The Hiring Function Reconsidered: on Closing the Circle

This paper investigates the matching of job searchers with vacant jobs: a key component of the dynamics of worker reallocation in the labour market. The job searchers may be unemployed, employed or not in the labour force and we estimate matching or hiring functions including all three groups. We show that previous studies, which ignore both employed job seekers and unemployed job seekers who are considered to be out of the labour force, produce biased estimates of the coefficients of interest. By considering only unemployment outflows into jobs and ignoring interdependencies with other flows, these studies overlook an important aspect of job matching. Our estimates on Australian data support a more general approach and produce models that dominate those proposed previously. We find that concentrating on the aggregate matching function alone does not reveal the full extent of the interaction across job searchers. Indeed, we find that job searchers from the three groups do not receive a fair share of hires: there appears to be segmentation of hiring opportunities which may be explained by a form of ranking of applicants. Together these results demonstrate that the disaggregate worker flows and their interdependence are key features on the labour market and should be included in studies of the hiring process.     

Job searchers,job matches and the elasticity of matching

This paper stresses the importance of a specification of the matching function in which the measure of job matches corresponds to the measure of job searchers. In many empirical studies on the matching function this requirement has not been fulfilled because it is difficult to find information about employed job searchers and job searchers from outside the labour market. In this paper, we specify and estimate matching functions where the flow corresponds to the correct stock. We use several approximations for the stock of non-unemployed job searchers. We find that the estimation results are sensitive to the approximation we use. Our main conclusion is that it is important to account for the behaviour of non-unemployed job searchers since otherwise the estimated parameters of the matching function may be seriously biased.     

Job matching, social network and word-of-mouth communication

Workers can find a job either directly or through personal contacts. From this micro scenario, we derive an aggregate matching function that has the standard properties but fails to be homogeneous of degree one. We show that, when the network size increases, on average, the unemployed workers hear about more vacancies through their social network. However, above a certain critical value, job matches decrease with network size. We then establish existence and uniqueness of the labor market equilibrium and study its properties. Finally, we demonstrate that the decentralized market equilibrium is not efficient because of both search and network externalities.     

Exchange rates and oil prices: A multivariate stochastic volatility analysis

This paper uses the multivariate stochastic volatility (MSV) and the multivariate GARCH (MGARCH) models to investigate the volatility interactions between the oil market and the foreign exchange (FX) market, in an attempt to extract information intertwined in the two for better volatility forecast. Our analysis takes into account structural breaks in the data. We find that when the markets are relatively calm (before the 2008 crisis), both oil and FX markets respond to shocks simultaneously and therefore no interaction is detected in daily data. However, during turbulent time, there is bi-directional volatility interaction between the two. In other words, innovations that hit one market also have some impact on the other at a later date and thus using such a dependence significantly improves the forecasting power of volatility models. The MSV models outperform others in fitting the data and forecasting exchange rate volatility. However, the MGARCH models do better job in forecasting oil volatility.     

Employed and Unemployed Job Seekers and the Business Cycle

The job search literature suggests that on‐the‐job search reduces the probability of un employed people finding jobs. However, there is little evidence that employed and unemployed job seekers are similar or apply for the same jobs. We compare employed and unemployed job seekers in their individual characteristics, preferences over working hours, job‐search strategies and employment histories, and identify how differences vary over the business cycle. We find systematic differences which persist over the business cycle. Our results are consistent with a segmented labour market in which employed and unemployed job seekers are unlikely to directly compete with each other for jobs.     

Spatial mobility and competition for jobs: Some theory and evidence for Western Germany

This paper sheds new light on the role of regional labor market conditions for regional mobility. We study competition for vacant jobs along two dimensions – between employed and unemployed job searchers, and between resident and non-resident job searchers – within a simple matching framework. Evidence from estimating regional matching functions with data on job searchers disaggregated by previous employment status and regional provenance indicates that competition for jobs along both dimensions affects hiring. Tests of the theoretical predictions suggest that labor market conditions do determine regional mobility, but the countervailing effects of competition between employed and unemployed dilute mobility effects.     

Optimal Portfolio Choice Under Decision‐Based Model Combinations

We propose a density combination approach featuring combination weights that depend on the past forecast performance of the individual models entering the combination through a utility‐based objective function. We apply this model combination scheme to forecast stock returns, both at the aggregate level and by industry, and investigate its forecasting performance relative to a host of existing combination methods, both within the class of linear and time‐varying coefficients, stochastic volatility models. Overall, we find that our combination scheme produces markedly more accurate predictions than the existing alternatives, both in terms of statistical and economic measures of out‐of‐sample predictability. Copyright © 2016 John Wiley & Sons, Ltd.     

