Operations Research
http://lists.repec.org/mailman/listinfo/nep-ore
Operations Research
2021-01-18
Forecasting Daily Volatility of Stock Price Index Using Daily Returns and Realized Volatility
http://d.repec.org/n?u=RePEc:hit:hiasdp:hias-e-104&r=ore
This paper compares the volatility predictive abilities of some time-varying volatility models such as thestochastic volatility (SV) and exponential GARCH (EGARCH) models using daily returns, the heterogeneous au-toregressive (HAR) model using daily realized volatility (RV) and the realized SV (RSV) and realized EGARCH(REGARCH) models using the both. The data are the daily return and RV of Dow Jones Industrial Aver-age (DJIA) in US and Nikkei 225 (N225) in Japan. All models are extended to accommodate the well-knownphenomenon in stock markets of a negative correlation between today's return and tomorrow's volatility. Weestimate the HAR model by the ordinary least squares (OLS) and the EGARCH and REGARCH models bythe quasi-maximum likelihood (QML) method. Since it is not straightforward to evaluate the likelihood of theSV and RSV models, we apply a Bayesian estimation via Markov chain Monte Carlo (MCMC) to them. Byconducting predictive ability tests and analyses based on model confidence sets, we confirm that the models us-ing RV outperform the models without RV, that is, the RV provides useful information on forecasting volatility.Moreover, we find that the realized SV model performs best and the HAR model can compete with it. Thecumulative loss analysis suggests that the differences of the predictive abilities among the models are partlycaused by the rise of volatility.
Takahashi, Makoto
Watanabe, Toshiaki
Omori, Yasuhiro
Exponential GARCH (EGARCH) model, Heterogeneous autoregressive (HAR) model, Markov chain Monte Carlo (MCMC), Realized volatility, Stochastic volatility, Volatility forecasting
2021-01
Thesenpapier: Managing combined power and heat portfolios in sequential spot power markets under uncertainty
http://d.repec.org/n?u=RePEc:dui:wpaper:2003&r=ore
The integrated provision of energy among various energy sectors plays an important role in the process of decarbonisation of large energy systems. An important pillar is thereby the decarbonisation of the heat sector, where nowadays still a large percentage of heat supply originates from high-emission fossil fuels like coal or oil. In Central Europe, combined heat and power (CHP) plant applications, e.g. in local district heating networks, represent established methods to provide both electricity and heat at the same time, lowering overall fuel demands and lowering concomitant emissions. Heat pumps, converting electricity into heat, are also increasingly adopted by commercial (and household) customers. However, the optimal marketing and production scheduling of the heat and power- providing portfolios under price uncertainty is a challenging and often complex task. The importance of proper uncertainty handling is underscored even more if the optimal dispatch of flexible technologies like storages needs to be considered. In this paper, we propose an enhanced multi-stage stochastic programming model for coordinated bidding in two sequential markets, namely the one-hour and the fifteen-minute electricity products in the German (day-ahead) spot market. Our study develops and applies a stochastic mixed-integer linear programming model for a virtual power plant, acting as a price taker in the mentioned electricity markets. The model determines the optimal bidding strategies for a heterogeneous portfolio of small gas-fired motor- CHP units, heat pumps, electric storage heaters and battery storage systems. Thereby, we introduce a novel approach to construct piece-wise linear bidding curves for these markets, choosing their supporting points based on the simulated price paths. For the evaluation of the benefits of decision-making by help of stochastic modelling and optimization with different scenario numbers, we develop a new concept, the Benefit of Stochastic Optimization (BSO) and reflect and contrast our results with the computational burden of stochastic simulation, using the example of a real-world portfolio. We find that stochastic optimisation is a valuable optimisation method that may inform and improve individual marketerâ€™s decision-making processes. However, the observable additional benefits, i.e. compared to deterministic point forecasts, are limited in the investigated cases, while computational expensiveness increases significantly when adding further scenarios.
Andreas Dietrich
Christian Furtwängler
Christoph Weber
stochastic optimization, combined heat and power, virtual power plant, value of stochastic simulation
Estimation of Varying Coefficient Models with Measurement Error
http://d.repec.org/n?u=RePEc:cep:stiecm:607&r=ore
We propose a semiparametric estimator for varying coeﬃcient models when the regressors in the nonparametric component are measured with error. Varying coeﬃcient models are an extension of other popular semiparametric models, including partially linear and nonparametric additive models, and deliver an attractive solution to the curse-of-dimensionality. We use deconvolution kernel estimation in a two-step procedure and show that the estimator is consistent and asymptotically normally distributed. We do not assume that we know the distribution of the measurement error a priori, nor do we assume that the error is symmetrically distributed. Instead, we suppose we have access to a repeated measurement of the noisy regressor and use the approach of Li and Vuong (1998) based on Kotlarski's (1967) identity. We show that the convergence rate of the estimator is signiﬁcantly reduced when the distribution of the measurement error is assumed unknown and possibly asymmetric. Finally, we study the small sample behaviour of our estimator in a simulation study.
