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Econometrics, Volume 6, Issue 3 (September 2018)

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Open AccessArticle Foreign Workers and the Wage Distribution: What Does the Influence Function Reveal?
Econometrics 2018, 6(3), 41; https://doi.org/10.3390/econometrics6030041
Received: 31 January 2018 / Revised: 31 August 2018 / Accepted: 31 August 2018 / Published: 7 September 2018
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Abstract
This paper draws upon influence function regression methods to determine where foreign workers stand in the distribution of private sector wages in Luxembourg, and assess whether and how much their wages contribute to wage inequality. This is quantified by measuring the effect that
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This paper draws upon influence function regression methods to determine where foreign workers stand in the distribution of private sector wages in Luxembourg, and assess whether and how much their wages contribute to wage inequality. This is quantified by measuring the effect that a marginal increase in the proportion of foreign workers—foreign residents or cross-border workers—would have on selected quantiles and measures of inequality. Analysis of the 2006 Structure of Earnings Survey reveals that foreign workers have generally lower wages than natives and therefore tend to haul the overall wage distribution downwards. Yet, their influence on wage inequality reveals small and negative. All impacts are further muted when accounting for human capital and, especially, job characteristics. Not observing any large positive inequality contribution on the Luxembourg labour market is a striking result given the sheer size of the foreign workforce and its polarization at both ends of the skill distribution. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available
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Open AccessArticle Using the Entire Yield Curve in Forecasting Output and Inflation
Econometrics 2018, 6(3), 40; https://doi.org/10.3390/econometrics6030040
Received: 17 June 2018 / Revised: 17 August 2018 / Accepted: 21 August 2018 / Published: 29 August 2018
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Abstract
In forecasting a variable (forecast target) using many predictors, a factor model with principal components (PC) is often used. When the predictors are the yield curve (a set of many yields), the Nelson–Siegel (NS) factor model is used in place of the PC
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In forecasting a variable (forecast target) using many predictors, a factor model with principal components (PC) is often used. When the predictors are the yield curve (a set of many yields), the Nelson–Siegel (NS) factor model is used in place of the PC factors. These PC or NS factors are combining information (CI) in the predictors (yields). However, these CI factors are not “supervised” for a specific forecast target in that they are constructed by using only the predictors but not using a particular forecast target. In order to “supervise” factors for a forecast target, we follow Chan et al. (1999) and Stock and Watson (2004) to compute PC or NS factors of many forecasts (not of the predictors), with each of the many forecasts being computed using one predictor at a time. These PC or NS factors of forecasts are combining forecasts (CF). The CF factors are supervised for a specific forecast target. We demonstrate the advantage of the supervised CF factor models over the unsupervised CI factor models via simple numerical examples and Monte Carlo simulation. In out-of-sample forecasting of monthly US output growth and inflation, it is found that the CF factor models outperform the CI factor models especially at longer forecast horizons. Full article
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Open AccessArticle The Stochastic Stationary Root Model
Econometrics 2018, 6(3), 39; https://doi.org/10.3390/econometrics6030039
Received: 31 March 2018 / Revised: 27 July 2018 / Accepted: 13 August 2018 / Published: 21 August 2018
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Abstract
We propose and study the stochastic stationary root model. The model resembles the cointegrated VAR model but is novel in that: (i) the stationary relations follow a random coefficient autoregressive process, i.e., exhibhits heavy-tailed dynamics, and (ii) the system is observed with measurement
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We propose and study the stochastic stationary root model. The model resembles the cointegrated VAR model but is novel in that: (i) the stationary relations follow a random coefficient autoregressive process, i.e., exhibhits heavy-tailed dynamics, and (ii) the system is observed with measurement error. Unlike the cointegrated VAR model, estimation and inference for the SSR model is complicated by a lack of closed-form expressions for the likelihood function and its derivatives. To overcome this, we introduce particle filter-based approximations of the log-likelihood function, sample score, and observed Information matrix. These enable us to approximate the ML estimator via stochastic approximation and to conduct inference via the approximated observed Information matrix. We conjecture the asymptotic properties of the ML estimator and conduct a simulation study to investigate the validity of the conjecture. Model diagnostics to assess model fit are considered. Finally, we present an empirical application to the 10-year government bond rates in Germany and Greece during the period from January 1999 to February 2018. Full article