Housing tenure and labor market impacts: The search goes on

We develop two search-theoretic models emphasizing firm entry to examine the Oswald hypothesis, the idea that homeownership is linked to inferior labor market outcomes, and compare their predictions to three extant theories. The five models have surprisingly different predictions about the labor market at both the aggregate and micro levels. Using a suitable instrumental variable strategy, we estimate both micro and aggregate level regression models of wages and unemployment and compare the estimates to those predictions. We find that while homeowners are less likely to be unemployed, they also have lower wages, all else equal, compared to renters. In addition, higher regional homeownership rates are associated with a greater probability of individual worker unemployment and higher wages. The outcome of a horserace between our new search-theoretic models is mixed—the wage-posting model predicts observed unemployment impacts while a bargaining variant does a better job explaining observed wages and aggregate labor market outcomes. Overall, we conclude that firm behavior is important for understanding the labor market impacts of homeownership. Because this is the case, regional homeownership rates are not good instruments for individual tenure choice in empirical work. And while individual homeowners may have inferior labor market outcomes as compared to renters, from the viewpoint of society, higher homeownership rates may result in greater job creation and overall production, among other benefits.     

Are Short‐lived Jobs Stepping Stones to Long‐Lasting Jobs?*

This article assesses whether short‐lived jobs (lasting one quarter or less and involuntarily ending in unemployment) are stepping stones to long‐lasting jobs (enduring 1 year or more) for Belgian long‐term unemployed school‐leavers. We proceed in two steps. First, we estimate labour market trajectories in a multi‐spell duration model that incorporates lagged duration and lagged occurrence dependence. Second, in a simulation we find that (fe)male school‐leavers accepting a short‐lived job are, within 2 years, 13.4 (9.5) percentage points more likely to find a long‐lasting job than in the counterfactual in which they reject short‐lived jobs.     

Forecasting expected shortfall: Should we use a multivariate model for stock market factors?

Is univariate or multivariate modeling more effective when forecasting the market risk of stock portfolios? We examine this question in the context of forecasting the one-week-ahead expected shortfall of a stock portfolio based on its exposure to the Fama–French and momentum factors. Applying extensive tests and comparisons, we find that in most cases there are no statistically significant differences between the forecasting accuracy of the two approaches. This result suggests that univariate models, which are more parsimonious and simpler to implement than multivariate factor-based models, can be used to forecast the downside risk of equity portfolios without losses in precision.     

The tourism forecasting competition

We evaluate the performances of various methods for forecasting tourism data. The data used include 366 monthly series, 427 quarterly series and 518 annual series, all supplied to us by either tourism bodies or academics who had used them in previous tourism forecasting studies. The forecasting methods implemented in the competition are univariate and multivariate time series approaches, and econometric models. This forecasting competition differs from previous competitions in several ways: (i) we concentrate on tourism data only; (ii) we include approaches with explanatory variables; (iii) we evaluate the forecast interval coverage as well as the point forecast accuracy; (iv) we observe the effect of temporal aggregation on the forecasting accuracy; and (v) we consider the mean absolute scaled error as an alternative forecasting accuracy measure. We find that pure time series approaches provide more accurate forecasts for tourism data than models with explanatory variables. For seasonal data we implement three fully automated pure time series algorithms that generate accurate point forecasts, and two of these also produce forecast coverage probabilities which are satisfactorily close to the nominal rates. For annual data we find that Naïve forecasts are hard to beat.     

Allocation of individuals to job levels under rationing

We augment an ordered response model for the job level at which individuals desire to work with allowance for individual reporting of a discrepancy between actual and desired job level, and we also investigate possible biases from restricting the observations to the employed, and omitting the unemployed. We find that the latter bias can be ignored and interpret this as evidence that the unemployed are not choosier than the employed. People claiming inadequate capability utilization are not markedly differently allocated than those claiming a proper match. Women's lower job levels cannot be explained from rationing by capability utilization.     

INFORMATION IN THE YIELD CURVE: A MACRO‐FINANCE APPROACH

We use a macro‐finance model, incorporating macroeconomic and financial factors, to study the term premium in the US bond market. Estimating the model using Bayesian techniques, we find that a single factor explains most of the variation in bond risk premiums. Furthermore, the model‐implied risk premiums account for up to 40% of the variability of one‐ and two‐year excess returns. Using the model to decompose yield spreads into an expectations and a term premium component, we find that, although this decomposition does not seem important to forecast economic activity, it is crucial to forecast inflation for most forecasting horizons. Copyright © 2012 John Wiley & Sons, Ltd.     