Hao Dong
Taisuke Otsu
Luke Taylor
2019-11
Reweighted nonparametric likelihood inference for linear functionals
http://d.repec.org/n?u=RePEc:cep:stiecm:614&r=ore
This paper is concerned with inference on finite dimensional parameters in semiparametric moment condition models, where the moment functionals are linear with respect to unknown nuisance functions. By exploiting this linearity, we reformulate the inference problem via the Riesz representer, and develop a general inference procedure based on nonparametric likelihood. For treatment effect or missing data analysis, the Riesz representer is typically associated with the inverse propensity score even though the scope of our framework is much wider. In particular, we propose a two-step procedure, where the first step computes the projection weights to approximate the Riesz representer, and the second step re-weights the moment conditions so that the likelihood increment admits an asymptotically pivotal chi-square calibration. Our re-weighting method is naturally extended to inference on treatment effects and data combination models, and other semiparametric problems. Simulation and empirical examples illustrate usefulness of the proposed method.
Karun Adusumilli
Taisuke Otsu
Chen Qiu
Nonparametric likelihood, Linear functional, Balancing weights
2020-11
Bull and Bear Markets During the COVID-19 Pandemic
http://d.repec.org/n?u=RePEc:pra:mprapa:104504&r=ore
The COVID-19 pandemic has caused severe disruption to economic and financial activity worldwide. We assess what happened to the aggregate U.S. stock market during this period, including implications for both short and long-horizon investors. Using the model of Maheu, McCurdy and Song (2012), we provide smoothed estimates and out-of-sample forecasts associated with stock market dynamics during the pandemic. We identify bull and bear market regimes including their bull correction and bear rally components, demonstrate the model's performance in capturing periods of significant regime change, and provide forecasts that improve risk management and investment decisions. The paper concludes with out-of-sample forecasts of market states one year ahead.
Maheu, John M
McCurdy, Thomas H
Song, Yong
predictive density, long-horizon returns, Markov switching
2020-11
Jackknife Lagrange multiplier test with many weak instruments
http://d.repec.org/n?u=RePEc:cep:stiecm:613&r=ore
This paper proposes a jackknife Lagrange multiplier (JLM) test for instrumental variable regression models, which is robust to (i) many instruments, where the number of instruments may increase proportionally with the sample size, (ii) arbitrarily weak instruments, and (iii) heteroskedastic errors. To the best of our knowledge, currently there is no asymptotically size correct test in this setting. Our idea is to modify the score statistic by jackknifing and to construct its heteroskedasticity robust variance estimator. Compared to Hansen, Hausman and Newey's (2008) modification for many instruments on the LM test by Kleibergen (2002) and Moreira (2001), our JLM test is robust for heteroskedastic errors and may circumvent possible decrease in the power function. Simulation results illustrate the desirable size robustness and power properties of the proposed method.
Yukitoshi Matsushita
Taisuke Otsu
many instruments, weak instruments, Lagrange multiplier test, jackknife
2020-08
Semiparametric Estimation and Model Selection for Conditional Mixture Copula Models
http://d.repec.org/n?u=RePEc:kan:wpaper:202104&r=ore
Conditional copula models allow the dependence structure among variables to vary with covariates, and thus can describe the evolution of the dependence structure with those factors. This paper proposes a conditional mixture copula which is a weighted average of several individual conditional copulas. We allow both the weights and copula parameters to vary with a covariate so that the conditional mixture copula offers additional flexibility and accuracy in describing the dependence structure. We propose a two-step semiparametric estimation method and develop asymptotic properties of the estimators. Moreover, we introduce model selection procedures to select the component copulas of the conditional mixture copula model. Simulation results suggest that the proposed procedures have a good performance in estimating and selecting conditional mixture copulas with different model specifications. The proposed model is then applied to investigate how the dependence structures among international equity markets evolve with the volatility in the exchange rate markets.
Guannan Liu
Wei Long
Bingduo Yang
Zongwu Cai
Conditional copula; Mixture copula; Model selection; Semiparametric estimation
2021-01
Monitoring Cointegrating Polynomial Regressions: Theory and Application to the Environmental Kuznets Curves for Carbon and Sulfur Dioxide Emissions
http://d.repec.org/n?u=RePEc:ihs:ihswps:27&r=ore
This paper develops residual-based monitoring procedures for cointegrating polynomial regressions, i. e., regression models including deterministic variables, integrated processes as well as integer powers of integrated processes as regressors. The regressors are allowed to be endogenous and the stationary errors are allowed to be serially correlated. We consider five variants of monitoring statistics and develop the results for three modified least squares estimators for the parameters of the CPRs. The simulations show that using the combination of self-normalization and a moving window leads to the best performance. We use the developed monitoring statistics to assess the structural stability of environmental Kuznets curves (EKCs) for both CO2and SO2 emissions for twelve industrialized country since the first oil price shock.
