Showing papers in "Journal of Applied Econometrics in 2013"

Generalized autoregressive score models with applications

Abstract: We propose a class of observation driven time series models referred to as Generalized Autoregressive Score (GAS) models. The mechanism to update the parameters over time is the scaled score of the likelihood function. This new approach provides a unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models. The GAS model encompasses other well-known models such as the generalized autoregressive conditional heteroskedasticity, the autoregressive conditional duration, the autoregressive conditional intensity, and Poisson count models with time-varying mean. In addition, our approach can lead to new formulations of observation driven models. We illustrate our framework by introducing new model specifications for time-varying copula functions and for multivariate point processes with time-varying parameters. We study the models in detail and provide simulation and empirical evidence.

The Role of Time-Varying Price Elasticities in Accounting for Volatility Changes in the Crude Oil Market

Abstract: There has been a systematic increase in the volatility of the real price of crude oil since 1986, followed by a decline in the volatility of oil production since the early 1990s. We explore reasons for this evolution. We show that a likely explanation of this empirical fact is that both the short-run price elasticities of oil demand and of oil supply have declined considerably since the second half of the 1980s. This implies that small disturbances on either side of the oil market can generate large price responses without large quantity movements, which helps explain the latest run-up and subsequent collapse in the price of oil. Our analysis suggests that the variability of oil demand and supply shocks actually has decreased in the more recent past preventing even larger oil price ‡uctuations than observed in the data. JEL classi…cation: E31, E32, Q43

Forecasting with Medium and Large Bayesian VARs

Abstract: This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases factor methods have been traditionally used, but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic dataset containing 168 variables. We find that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Typically, we find that the simple Minnesota prior forecasts well in medium and large VARs, which makes this prior attractive relative to computationally more demanding alternatives. Our empirical results show the importance of using forecast metrics based on the entire predictive density, instead of relying solely on those based on point forecasts

Macroeconomic forecasting and structural change

Abstract: The aim of this paper is to assess whether explicitly modeling structural change increases the accuracy of macroeconomic forecasts. We produce real time out-of-sample forecasts for inflation, the unemployment rate and the interest rate using a Time-Varying Coefficients VAR with Stochastic Volatility (TV-VAR) for the US. The model generates accurate predictions for the three variables. In particular for inflation the TV-VAR outperforms, in terms of mean square forecast error, all the competing models: fixed coefficients VARs, Time-Varying ARs and the na¨ove random walk model. These results are also shown to hold over the period commonly referred to as the ”Great Moderation”.

Narrow and scientific replication of ‘The slave trade and the origins of mistrust in Africa'

Abstract: SUMMARY Nunn and Wantchekon (2011) argue that slave trades led to a culture of mistrust in Africa. They regress self-reported trust from the 2005 Afrobarometer surveys on ethnicity-specific historic slave exports. Individuals from ethnic groups that experienced high levels of slave exports are less trusting. Causality is demonstrated by instrumenting slave exports using the historic distance of each ethnic group to the coast. Our narrow replication yields identical results. The scientific replication repeats the analysis with Afrobarometer survey data from 2008, which includes two new countries and more ethnic groups. Our replication confirms the results of Nunn and Wantchekon. Copyright © 2012 John Wiley & Sons, Ltd.

On the simultaneity problem in the aid and growth debate

Abstract: This paper shows that foreign aid has a significant positive average effect on real per capita gross domestic product (GDP) growth if, and only if, the quantitatively large negative reverse causal effect of per capita GDP growth on foreign aid is adjusted for in the growth regression. Instrumental variables estimates show that a 1 percentage point increase in GDP per capita growth decreased foreign aid by over 4%. Adjusting for this quantitatively large, negative reverse causal effect of economic growth on foreign aid shows that a 1% increase in foreign aid increased real per capita GDP growth by around 0.1 percentage points.

Benefit duration, unemployment duration and job match quality: a regression‐discontinuity approach

Abstract: We use a sharp discontinuity in the maximum duration of benefit entitlement to identify the effect of extended benefit duration on unemployment duration and post-unemployment outcomes (employment stability and re-employment wages). We address dynamic selection, which may arise even under an initially random assignment to treatment, estimating a bivariate discrete-time hazard model jointly with a wage equation and correlated unobservables. Owing to the non-stationarity of job search behavior, we find heterogeneous effects of extended benefit duration on the re-employment hazard and on job match quality. Our results suggest that the unemployed who find a job close to and after benefit exhaustion experience less stable employment patterns and receive lower re-employment wages compared to their counterparts who receive extended benefits and exit unemployment in the same period. These results are found to be significant for men but not for women.