Adding dummy variables: A simple approach for improved volatility forecasting in electricity market

This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility. Considering day-of-the-week effects, structural breaks, or both, we propose three classes of HAR models to forecast electricity volatility based on existing HAR models. The estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon, whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily, weekly, and monthly horizons. The out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility, and in most cases, dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week effects. The out-of-sample results were robust across three different methods. More importantly, we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP*

In this article, we merge two strands from the recent econometric literature. First, factor models based on large sets of macroeconomic variables for forecasting, which have generally proven useful for forecasting. However, there is some disagreement in the literature as to the appropriate method. Second, forecast methods based on mixed‐frequency data sampling (MIDAS). This regression technique can take into account unbalanced datasets that emerge from publication lags of high‐ and low‐frequency indicators, a problem practitioner have to cope with in real time. In this article, we introduce Factor MIDAS, an approach for nowcasting and forecasting low‐frequency variables like gross domestic product (GDP) exploiting information in a large set of higher‐frequency indicators. We consider three alternative MIDAS approaches (basic, smoothed and unrestricted) that provide harmonized projection methods that allow for a comparison of the alternative factor estimation methods with respect to nowcasting and forecasting. Common to all the factor estimation methods employed here is that they can handle unbalanced datasets, as typically faced in real‐time forecast applications owing to publication lags. In particular, we focus on variants of static and dynamic principal components as well as Kalman filter estimates in state‐space factor models. As an empirical illustration of the technique, we use a large monthly dataset of the German economy to nowcast and forecast quarterly GDP growth. We find that the factor estimation methods do not differ substantially, whereas the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the Factor MIDAS models, which confirms the usefulness of the mixed‐frequency techniques that can exploit timely information from business cycle indicators.     

Subsidizing low-skilled jobs in a dual labor market

We introduce a matching model that allows for classical and frictional unemployment. The labor market is dual featuring low-skilled and high-skilled workers and simple and complex jobs. Simple jobs pay a minimum wage, while wages in the complex jobs are determined by Nash bargaining. Opportunities for low-skilled workers are limited to simple jobs; while high-skilled unemployed can apply for both types of jobs, and thereby can accept to be downgraded. We analyze the outcomes of simple job subsidy policies assuming that government budget is balanced through taxes on occupied workers. We first give conditions for the existence and uniqueness of a steady-state equilibrium and we then analyze the effects of different fiscal instruments. We show that in this set-up, increasing simple job subsidies does not necessarily reduce low-skilled unemployment or unemployment spells. By introducing heterogeneous skills and possible downgrading of the high-skilled workers, we show that the effectiveness of such policies in reducing the classical unemployment is decreasing. In fact, any additional classical unemployed re-entering the job market is accompanied by an increasing number of high-skilled workers downgrading to low-skilled jobs. We calibrate the model on French labor market data. It is found that for five low-skilled workers leaving classical unemployment, two high-skilled workers are downgraded.     

Forecasting television ratings

Despite the state of flux in media today, television remains the dominant player globally for advertising spending. Since television advertising time is purchased on the basis of projected future ratings, and ad costs have skyrocketed, there is increasingly pressure to forecast television ratings accurately. The forecasting methods that have been used in the past are not generally very reliable, and many have not been validated; also, even more distressingly, none have been tested in today’s multichannel environment. In this study we compare eight different forecasting models, ranging from a naïve empirical method to a state-of-the-art Bayesian model-averaging method. Our data come from a recent time period, namely 2004-2008, in a market with over 70 channels, making the data more typical of today’s viewing environment. The simple models that are commonly used in industry do not forecast as well as any econometric models. Furthermore, time series methods are not applicable, as many programs are broadcast only once. However, we find that a relatively straightforward random effects regression model often performs as well as more sophisticated Bayesian models in out-of-sample forecasting. Finally, we demonstrate that making improvements in ratings forecasts could save the television industry between $250 and $586 million per year.     

Marketplace matching in Britain: Evidence from individual unemployment spells

This paper investigates job matching patterns in Great Britain. Evidence from individual transitions out of unemployment demonstrates that recently unemployed workers are likely to find jobs in the existing stock of vacancies. If, however, they are unlucky and fail to match early on, job seekers cease matching with existing vacancies. Workers with longer unemployment spells instead form matches with the flow of new vacancies. This pattern is more pronounced for workers who experienced only short spells of employment prior to their current job search. This evidence provides robust support for stock-flow matching but is difficult to reconcile with random matching.     

Forecasting and loss functions

This paper considers two problems of interpreting forecasting competition error statistics. The first problem is concerned with the importance of linking the error measure (loss function) used in evaluating a forecasting model with the loss function used in estimating the model. It is argued that because the variety of uses of any single forecast, such matching is impractical. Secondly, there is little evidence that matching would have any impact on comparative forecast performance, however measured. As a consequence the results of forecasting competitions are not affected by this problem. The second problem is concerned with the interpreting performance, when evaluated through M(ean) S(quare) E(rror). The authors show that in the Makridakis Competition, good MSE performance is solely due to performance on a small number of the 1001 series, and arises because of the effects of scale. They conclude that comparisons of forecasting accuracy based on MSE are subject to major problems of interpretation.