Knorre, Fabian
Wagner, Martin
Grupe, Maximilian
Cointegrating Polynomial Regression, Environmental Kuznets Curve, Monitoring, Structural Change
2020-12
The Leontief Paradox Redux
http://d.repec.org/n?u=RePEc:keo:dpaper:2020-018&r=ore
Shortly after Leamer (1980) found that the Leontief Paradox was based on a simple conceptual misunderstanding, Brecher and Choudhri (1982) argued that the fact that the United States exported labor services was, in itself, paradoxical because it is true if and only if its per-capita consumption is less than the world average. Surprisingly, however, no formal answer to this paradox has been provided for nearly four decades. This paper revisits this paradox and formally shows that the paradox can be resolved if the Heckscher-Ohlin-Vanek model takes into account technology differences across countries and trade imbalance. In contrast, the paradox cannot be resolved even if the analysis takes into account quasi-homothetic preferences, the Armington home bias, or offshoring.
Kozo Kiyota
Leontief Paradox, Technology differences, Trade imbalance, Nonhomothetic preferences, Armington home bias, offshoring
2020-10-07
Nonparametric intermediate order regression quantiles
http://d.repec.org/n?u=RePEc:cep:stiecm:608&r=ore
This paper studies nonparametric estimation of d-dimensional conditional quantile functions and their derivatives in the tails. We investigate asymptotic properties of the local and global nonparametric quantile regression estimators proposed by Chaudhuri (1991a, b), respectively, under the intermediate order quantile asymptotics: as the sample size n goes to inﬁnity, the quantile αn and a bandwidth parameter δn satisfy αn → 0 and nδd nαn → ∞ (or αn → 1 and nδd n(1−αn) →∞). We derive the pointwise convergence rate and asymptotic distribution of the local nonparametric quantile regression estimator, and the sup-norm convergence rate of the global nonparametric quantile regression estimator. Our results complement the papers by Chaudhuri (1991a, b), where the quantile αn does not vary with n, and Chernozhukov (1998), where the quantile αn satisﬁes αn → 0 and nδd nαn → 0.
Joseph Altonji
Hidehiko Ichimura
Taisuke Otsu
Quantile regression, Local polynomial regression: Extremes
2019-11
Empirical Monte Carlo Evidence on Estimation of Timing-of-Events Models
http://d.repec.org/n?u=RePEc:iza:izadps:dp14015&r=ore
This paper builds on the Empirical Monte Carlo simulation approach developed by Huber et al. (2013) to study the estimation of Timing-of-Events (ToE) models. We exploit rich Swedish data of unemployed job-seekers with information on participation in a training program to simulate placebo treatment durations. We first use these simulations to examine which covariates are key confounders to be included in selection models. The joint inclusion of specific short-term employment history indicators (notably, the share of time spent in employment), together with baseline socio-economic characteristics, regional and inflow timing information, is important to deal with selection bias. Next, we omit subsets of explanatory variables and estimate ToE models with discrete distributions for the ensuing systematic unobserved heterogeneity. In many cases the ToE approach provides accurate effect estimates, especially if time-varying variation in the unemployment rate of the local labor market is taken into account. However, assuming too many or too few support points for unobserved heterogeneity may lead to large biases. Information criteria, in particular those penalizing parameter abundance, are useful to select the number of support points.
Lombardi, Stefano
van den Berg, Gerard J.
Vikström, Johan
duration analysis, unemployment, propensity score, matching, training, employment
2021-01
Exact simulation of two-parameter Poisson-Dirichlet random variables
http://d.repec.org/n?u=RePEc:ehl:lserod:107937&r=ore
Consider a random vector (V1, . . . , Vn) where {Vk}k=1,...,n are the first n components of a two-parameter Poisson-Dirichlet distribution P D(α, θ). In this paper, we derive a decomposition for the components of the random vector, and propose an exact simulation algorithm to sample from the random vector. Moreover, a special case arises when θ/α is a positive integer, for which we present a very fast modified simulation algorithm using a compound geometric representation of the decomposition. Numerical examples are provided to illustrate the accuracy and effectiveness of our algorithms.
Dassios, Angelos
Zhang, Junyi
two-parameter Poisson-Dirichlet distribution; exact simulation; subordinator
2020-12-17
Switching Regressions with Imperfect Regime Classification Information: Theory and Applications
http://d.repec.org/n?u=RePEc:cep:stiecm:610&r=ore
V A Hajivassiliou
Switching regressions models, Measurement Errors, Trigger-price mechanisms, Price- ,xing
2019-11
Should Stock Returns Predictability be hooked on Long Horizon Regressions?
http://d.repec.org/n?u=RePEc:mcd:mcddps:2021_03&r=ore
This paper re-examines stock returns predictability over the business cycle using price-dividend and price-earnings valuation ratios as predictors. Unlike prior studies that habitually implement long-horizon/predictive regressions, we conduct a testing framework in the frequency domain. Predictive regressions support no predictability; in contrast, our results in the frequency domain verify significant predictability at medium and long horizons. To robustify predictability patterns, the analysis is executed repetitively for fixed-length rolling samples of various sizes. Overall, stock returns are predictable for wavelengths higher than five years. This finding is robust and independent of time, window size and predictor.