Pooling versus model selection for nowcasting gdp with many predictors: empirical evidence for six industrialized countries

Abstract: SUMMARY This paper discusses pooling versus model selection for nowcasting with large datasets in the presence of model uncertainty. In practice, nowcasting a low-frequency variable with a large number of high-frequency indicators should account for at least two data irregularities: (i) unbalanced data with missing observations at the end of the sample due to publication delays; and (ii) different sampling frequencies of the data. Two model classes suited in this context are factor models based on large datasets and mixed-data sampling (MIDAS) regressions with few predictors. The specification of these models requires several choices related to, amongst other things, the factor estimation method and the number of factors, lag length and indicator selection. Thus there are many sources of misspecification when selecting a particular model, and an alternative would be pooling over a large set of different model specifications. We evaluate the relative performance of pooling and model selection for nowcasting quarterly GDP for six large industrialized countries. We find that the nowcast performance of single models varies considerably over time, in line with the forecasting literature. Model selection based on sequential application of information criteria can outperform benchmarks. However, the results highly depend on the selection method chosen. In contrast, pooling of nowcast models provides an overall very stable nowcast performance over time. Copyright © 2012 John Wiley & Sons, Ltd.

Var forecasting using bayesian variable selection

Abstract: This paper develops methods for automatic selection of variables in Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I provide computationally efficient algorithms for stochastic variable selection in generic linear and nonlinear models, as well as models of large dimensions. The performance of the proposed variable selection method is assessed in forecasting three major macroeconomic time series of the UK economy. Databased restrictions of VAR coefficients can help improve upon their unrestricted counterparts in forecasting, and in many cases they compare favorably to shrinkage estimators.

How effective are unemployment benefit sanctions? looking beyond unemployment exit

Abstract: This paper provides a comprehensive evaluation of benefit sanctions,i.e. temporary reductions in unemployment benefits as punishment for noncompliance with eligibility requirements. In addition to the effects on unemployment durations, we evaluate the effects on post-unemployment employment stability, on exits from the labor market and on earnings. In our analysis we use a rich set of Swiss register data which allow us to distinguish between ex ante effects, the effects of warnings and the effects of enforcement of benefit sanctions. Adopting a multivariate mixed proportional hazard approach to address selectivity, we find that both warnings and enforcement increase the job finding rate and the exit rate out of the labor force. Warnings do not affect subsequent employment stability but do reduce post-unemployment earnings. Actual benefit reductions lower the quality of post-unemployment jobs both in terms of job duration as well as in terms of earnings. The net effect of a benefit sanction on post-unemployment income is negative. Over a period of two years after leaving unemployment workers who got a benefit sanction imposed face a net income loss equivalent to 30 days of full pay due to the ex post effect. In addition to that, stricter monitoring may reduce net earnings by up to 4 days of pay for every unemployed worker due to the ex ante effect. Keywords: Benefit sanctions, earnings effects, unemployment duration, competing-risk duration models. JEL Classification: J64, J65, J68

Spatial filtering, model uncertainty and the speed of income convergence in Europe

Abstract: In this paper we put forward a Bayesian Model Averaging method aimed at performing inference under model uncertainty in the presence of potential spatial autocorrelation The method uses spatial filtering in order to account for uncertainty in spatial linkages Our procedure is applied to a dataset of income per capita growth and 50 potential determinants for 255 NUTS-2 European regions We show that ignoring uncertainty in the type of spatial weight matrix can have an important effect on the estimates of the parameters attached to the model covariates After integrating out the uncertainty implied by the choice of regressors and spatial links, human capital investments and transitional dynamics related to income convergence appear as the most robust determinants of growth at the regional level in Europe Our results imply that a quantitatively important part of the income convergence process in Europe is influenced by spatially correlated growth spillovers

Carrot and stick : How reemployment bonuses and benefit sanctions affect exit rates from welfare

Abstract: SUMMARY To increase the exit from welfare, benefit recipients in the municipality of Rotterdam were exposed to various financial incentives. Once their benefit spell exceeded one year, welfare recipients were entitled to a re-employment bonus if they found a job that lasted at least 6 months. However, they could also be punished for noncompliance with eligibility requirements and face a sanction, i.e. a temporary reduction of their benefits. We find that the financial sticks in the form of benefit sanctions were effective in stimulating the exit from welfare, while the financial carrots in the form of re-employment bonuses were not. Copyright © 2011 John Wiley & Sons, Ltd.

