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

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

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

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

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