Theologos Dergiades
Panos K. Pouliasis
Stock Returns; Long-Horizon Predictability, Frequency Domain.
2021-02
Estimating Partially Conditional Quantile Treatment Effects
http://d.repec.org/n?u=RePEc:kan:wpaper:202103&r=ore
This paper proposes a new model, termed as the partially conditional quantile treatment effect model, to characterize the heterogeneity of treatment effect conditional on some predetermined variable(s). We show that this partially conditional quantile treatment effect is identified under the assumption of selection on observables, which leads to a semiparametric estimation procedure in two steps: first, parametric estimation of the propensity score function and then, nonparametric estimation of conditional quantile treatment effects. Under some regularity conditions, the consistency and asymptotic normality of the proposed semiparametric estimator are derived. Furthermore, the finite sample performance of the proposed method is illustrated through Monte Carlo experiments. Finally, we apply our methods to estimate the quantile treatment effects of a first-time motherÕs smoking during the pregnancy on the babyÕs weight as a function of the motherÕs age, and our empirical results show substantial heterogeneity across different motherÕs ages with a significant negative effect of smoking on infant birth weight across all motherÕs ages and quantiles considered.
Zongwu Cai
Ying Fang
Ming Lin
Shengfang Tang
Conditional quantile treatment effect; Heterogeneity; Propensity score; Semiparametric estimation; Treatment effect on treated
2021-01
Semi-Structural VAR and Unobserved Components Models to Estimate Finance-Neutral Output Gap
http://d.repec.org/n?u=RePEc:bfr:banfra:791&r=ore
The paper assesses the impact of adding information on financial cycles on the output gap estimates for eight advanced economies using two unobserved components models: a reduced form extended Hodrick-Prescott filter, and a standard semi-structural unobserved components model. To complement these models, a semi-structural vector autoregression model is proposed in which only supply shocks are identified. The accuracy of the output gap estimates is assessed based on their performance in predicting recessions. The models with financial variables generally produce more accurate output gap estimates at the expense of increased real-time volatility. While the extended Hodrick-Prescott filter is particularly appealing for its real-time stability, it lags behind the two semi-structural models in terms of forecasting performance. The vector autoregression model augmented with financial variables features the best in-sample forecasting performance, and it has similar real-time prediction capabilities to the semi-structural unobserved components model. Overall, financial cycles appear to be relevant in Japan, Spain, the UK, and – to a lesser extent – in the US and in France, while they are relatively muted in Canada, Germany, and Italy.
Kátay Gábor
Kerdelhué Lisa
Lequien Matthieu
Unobserved Components model, semi-structural VAR, output gap, financial cycle, sustainable growth, credit, house prices, advanced economies.
2020
Novel Approaches to Coherency Conditions in Dynamic LDV Models: Quantifying Financing Constraints and a Firm's Decision and Ability to Innovate
http://d.repec.org/n?u=RePEc:cep:stiecm:606&r=ore
We develop novel methods for establishing coherency conditions in Static and Dynamic Limited Dependent Variables (LDV) Models. We propose estimation strategies based on Conditional Maximum Likelihood Estimation for simultaneous LDV models without imposing recursivity. Monte-Carlo experiments confirm substantive Mean-Squared-Error improvements of our approach over other estimators. We analyse the impact of financing constraints on innovation: ceteris paribus, a firm facing binding finance constraints is substantially less likely to undertake innovation, while the probability that a firm encounters a binding finance constraint more than doubles if the firm is innovative. A strong role for state dependence in dynamic versions of our models is also established.
V A Hajivassiliou
Frédérique Savignac
Frédérique Savignac
Financing Constraints, Innovation, Dynamic Limited Dependent Variable Models, Joint Bivariate Probit Model, Econometric Coherency Conditions, State Dependence
2019-10
Bias and Discrimination: What Do We Know?
http://d.repec.org/n?u=RePEc:iza:izadps:dp13983&r=ore
The paper presents the economic literature on gender bias, illustrating the underpinnings in the psychology of bias and stereotyping; the incorporation of these insights into current theoretical and empirical research in economics, and the literature on methods to contrast bias presenting evidence (where it exists) of their effectiveness. The second part of the paper presents results of an experiment in revealing unconscious bias.
Della Giusta, Marina
Bosworth, Steven J.
discrimination, gender, unconscoius bias, licensing
2020-12
The Limit of the Non-dictatorship Index
http://d.repec.org/n?u=RePEc:cvh:coecwp:2020/06&r=ore
In this paper we determine the asymptotic behavior of the Non-dictatorship Index (NDI) introduced in Bednay, Moskalenko and Tasnádi (2019). We show that if m denotes the number of alternatives, then as the number of voters tends to infinity the NDI of any anonymous voting rule tends to (m − 1)/m, which equals the NDI of the constant rule.