Estimation of dynamic panel data models with sample selection

Abstract: SUMMARY We propose a new method for estimating dynamic panel data models with selection. The method uses backward substitution for the lagged dependent variable, which leads to an estimating equation that requires correcting for contemporaneous selection only. The estimator is valid under relatively weak assumptions about errors and permits avoiding the weak instruments problem associated with differencing. We also propose a simple test for selection bias that is based on the addition of a selection term to the first-difference equation and subsequent testing for significance of this term. The methods are applied to estimating dynamic earnings equations for women. Copyright © 2011 John Wiley & Sons, Ltd.

Measuring the Effect of Napster on Recorded Music Sales: Difference-in-Differences Estimates Under Compositional Changes

Abstract: SUMMARY This paper measures the effect of Napster on record sales. I treat the introduction of Napster as a technological event that only Internet users experienced, and use a difference-in-differences (DD) approach. Because of potential compositional changes in Internet users, I examine identifying assumptions for the DD estimator under compositional changes and develop a test for identifying restrictions. To address potential bias due to compositional changes, I extend DD matching estimators to the case of two-variate propensity scores. I find evidence suggesting that file sharing is likely to explain 20% of total sales decline, which is driven by households with children aged 6–17. Copyright © 2011 John Wiley & Sons, Ltd.

Multivariate volatility modeling of electricity futures

Abstract: The deregulation of European electricity markets has led to an increasing need in understanding the volatility and correlation structure of electricity prices. We model a multivariate futures series of the European Energy Exchange (EEX) index, using an asymmetric GARCH model for volatilities and augmented dynamic conditional correlation (DCC) models for correlations. In particular, we allow for smooth changes in the unconditional volatilities and correlations through a multiplicative component that we estimate non-parametrically. We also introduce exogenous variables in our new multiplicative DCC model to account for congestion in short-term conditional volatilities. We find different correlation dynamics for long and short-term contracts and the new model achieves higher forecasting performance compared to a standard DCC model.

Panel data estimates of the production function and product and labor market imperfections

Abstract: SUMMARY Consistent with two models of imperfect competition in the labor market—the efficient bargaining model and the monopsony model—we provide two extensions of a microeconomic version of Hall's framework for estimating price-cost margins. We show that both product and labor market imperfections generate a wedge between factor elasticities in the production function and their corresponding shares in revenue, which can be characterized by a ‘joint market imperfections parameter’. Using an unbalanced panel of 10,646 French firms in 38 manufacturing industries over the period 1978–2001, we can classify these industries into six different regimes depending on the type of competition in the product and the labor market. By far the most predominant regime is one of imperfect competition in the product market and efficient bargaining in the labor market (IC-EB), followed by a regime of imperfect competition in the product market and perfect competition or right-to-manage bargaining in the labor market (IC-PR), and by a regime of perfect competition in the product market and monopsony in the labor market (PC-MO). For each of these three predominant regimes, we assess within-regime firm differences in the estimated average price-cost mark-up and rent sharing or labor supply elasticity parameters, following the Swamy methodology to determine the degree of true firm dispersion. To assess the plausibility of our findings in the case of the dominant regime (IC-EB), we also relate our industry and firm-level estimates of price-cost mark-up and extent of rent sharing to industry characteristics and firm-specific variables respectively. Copyright © 2011 John Wiley & Sons, Ltd.

Rare shocks, great recessions

Abstract: SUMMARY We estimate a DSGE (dynamic stochastic general equilibrium) model where rare large shocks can occur, by replacing the commonly used Gaussian assumption with a Student's t-distribution. Results from the Smets and Wouters (American Economic Review 2007; 97: 586–606) model estimated on the usual set of macroeconomic time series over the 1964–2011 period indicate that (i) the Student's t specification is strongly favored by the data even when we allow for low-frequency variation in the volatility of the shocks, and (ii)) the estimated degrees of freedom are quite low for several shocks that drive US business cycles, implying an important role for rare large shocks. This result holds even if we exclude the Great Recession period from the sample. We also show that inference about low-frequency changes in volatility—and, in particular, inference about the magnitude of Great Moderation—is different once we allow for fat tails. Copyright © 2014 John Wiley & Sons, Ltd.

Real-time forecasting of inflation and output growth with autoregressive models in the presence of data revisions

Abstract: We examine how the accuracy of real-time forecasts from models that include autoregressive terms can be improved by estimating the models on ‘lightly revised’ data instead of using data from the latest-available vintage. The benefits of estimating autoregressive models on lightly revised data are related to the nature of the data revision process and the underlying process for the true values. Empirically, we find improvements in root mean square forecasting error of 2–4% when forecasting output growth and inflation with univariate models, and of 8% with multivariate models. We show that multiple-vintage models, which explicitly model data revisions, require large estimation samples to deliver competitive forecasts. Copyright © 2012 John Wiley & Sons, Ltd.

