey Effects in Pre-election Polls

Pre-election polls can suffer from survey effects. For example, surveyed individuals can become more aware of the upcoming election so that they become more inclined to vote. These effects may depend on factors like political orientation and prior intention to vote, and this may cause biases in forecasts of election outcomes. We advocate a simple methodology to estimate the magnitude of these survey effects, which can be taken into account when translating future poll results into predicted election outcomes. The survey effects are estimated by collecting survey data both before and after the election. We illustrate our method by means of a field study with data concerning the 2009 European Parliament elections in the Netherlands. Our study provides empirical evidence of significant positive survey effects with respect to voter participation, especially for individuals with low intention to vote. For our data, the overall survey effect on party shares is small. This effect can be more substantial for less balanced survey samples, for example, if political orientation and voting intention are correlated in the sample. We conclude that pre-election polls that do not correct for survey effects will overestimate voter turnout and will have biased party shares.data collection;bias correction;survey effects;intention modification;pre-election polls;turnout forecast;self-prophecy

Estimated Incident Cost Savings in Shipping Due to Inspections

The effectiveness of safety inspections has been analysed from various angles, but until now, relatively little attention has been given to translate risk reduction into incident cost savings. This paper quantifies estimated cost savings based on port state control inspections and industry vetting inspections. It is based on a unique dataset of 515,194 ship arrivals and inspections from the United States of America and Australia, and inspections of three industry vetting inspection regimes, for the time period 2002 to 2007. The risk reducing effect of inspections is estimated by means of duration models, in terms of inspection gains based on the probability of survival. The results suggest average total estimated cost savings in the range of USD 74 to 192 thousand (median USD 19 to 46 thousand) owing to reduced risk of total loss due to a port state control inspection. Cost savings for industry inspections are found to be even higher, especially for tankers. The savings vary by type, age and size of the ship. The benefits of an inspection are in general larger for older and larger vessels, and also for vessels with undefined flags and unknown classification societies. As inspection costs are relatively low in comparison to potential cost savings, the results underline the importance in determining high risk ships to prevent costs due to total loss of ships.maritime safety;duration analysis;ship inspection

A single-electron inverter

A single-electron inverter was fabricated that switches from a high output to a low output when a fraction of an electron is added to the input. For the proper operation of the inverter, the two single-electron transistors that make up the inverter must exhibit voltage gain. Voltage gain was achieved by fabricating a combination of parallel-plate gate capacitors and small tunnel junctions in a two-layer circuit. Voltage gain of 2.6 was attained at 25 mK and remained larger than one for temperatures up to 140 mK. The temperature dependence of the gain agrees with the orthodox theory of single-electron tunneling.Comment: 3 pages, 4 figures (1 color), to be published in Appl. Phys. Let

Time series forecasting by principal covariate regression.

This paper is concerned with time series forecasting in the presence of a large numberof predictors. The results are of interest, for instance, in macroeconomic and financialforecasting where often many potential predictor variables are available. Most of thecurrent forecast methods with many predictors consist of two steps, where the largeset of predictors is first summarized by means of a limited number of factors -forinstance, principal components- and, in a second step, these factors and their lags areused for forecasting. A possible disadvantage of these methods is that the constructionof the components in the first step is not directly related to their use in forecasting inthe second step. This motivates an alternative method, principal covariate regression(PCovR), where the two steps are combined in a single criterion. This method hasbeen analyzed before within the framework of multivariate regression models. Moti-vated by the needs of macroeconomic time series forecasting, this paper discusses twoadjustments of standard PCovR that are necessary to allow for lagged factors and forpreferential predictors. The resulting nonlinear estimation problem is solved by meansof a method based on iterative majorization. The paper discusses some numericalaspects and analyzes the method by means of simulations. Further, the empirical per-formance of PCovR is compared with that of the two-step principal component methodby applying both methods to forecast four US macroeconomic time series from a set of132 predictors, using the data set of Stock and Watson (2005).distributed lags;dynamic factor models;economic forecasting;iterative majorization;principal components;principal covariate regression

Forecast comparison of principal component regression and principal covariate regression

Forecasting with many predictors is of interest, for instance, inmacroeconomics and finance. This paper compares two methods for dealing withmany predictors, that is, principal component regression (PCR) and principalcovariate regression (PCovR). Theforecast performance of these methods is compared by simulating data fromfactor models and from regression models. The simulations show that, in general, PCR performs better for the first type of data and PCovR performs better for the second type of data. The simulations also clarify the effect of the choice of the PCovR weight on the orecast quality.economic forecasting;principal components;factor model;principal covariates;regression model

Improved forecasting with leading indicators: the principal covariate index

We propose a new method of leading index construction that combines the need for data compression with the objective of forecasting. This so-called principal covariate index is constructed to forecast growth rates of the Composite Coincident Index. The forecast performance is compared with an alternative index based on principal components and with the Composite Leading Index of the Conference Board. The results show that the new index, which takes the forecast objective explicitly into account, provides significant gains over other single-index methods, both in terms of forecast accuracy and in terms of predicting recession probabilities