Bednay, Dezső
Fleiner, Balázs
Tasnádi, Attila
voting rules, dictatorship, non-dictatorship index
2020-12-29
Day-ahead electricity prices prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling
http://d.repec.org/n?u=RePEc:arx:papers:2101.05249&r=ore
The availability of accurate day-ahead electricity price forecasts is pivotal for electricity market participants. In the context of trade liberalisation and market harmonisation in the European markets, accurate price forecasting becomes even more difficult to obtain. The increasing power market integration has complicated the forecasting process, where electricity forecasting requires considering features from both the local market and ever-growing coupling markets. In this paper, we apply state-of-the-art deep learning models, combined with feature selection algorithms for electricity price prediction under the consideration of market coupling. We propose three hybrid architectures of long-short term memory (LSTM) deep neural networks and compare the prediction performance, in terms of various feature selections. In our empirical study, we construct a broad set of features from the Nord Pool market and its six coupling countries for forecasting the Nord Pool system price. The results show that feature selection is essential to achieving accurate prediction. Superior feature selection algorithms filter meaningful information, eliminate irrelevant information, and further improve the forecasting accuracy of LSTM-based deep neural networks. The proposed models obtain considerably accurate results.
Wei Li
Denis Mike Becker
2021-01
How To Go Viral: A COVID-19 Model with Endogenously Time-Varying Parameters
http://d.repec.org/n?u=RePEc:fip:fedrwp:88807&r=ore
This paper estimates a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. The paper's Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point increase in the death rate among reported cases. The model forecasts perform well, even relative to models from epidemiology and statistics.
Paul Ho
Thomas A. Lubik
Christian Matthes
Bayesian Estimation; Panel; Time-Varying Parameters
2020-08-21
Second-order refinements for t-ratios with many instruments
http://d.repec.org/n?u=RePEc:cep:stiecm:612&r=ore
This paper studies second-order properties of the many instruments robust t-ratios based on the limited information maximum likelihood and Fuller estimators for instrumental variable regression models under the many instruments asymptotics, where the number of instruments may increase proportionally with the sample size n, and proposes second-order refinements to the t-ratios to improve the size and power properties. Based on asymptotic expansions of the null and non-null distributions of the t-ratios derived under the many instruments asymptotics, we show that the second order terms of those expansions may have non-trivial impacts on the size as well as the power properties. Furthermore, we propose adjusted t-ratios whose approximation errors for the null rejection probabilities are of order O(n^{-1}) in contrast to the ones for the unadjusted t-ratios of order O(n^{-1/2}), and show that these adjustments induce some desirable power properties in terms of the local maximinity.
Yukitoshi Matsushita
Taisuke Otsu
simultaneous equation, many instrumental variables, higher order expansion
2020-05
The Analysis and the Measurement of Poverty: An Interval Based Composite Indicator Approach
http://d.repec.org/n?u=RePEc:pra:mprapa:104462&r=ore
The analysis and measurement of poverty is a crucial issue in the field of social science. Poverty is a multidimensional notion that can be measured using composite indicators relevant to synthesizing statistical indicators. Subjective choices could, however, affect these indicators. We propose interval-based composite indicators to avoid the problem, enabling us in this context to obtain robust and reliable measures. Based on a relevant conceptual model of poverty we have identified, we will consider all the various factors identified. Then, considering a different random configuration of the various factors, we will compute a different composite indicator. We can obtain a different interval for each region based on the distinct factor choices on the different assumptions for constructing the composite indicator. So we will create an interval-based composite indicator based on the results obtained by the Monte-Carlo simulation of all the different assumptions. The different intervals can be compared, and various rankings for poverty can be obtained. For their parameters, such as center, minimum, maximum, and range, the poverty interval composite indicator can be considered and compared. The results demonstrate a relevant and consistent measurement of the indicator and the shadow sector's relevant impact on the final measures.
Drago, Carlo
poverty, composite indicators, interval data, symbolic data
2020-12-01
Entrepreneurs are exposed to large uninsured risks. The risks may discourage them from creating productive assets. This may generate a productive asset shortage and stimulate demand for speculative bubbles. We introduce entrepreneurial risks into a textbook growth model with infinitely lived agents. In our model, entrepreneurs face no credit constraints. If the degree of entrepreneurial risks is in the middle range, bubbles are likely to emerge. If the degree of entrepreneurial risks is high, bubbles promote growth because bubbles work as a buffer for the risks. Otherwise, bubbles lower growth. The effect of the collapse of bubbles also depends on the degree of the risks. Moreover, asset bubbles amplify fundamental shocks.
http://d.repec.org/n?u=RePEc:kyo:wpaper:1052&r=ore
Takeo Hori
Ryonghun Im
asset bubbles, idiosyncratic risks, incomplete insurance, amplification, growth effect, welfare analysis.
2021-01
Sequential Trading With Coarse Contingencies
http://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2020_254&r=ore
We consider a dynamic economy in which agents are initially unaware of some risks. As awareness of these risks emerges, markets re-open so agents can re-optimize and purchase insurance. An inefficiency may nonetheless arise as the cost of insurance is not spread over time. This ``savings mistake" does not arise in two benchmark cases. In those, the ability to re-trade fully negates the initial misperception of risks. We also demonstrate the possibility of unexpected default. This arises when agents borrow "too much" and once perceptions change, there is no equilibrium price at which they can refinance their debt.