The effect of parental employment on child schooling

Abstract: SUMMARY This paper presents a model that provides conditions under which a causal interpretation can be given to the association between childhood parental employment and subsequent educational attainments of children. The key parameter comes from theconditional demand function for children's future earning capacity. Its identification rests on having data on siblings and assumptions about the timing of parents' knowledge of their children's endowments. In addition to sibling differences, the useof a fixed-effects instrumental-variables estimator identifies the parameter under weaker conditions. Empirical analysis informed by the model reveals a negative and significant effect on the child's educational attainment of the months of the mother's full-time employment when the child was aged 0–5. The effect of the mother's part-time employment is smaller and less well determined, but again negative. These results suggest that the substitution effect of the mother's employment dominates the income effects. Stronger adverse effects are found for children of less-educated mothers. Copyright © 2012 John Wiley & Sons, Ltd.

Reverse regressions and long-horizon forecasting

Abstract: SUMMARY Long-horizon predictive regressions in finance pose formidable econometric problems when estimated using available sample sizes. Hodrick in 1992 proposed a remedy that is based on running a reverse regression of short-horizon returns on the long-run mean of the predictor. Unfortunately, this only allows the null of no predictability to be tested, and assumes stationary regressors. In this paper, we revisit long-horizon forecasting from reverse regressions, and argue that reverse regression methods avoid serious size distortions in long-horizon predictive regressions, even when there is some predictability and/or near unit roots. Meanwhile, the reverse regression methodology has the practical advantage of being easily applicable when there are many predictors. We apply these methods to forecasting excess bond returns using the term structure of forward rates, and find that there is indeed some return forecastability. However, confidence intervals for the coefficients of the predictive regressions are about twice as wide as those obtained with the conventional approach to inference. We also include an application to forecasting excess stock returns. Copyright © 2011 John Wiley & Sons, Ltd.

Time‐varying dynamics of the real exchange rate: an empirical analysis

Abstract: SUMMARY We use a time-varying structural vector autoregression to investigate evolving dynamics of the real exchange rate for the UK, euro area and Canada. We show that demand and nominal shocks have a substantially larger impact on the real exchange rate after the mid 1980s. Real exchange rate volatility, relative to fundamentals, also shows a marked increase after this point in time. However, there is some evidence suggesting a closer business cycle co-movement of the real exchange rate and fundamentals. Simulations from an open-economy DSGE model show that these results are consistent with a decline in exchange rate pass-through. Copyright © 2012 John Wiley & Sons, Ltd.

Semiparametric vector mem

Abstract: In financial time series analysis we encounter several instances of non‐negative valued processes (volumes, trades, durations, realized volatility, daily range, and so on) which exhibit clustering and can be modeled as the product of a vector of conditionally autoregressive scale factors and a multivariate iid innovation process (vector Multiplicative Error Model). Two novel points are introduced in this paper relative to previous suggestions: a more general specification which sets this vector MEM apart from an equation by equation specification; and the adoption of a GMM-based approach which bypasses the complicated issue of specifying a general multivariate non‐negative valued innovation process. A vMEM for volumes, number of trades and realized volatility reveals empirical support for a dynamically interdependent pattern of relationships among the variables on a number of NYSE stocks.

Multivariate methods for monitoring structural change

Abstract: SUMMARY Detection of structural change is a critical empirical activity, but continuous ‘monitoring’ for changes in real time raises well-known econometric issues that have been explored in a single series context. If multiple series co-break then it is possible that simultaneous examination of a set of series helps identify changes with higher probability or more rapidly than when series are examined on a case-by-case basis. Some asymptotic theory is developed for maximum and average CUSUM detection tests. Monte Carlo experiments suggest that these both provide an improvement in detection relative to a univariate detector over a wide range of experimental parameters, given a sufficiently large number of co-breaking series. This is robust to a cross-sectional correlation in the errors (a factor structure) and heterogeneity in the break dates. We apply the test to a panel of UK price indices. Copyright © 2011 John Wiley & Sons, Ltd.

Semi/nonparametric estimation of consumer search costs

Abstract: This paper studies the estimation of the distribution of non-sequential search costs. We show that the search cost distribution is identified by combining data from multiple markets with common search technology but varying consumer valuations, firms' costs, and numbers of competitors. To exploit such data optimally, we provide a new method based on semi-nonparametric estimation. We apply our method to a dataset of online prices for memory chips and find that the search cost density is essentially bimodal, such that a large fraction of consumers searches very little, whereas a smaller fraction searches a relatively large number of stores. Copyright (c) 2012 John Wiley & Sons, Ltd.