(This article belongs to the Special Issue Celebrated Econometricians: Katarina Juselius and Søren Johansen)
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Open AccessEditorial Econometrics Best Paper Award 2018
Econometrics 2018, 6(3), 38; https://doi.org/10.3390/econometrics6030038
Received: 15 August 2018 / Accepted: 17 August 2018 / Published: 19 August 2018
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Open AccessArticle Detecting and Measuring Nonlinearity
Econometrics 2018, 6(3), 37; https://doi.org/10.3390/econometrics6030037
Received: 28 January 2018 / Revised: 18 March 2018 / Accepted: 2 August 2018 / Published: 9 August 2018
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Abstract
This paper proposes an approach to measure the extent of nonlinearity of the exposure of a financial asset to a given risk factor. The proposed measure exploits the decomposition of a conditional expectation into its linear and nonlinear components. We illustrate the method
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This paper proposes an approach to measure the extent of nonlinearity of the exposure of a financial asset to a given risk factor. The proposed measure exploits the decomposition of a conditional expectation into its linear and nonlinear components. We illustrate the method with the measurement of the degree of nonlinearity of a European style option with respect to the underlying asset. Next, we use the method to identify the empirical patterns of the return-risk trade-off on the SP500. The results are strongly supportive of a nonlinear relationship between expected return and expected volatility. The data seem to be driven by two regimes: one regime with a positive return-risk trade-off and one with a negative trade-off. Full article
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Open AccessArticle The Relation between Monetary Policy and the Stock Market in Europe
Econometrics 2018, 6(3), 36; https://doi.org/10.3390/econometrics6030036
Received: 21 March 2018 / Revised: 25 July 2018 / Accepted: 31 July 2018 / Published: 5 August 2018
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Abstract
We use a cointegrated structural vector autoregressive model to investigate the relation between monetary policy in the euro area and the stock market. Since there may be an instantaneous causal relation, we consider long-run identifying restrictions for the structural shocks and also used
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We use a cointegrated structural vector autoregressive model to investigate the relation between monetary policy in the euro area and the stock market. Since there may be an instantaneous causal relation, we consider long-run identifying restrictions for the structural shocks and also used (conditional) heteroscedasticity in the residuals for identification purposes. Heteroscedasticity is modelled by a Markov-switching mechanism. We find a plausible identification scheme for stock market and monetary policy shocks which is consistent with the second-order moment structure of the variables. The model indicates that contractionary monetary policy shocks lead to a long-lasting downturn of real stock prices. Full article
(This article belongs to the Special Issue Celebrated Econometricians: Katarina Juselius and Søren Johansen)
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Open AccessArticle Filters, Waves and Spectra
Econometrics 2018, 6(3), 35; https://doi.org/10.3390/econometrics6030035
Received: 17 March 2018 / Revised: 15 July 2018 / Accepted: 17 July 2018 / Published: 27 July 2018
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Abstract
Econometric analysis requires filtering techniques that are adapted to cater to data sequences that are short and that have strong trends. Whereas the economists have tended to conduct their analyses in the time domain, the engineers have emphasised the frequency domain. This paper
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Econometric analysis requires filtering techniques that are adapted to cater to data sequences that are short and that have strong trends. Whereas the economists have tended to conduct their analyses in the time domain, the engineers have emphasised the frequency domain. This paper places its emphasis in the frequency domain; and it shows how the frequency-domain methods can be adapted to cater to short trended sequences. Working in the frequency domain allows an unrestricted choice to be made of the frequency response of a filter. It also requires that the data should be free of trends. Methods for extracting the trends prior to filtering and for restoring them thereafter are described. Full article
(This article belongs to the Special Issue Filtering)