Sarah Auster
Jeremy Kettering
Asen Kochov
coarse perceptions, unforeseen risks, sequential trading, default
2021-01
Set-up Costs and the Financing of Young Firms
http://d.repec.org/n?u=RePEc:bfr:banfra:792&r=ore
We show that set-up costs are a key determinant of the capital structure of young firms. Theoretically, when firms face high set-up costs, they can only be established by leveraging up and lengthening debt maturity. Empirically, we use a large sample of French firms to show that young firms have a significantly higher leverage and issue longer-maturity debt than seasoned companies. As predicted by the model, these patterns are stronger in high set-up cost industries and for firms with lower profitability. Last, we show that, following an exogenous shock that reduces banks' supply of long-term loans, young firms in high set-up cost industries grow significantly less.
François Derrien
Mésonnier Jean-Stéphane
Vuillemey Guillaume
Young firms, capital structure, set-up costs, leverage, debt maturity, financial frictions.
2020
Beyond the Origin Dummy: Heterogeneity of Ethnicity and Human Capital Accumulation
http://d.repec.org/n?u=RePEc:iza:izadps:dp14019&r=ore
Ethnic background is a well recognized complementary factor in human capital accumulation process. This paper investigates how three aspects of ethnicity affect human capital formation: group's quality, size and closeness of ties. Relying on heteroskedasticity to identify parameters in the presence of endogenous regressors, I find evidence of heterogenous effects of ethnicity for men and women. The results show that women in groups characterized by close ties benefit from high-quality ethnic environment, regardless of the group's size. In contrast, among men, the group's size appears to be of importance, and men in large groups characterized by loose ties benefit the most from higher quality of the ethnic group. The results are consistent with different socialization patterns, as evidenced by the literature.
Postepska, Agnieszka
human capital formation, ethnicity, endogamy, networks, identification through conditional second moments
2021-01
Effort Comparisons for a Class of Four-Player Tournaments
http://d.repec.org/n?u=RePEc:ces:ceswps:_8761&r=ore
We propose a novel tournament design that incorporates the main properties of a round-robin tournament, a Swiss tournament, and a race. Following an equilibrium analysis, we compare 36 tournament structures inherent in our model and several well-known tournament models from the literature, on the basis of expected total equilibrium effort. We show that two of the tournament structures we introduce outperform all the other tournament structures considered.
Deren Caglayan
Emin Karagözoglu
Kerim Keskin
Cagri Saglam
contest, multi-player contest, race, round-robin tournament, Swiss tournament, tournament
2020
Energy Expenditure in Egypt: Empirical Evidence Based on a Quantile Regression Approach
http://d.repec.org/n?u=RePEc:iza:izadps:dp14011&r=ore
This paper investigates the key factors affecting household energy expenditure in Egypt. Based upon the latest 2015 Egyptian HIECS Survey, we develop a quantile regression model with an innovative variable selection approach via Adaptive Lasso Regularization technique to untangle the spectrum of household energy expenditure. Unsurprisingly, income, age, household size, housing size, and employment status are salient predictors for energy expenditure. Housing characteristics have a moderate impact, while socio-economic attributes have a much larger one. The largest variations in household energy expenditures in Egypt are mainly due to variations in income, household size, and housing type. Our findings document substantial differences in household energy expenditure, originating from the asymmetric tails of the energy expenditure distribution. This outcome highlights the added value of implementing quantile regression methods. Our empirical results have various interesting policy implications regarding residential energy efficiency and carbon emissions reduction in Egypt.
Belaïd, Fateh
Rault, Christophe
residential energy expenditure, energy efficiency, quantile regression, adaptive lasso, Egypt
2021-01
Market Pricing of Fundamentals at the Shanghai Stock Exchange: Evidence from a Dividend Discount Model with Adaptive Expectations
http://d.repec.org/n?u=RePEc:wyi:wpaper:002582&r=ore
We study market pricing of fundamentals at the Shanghai Stock Exchange, incorporating possible irrational pricing behavior with adaptive expectation. Using panel data of listed stocks to overcome the limited information in aggregate time series data, we estimated key parameters of the price elasticity of dividends and the expectation adjustment based on a linear dynamic panel data model. We use a major subset of stocks with stationary real prices and cash flows and apply methods that correct for incidental parameter bias. The resulting price elasticity of dividends is about 0.46 (0.35) based on annual (quarterly) data, which is sizable given high PD (PE) ratios in the market. Our results imply that slow expectation adjustment contributes to “bubble-like” price patterns. We also show prices significantly react to macro information related to the discount rate, but these effects are very sensitive to the information set used.