Categorical semiparametric varying-coefficient models

Abstract: SUMMARY Semiparametric varying-coefficient models have become a common fixture in applied data analysis. Existing approaches, however, presume that those variables affecting the coefficients are continuous in nature (or that there exists at least one such continuous variable) which is often not the case. Furthermore, when all variables affecting the coefficients are categorical/discrete, theoretical underpinnings cannot be obtained as a special case of existing approaches and, as such, requires a separate treatment. In this paper we use kernel-based methods that place minimal structure on the underlying mechanism governing parameter variation across categorical variables while providing a consistent and efficient approach that may be of interest to practitioners. One area where such models could be particularly useful is in settings where interactions among the categorical and real-valued predictors consume many (or even exhaust) degrees of freedom for fully parametric models (which is frequently the case in applied settings). Furthermore, we demonstrate that our approach behaves optimally when in fact there is no variation in a model's coefficients across one or more of the categorical variables (i.e. the approach pools over such variables with a high probability). An illustrative application demonstrates potential benefits for applied researchers. Copyright © 2011 John Wiley & Sons, Ltd.

The Effect of Fragmentation in Trading on Market Quality in the UK Equity Market

Abstract: Summary We investigate the effects of fragmentation in equity markets on the quality of trading outcomes in a panel of FTSE stocks over the period 2008–2011. This period coincided with a great deal of turbulence in the UK equity markets, which had multiple causes that need to be controlled for. To achieve this, we use the common correlated effects estimator for large heterogeneous panels. We extend this estimator to quantile regression to analyse the whole conditional distribution of market quality. We find that both fragmentation in visible order books and dark trading that is offered outside the visible order book lower volatility. But dark trading increases the variability of volatility, while visible fragmentation has the opposite effect, in particular at the upper quantiles of the conditional distribution. The transition from a monopolistic to a fragmented market is non-monotonic with respect to the degree of fragmentation. Copyright © 2015 John Wiley & Sons, Ltd.

Duration dependence versus unobserved heterogeneity in treatment effects: Swedish labor market training and the transition rate to employment

Abstract: SUMMARY The effect of a treatment on the hazard rate of a duration outcome may depend on the elapsed time since treatment. In addition, treatment effects may be heterogeneous across agents. The former gives rise to duration dependence of the treatment effect, whereas unobserved heterogeneity gives rise to spurious duration dependence of the observable hazard rate. We develop a model allowing for duration dependence and unobserved heterogeneity in the treatment effect. The model incorporates a Timing of Events model and allows for selectivity on unobservables. We prove identification, exploiting variation in the timing of treatment and outcome. In the application we analyze the effects of the Swedish vocational employment training program on the individual transition rate from unemployment to work. We demonstrate the appropriateness of the approach by studying the enrollment process. The data cover the population and include multiple unemployment spells for many individuals. The results indicate a large, significantly positive effect on exit to work shortly after exiting the program. The effect at the individual level diminishes after some weeks. When taking account of the time spent in the program, the effect on the mean unemployment duration is small. Copyright © 2012 John Wiley & Sons, Ltd.

Investment decisions in manufacturing: Assessing the effects of real oil prices and their uncertainty

Abstract: SUMMARY We investigate the effects of real oil prices and their uncertainty on investment decisions. Making use of plant-level data, we estimate dynamic, discrete-choice models that allow modeling investment inaction, under different assumptions related to initial conditions and unobserved heterogeneity. We find that increases in real oil price changes and in real oil price uncertainty significantly reduce the likelihood of investment action, in line with the predictions of irreversible investment theory. We also document that investment decisions exhibit strong, pure state dependence and are also significantly affected by initial conditions. Copyright © 2012 John Wiley & Sons, Ltd.

Euclidean revealed preferences: testing the spatial voting model

Abstract: SUMMARY In the spatial model of voting, voters choose the candidate closest to them in the ideological space. Recent work by Degan and Merlo in 2009 shows that it is falsifiable on the basis of individual voting data in multiple elections. We show how to tackle the fact that the model only partially identifies the distribution of voting profiles and we give a formal revealed preference test of the spatial voting model in three national elections in the USA, and strongly reject the spatial model in all cases. We also construct confidence regions for partially identified voter characteristics in an augmented model with unobserved valence dimension, and identify the amount of voter heterogeneity necessary to reconcile the data with spatial preferences. Copyright © 2011 John Wiley & Sons, Ltd.