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Open AccessArticle Financial Big Data Solutions for State Space Panel Regression in Interest Rate Dynamics
Econometrics 2018, 6(3), 34; https://doi.org/10.3390/econometrics6030034
Received: 12 February 2018 / Revised: 19 June 2018 / Accepted: 7 July 2018 / Published: 18 July 2018
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Abstract
A novel class of dimension reduction methods is combined with a stochastic multi-factor panel regression-based state-space model in order to model the dynamics of yield curves whilst incorporating regression factors. This is achieved via Probabilistic Principal Component Analysis (PPCA) in which new statistically-robust
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A novel class of dimension reduction methods is combined with a stochastic multi-factor panel regression-based state-space model in order to model the dynamics of yield curves whilst incorporating regression factors. This is achieved via Probabilistic Principal Component Analysis (PPCA) in which new statistically-robust variants are derived also treating missing data. We embed the rank reduced feature extractions into a stochastic representation for state-space models for yield curve dynamics and compare the results to classical multi-factor dynamic Nelson–Siegel state-space models. This leads to important new representations of yield curve models that can be practically important for addressing questions of financial stress testing and monetary policy interventions, which can incorporate efficiently financial big data. We illustrate our results on various financial and macroeconomic datasets from the Euro Zone and international market. Full article
(This article belongs to the Special Issue Big Data in Economics and Finance)
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Open AccessArticle Some Results on 1 Polynomial Trend Filtering
Econometrics 2018, 6(3), 33; https://doi.org/10.3390/econometrics6030033
Received: 22 May 2018 / Revised: 30 June 2018 / Accepted: 4 July 2018 / Published: 10 July 2018
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Abstract
1 polynomial trend filtering, which is a filtering method described as an 1-norm penalized least-squares problem, is promising because it enables the estimation of a piecewise polynomial trend in a univariate economic time series without prespecifying the number and location
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1 polynomial trend filtering, which is a filtering method described as an 1-norm penalized least-squares problem, is promising because it enables the estimation of a piecewise polynomial trend in a univariate economic time series without prespecifying the number and location of knots. This paper shows some theoretical results on the filtering, one of which is that a small modification of the filtering provides not only identical trend estimates as the filtering but also extrapolations of the trend beyond both sample limits. Full article
(This article belongs to the Special Issue Filtering)
Open AccessArticle Econometric Fine Art Valuation by Combining Hedonic and Repeat-Sales Information
Econometrics 2018, 6(3), 32; https://doi.org/10.3390/econometrics6030032
Received: 11 December 2017 / Revised: 11 April 2018 / Accepted: 5 June 2018 / Published: 24 June 2018
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Abstract
Statistical methods are widely used for valuation (prediction of the value at sale or auction) of a unique object such as a work of art. The usual approach is estimation of a hedonic model for objects of a given class, such as paintings
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Statistical methods are widely used for valuation (prediction of the value at sale or auction) of a unique object such as a work of art. The usual approach is estimation of a hedonic model for objects of a given class, such as paintings from a particular school or period, or in the context of real estate, houses in a neighborhood. Where the object itself has previously sold, an alternative is to base an estimate on the previous sale price. The combination of these approaches has been employed in real estate price index construction (e.g., Jiang et al. 2015); in the present context, we treat the use of these different sources of information as a forecast combination problem. We first optimize the hedonic model, considering the level of aggregation that is appropriate for pooling observations into a sample, and applying model-averaging methods to estimate predictive models at the individual-artist level. Next, we consider an additional stage in which we incorporate repeat-sale information, in a subset of cases for which this information is available. The methods are applied to a data set of auction prices for Canadian paintings. We compare the out-of-sample predictive accuracy of different methods and find that those that allow us to use single-artist samples produce superior results, that data-driven averaging across predictive models tends to produce clear gains, and that, where available, repeat-sale information appears to yield further improvements in predictive accuracy. Full article
(This article belongs to the Special Issue Celebrated Econometricians: Peter Phillips)
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