Mingyang Li
Linlin Niu
Andrew Pua
Stock price determination, Adaptive expectation, Time-varying discount rate, Incidental parameter bias
2020-12-30
Inequality in models with a competition for market shares
http://d.repec.org/n?u=RePEc:zur:econwp:375&r=ore
This paper develops a framework to systematically study how changes in market conditions affect the equilibrium inequality between heterogeneous agents. By stating our setting as a "competition for market shares", we can derive inequality predictions for vastly different competition models. This approach allows us to identify a common structure, e.g., in monopolistic competition, perfect competition, or competition for prizes, that explains why these models deliver similar inequality predictions. We apply our results to problems from trade, competition theory, consumption inequality, political economics and marketing, and relate some of the predicted inequality patterns to empirical evidence.
Andreas Hefti
Julian Teichgräber
Inequality analysis, market shares, power functions, monopolistic competition, perfect competition competition for prizes
2021-01
Nitrate Pollution and Efficiency Measurement in Intensive Farming Systems: A Parametric By-Production Technology Approach
http://d.repec.org/n?u=RePEc:crt:wpaper:2101&r=ore
This paper develops a novel empirical framework for estimating individual emission levels in a nonpoint source (NPS) pollution problem. For doing so we incorporate into the GME model suggested by Kaplan et al., (2003) a specific theoretical structure describing both crop production technology and nature's residual generating mechanism based on the multiple production relations model suggested by Murty et al, (2012) fitted into a parametric stochastic framework.
Michail Tsagris
Vangelis Tzouvelekas
nitrogen leaching, multiple production relations, Generalized Maximum Entropy, greenhouse farming, Crete
2021-01-05
Optimal Minimax Rates against Non-smooth Alternatives
http://d.repec.org/n?u=RePEc:kyo:wpaper:1051&r=ore
This study investigates optimal minimax rates for specification testing when the alternative hypothesis is built on a set of non-smooth functions. The set consists of bounded functions that are not necessarily differentiable with no smoothness constraints imposed on their derivatives. In the instrumental variable regression set up with an unknown error variance structure, we find that the optimal minimax rate is n−1/4, where n is the sample size. The rate is achieved by a simple test based on the difference between non-parametric and parametric variance estimators.
Kohtaro Hitomi
Masamune Iwasawa
Yoshihiko Nishiyama
optimal minimax rate; specification test; instrumental variable regression model; nearest neighbor method
2020-12
MStatistical Discrimination in a Search Equilibrium Model: Racial Wage and Employment Disparities in the US
http://d.repec.org/n?u=RePEc:lie:wpaper:82&r=ore
In the US, black workers spend more time in unemployment, lose their jobs more rapidly, and earn lower wages than white workers. This paper quantifies the contributions of statistical discrimination, as portrayed by negative stereotyping and screening discrimination, to such employment and wage disparities. We develop an equilibrium search model of statistical discrimination with learning based on Moscarini (2005) and estimate it by indirect inference. We show that statistical discrimination alone cannot simultaneously explain the observed differences in residual wages and monthly job loss probabilities between black and white workers. However, a model with negative stereotyping, larger unemployment valuation and faster learning about the quality of matches for black workers can account for these facts. One implication of our findings is that black workers have larger returns to tenure.
Linas Tarasonis
Bruno Decreuse
Learning; Screening discrimination; Job search; Indirect inference
2020-12-18
Rules and Mutation - A Theory of How Efficiency and Rawlsian Egalitarianism/Symmetry May Emerge
http://d.repec.org/n?u=RePEc:cam:camdae:2101&r=ore
For any game, we provide a justification for why in the long-run outcomes are mostly both efficient and egalitarian/symmetric in the Rawlsian sense. We do this by constructing an adaptive dynamic framework with four features. First, agents select rules to implement actions. Second, rule selection satisfies some minimal payoff monotonicity: rules that do best are chosen with a positive probability. Third, in choosing rules agents are subject to "small" random mutation. Fourth mutation is payoff-dependent with agents mutating more when they do badly than when they do well. Our main result is: if the set of feasible rules R is sufficiently rich then outcomes that survive maximise the payoff of the player that does least well. We also show that if R is restricted to those that do best-reply on uniform histories then outcomes that survive are efficient and egalitarian amongst the set of minimum weak CURB sets. Finally, we consider long-run outcomes assuming mutation is payoff-independent; in contrast to our strong selection result above, in this case we show indeterminacy: any outcome can survive if R is sufficiently rich.
Juang, W-T.
Sabourian, H.
2021-01-04
Social Security and Endogenous Demographic Change: Child Support and Retirement Policies
http://d.repec.org/n?u=RePEc:iza:izadps:dp14018&r=ore
This paper studies retirement and child support policies in a small, open, overlapping-generations economy with PAYG social security and endogenous retirement and fertility decisions. It demonstrates that neither fertility nor retirement choices necessarily coincide with socially optimal allocation, because agents do not take into account the externalities of fertility and the elderly labor supply in the economy as a whole. It shows that governments can realize the first-best allocation by introducing a child allowance scheme and a subsidy to incentivize the labor supply of older workers. As an alternative to subsidizing the elderly labor supply, we show that the first-best allocation can also be achieved by controlling the retirement age. Finally, the model is simulated in order to study whether the policies devoted to realizing the social optimum in a market economy could be a Pareto improvement.
Cipriani, Giam Pietro
Fioroni, Tamara
PAYG pensions, social security, endogenous fertility, endogenous retirement
2021-01
Nowcasting in a pandemic using non-parametric mixed frequency VARs
http://d.repec.org/n?u=RePEc:ecb:ecbwps:20212510&r=ore
This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of extreme observations, for instance those produced by the COVID-19 pandemic of 2020. This is due to their flexibility and ability to model outliers. In an application involving four major euro area countries, we find substantial improvements in nowcasting performance relative to a linear mixed frequency VAR. JEL Classification: C11, C32, C53, E37
Huber, Florian
Koop, Gary
Onorante, Luca
Pfarrhofer, Michael
Schreiner, Josef
Bayesian, macroeconomic forecasting, regression tree models, vector autoregressions
2021-01
Housing Search Methods and Residential Satisfaction
http://d.repec.org/n?u=RePEc:keo:dpaper:2020-019&r=ore
This paper examines the impact of housing search methods on subsequent residential satisfaction among Japanese homeowners. Our empirical results based on the Structural Equation Modeling (SEM) show that using the Internet or visiting housing exhibits during a search process can improve subsequent residential satisfaction. These results are consistent with the notion that these information search methods can alleviate information asymmetry and/or provide more information about properties available on market. Furthermore, the positive effects of these search methods on residential satisfaction can be observed for custom-build homes and second-hand homes but not for houses built for sale.
Hiroaki Niikura
Michio Naoi
Miki Seko
Housing search method, Residential satisfaction, Information asymmetry, Structural Equation Modeling
2020-10-19
Productivity outcomes in online labor markets and within-task complexity and difficultly
http://d.repec.org/n?u=RePEc:zbw:glodps:739&r=ore
We analyze the impact of within-task difficulty and complexity on workers' productivity in online labor markets. Using a randomized control quasi-experiment in AMT we are able to define the difficulty and complexity embodied in requested sub-tasks within a problem-solved task. We find that our productivity measures are negatively related to the difficulty and complexity of a specific sub-task. This finding is robust to several sources of workers' heterogeneity and to different pay schemes.
Mourelatos, Evaggelos
Giannakopoulos, Nicholas
Tzagarakis, Manolis
Productivity,Online Labor markets,Task Difficulty and Complexity
2020
What Can We Learn About Economics from Sport during Covid-19?
http://d.repec.org/n?u=RePEc:rdg:emxxdp:em-dp2021-01&r=ore
The economics of sport and how sport provides insights into economics have experienced exogenous shocks from Covid-19, facilitating many natural experiments. These have provided partial answers to questions of: how airborne viruses may spread in crowds; how crowds respond to the risk and information about infection; how the absence of crowds may affect social pressure and arbitration decisions; and how quickly betting markets respond to new information. We review this evidence and advise how sports economics research could continue to be most valuable to policymakers.
Carl Singleton
Alex Bryson
Peter Dolton
J. James Reade
Dominik Schreyer
Sports Economics, Coronavirus, Natural Experiments, Referee Bias, Social Pressure, Prediction Markets
2021-01-14
The rise of digital watchers
http://d.repec.org/n?u=RePEc:snb:snbwpa:2021-01&r=ore
Many consumers use payment instruments to control their budget. Previously, such behavior has been associated with checking disposable cash ("pocket watching"). Based on recent survey data, we show that "digital watchers" have emerged, i.e., noncash payers who use digital applications to control their budget. Both watcher types have distinct characteristics. Pocket watchers tend to have lower incomes than other consumers, while digital watchers ascribe low security risk to payment cards. Watching behavior influences current and future payment behaviors. Pocket watchers use cash more intensively than nonwatching cash payers. Digital watchers expect to intensify their reliance on noncash payment instruments more strongly than nonwatching noncash payers.
Till Ebner
Thomas Nellen
Jörn Tenhofen
Payment behavior, control motive, pocket watcher, digital watcher, survey data, central bank digital currency
2021
Inflation Expectations in the U.S.: Linking Markets, Households, and Businesses
http://d.repec.org/n?u=RePEc:imf:imfwpa:2020/240&r=ore
Inflation has been below the Federal Reserve’s target for much of the past 20 years, creating worries that inflation may be deanchoring from the FOMC’s target. This paper uses a factor model that incorporates information from professional forecasters, household and business surveys, and the market for Treasury inflation protected securities (TIPS) to estimate long-run inflation expectations. These have fallen notably in the past few years (to roughly 1.9 percent for CPI inflation, well below the FOMC’s target). It appears that, even before the covid recession, the private sector viewed the economy as likely to suffer from persistent headwinds to inflation.
Peter D. Williams
Inflation;Return on investment;Oil prices;Liquidity;Inflation targeting;inflation expectations,yield curve,factor modeling,no-arbitrage term structure model,TIPS,surveys,WP,inflation expectation,inflation risk premium,breakeven inflation series,CPI inflation,risk premia
2020-11-13