Operations Research
http://lists.repec.org/mailman/listinfo/nep-ore
Operations Research
2018-05-21
Specification tests for non-Gaussian maximum likelihood estimators
http://d.repec.org/n?u=RePEc:rim:rimwps:18-22&r=ore
We propose generalised DWH specification tests which simultaneously compare three or more likelihood-based estimators of conditional mean and variance parameters in multivariate conditionally heteroskedastic dynamic regression models. Our tests are useful for Garch models and in many empirically relevant macro and finance applications involving Vars and multivariate regressions. To design powerful and reliable tests, we determine the rank deficiencies of the differences between the estimators' asymptotic covariance matrices under the null of correct specification, and take into account that some parameters remain consistently estimated under the alternative of distributional misspecification. Finally, we provide finite sample results through Monte Carlo simulations.
Gabriele Fiorentini
Enrique Sentana
Durbin-Wu-Hausman Tests, Partial Adaptivity, Semiparametric Estimators, Singular Covariance Matrices
2018-05
The Accuracy of Linear and Nonlinear Estimation in the Presence of the Zero Lower Bound
http://d.repec.org/n?u=RePEc:fip:feddwp:1804&r=ore
This paper evaluates the accuracy of linear and nonlinear estimation methods for dynamic stochastic general equilibrium models. We generate a large sample of artificial datasets using a global solution to a nonlinear New Keynesian model with an occasionally binding zero lower bound (ZLB) constraint on the nominal interest rate. For each dataset, we estimate the nonlinear model—solved globally, accounting for the ZLB—and the linear analogue of the nonlinear model—solved locally, ignoring the ZLB—with a Metropolis-Hastings algorithm where the likelihood function is evaluated with a Kalman filter, unscented Kalman filter, or particle filter. In datasets that resemble the U.S. experience, the nonlinear model estimated with a particle filter is more accurate and has a higher marginal data density than the linear model estimated with a Kalman filter, as long as the measurement error variances in the particle filter are not too big.
Atkinson, Tyler
Richter, Alexander
Throckmorton, Nathaniel
Bayesian estimation; nonlinear solution; particle filter; unscented Kalman filter
2018-05-07
Group Size, Collective Action and Complementarities in Efforts
http://d.repec.org/n?u=RePEc:tse:wpaper:32635&r=ore
We revisit the group size paradox in a model where two groups of different sizes compete for a prize exhibiting a varying degree of rivalry and where group effort is given by a CES function of individual e¤orts. We show that the larger group can be more successful than the smaller group if the degree of complementarity is sufficiently high relative to the degree of rivalry of the prize.
Cheikbossian, Guillaume
Fayat, Romain
group size paradox; group contest; complementarity; (impure) public good
2018-05
A Posterior-Based Wald-Type Statistic for Hypothesis Testing
http://d.repec.org/n?u=RePEc:ris:smuesw:2018_008&r=ore
A new Wald-type statistic is proposed for hypothesis testing based on Bayesian posterior distributions. The new statistic can be explained as a posterior version of Wald test and have several nice properties. First, it is well-defi ned under improper prior distributions. Second, it avoids Jeffreys-Lindley's paradox. Third, under the null hypothesis and repeated sampling, it follows a x2 distribution asymptotically, offering an asymptotically pivotal test. Fourth, it only requires inverting the posterior covariance for the parameters of interest. Fifth and perhaps most importantly, when a random sample from the posterior distribution (such as an MCMC output) is available, the proposed statistic can be easily obtained as a by-product of posterior simulation. In addition, the numerical standard error of the estimated proposed statistic can be computed based on the random sample. The finite sample performance of the statistic is examined in Monte Carlo studies. The method is applied to two latent variable models used in microeconometrics and financial econometrics.
Li, Yong
Liu, Xiaobin
Zeng, Tao
Yu, Jun
Decision theory; Hypothesis testing; Latent variable models; Posterior simulation; Wald test.
2018-05-11
Discussion of “asymptotic theory of outlier detection algorithms for linear time series regression models” by Johansen and Nielsen
http://d.repec.org/n?u=RePEc:ehl:lserod:66724&r=ore
Atkinson, Anthony C.
Cerioli, Andrea
Riani, Marco
Fan plot; forward search; Mahalanobis distance; monitoring; robustness
2016-06-01
Development of a load sharing policy by managing the residual life based on a stochastic process
http://d.repec.org/n?u=RePEc:tsa:wpaper:0177mss&r=ore
In this paper, we analyze the time (viz., the number of cycles) to reach any given crack size in a fatigue life test using a gamma stochastic process. It is assumed that the time increments are nonstationary but independent for each specimen while the shape parameter of the gamma distribution is a function of the crack length. In addition, using a random effect model, the between-specimen variability is explained by modeling the scale parameter of the process with a gamma distribution. This yields explicit formulas for the marginal lifetime distributions, the associated mean and variance which boosts computational efficiency.
David Han
fatigue crack growth, gamma distribution, lifetime estimation, Paris law, reliability, stochastic process
2016-11-06
A Time-Space Dynamic Panel Data Model with Spatial Moving Average Errors
http://d.repec.org/n?u=RePEc:pra:mprapa:86371&r=ore
This paper focuses on the estimation and predictive performance of several estimators for the time-space dynamic panel data model with Spatial Moving Average Random Effects (SMA-RE) structure of the disturbances. A dynamic spatial Generalized Moments (GM) estimator is proposed which combines the approaches proposed by Baltagi, Fingleton and Pirotte (2014) and Fingleton (2008). The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a forecasting approach is proposed and a linear predictor is derived. Using Monte Carlo simulations, we compare the short-run and long-run effects and evaluate the predictive efficiencies of optimal and various suboptimal predictors using the Root Mean Square Error (RMSE) criterion. Last, our approach is illustrated by an application in geographical economics which studies the employment levels across 255 NUTS regions of the EU over the period 2001-2012, with the last two years reserved for prediction.
Baltagi, Badi H.
Fingleton, Bernard
Pirotte, Alain
Panel data; Spatial lag; Error components; Time-space; Dynamic;OLS; Within; GM; Spatial autocorrelation; Direct and indirect effects; Moving average; Prediction; Simulations, Rook contiguity, Interregional trade.
2018-04-18
The Hidden Predictive Power of Cryptocurrencies: Evidence from US Stock Market
http://d.repec.org/n?u=RePEc:cui:wpaper:0056&r=ore
This paper is motivated by the news that the surge in cryptocurrencies is an important candidate to in explaining the plummeting stock markets. To validate this believe, we construct a predictive model in which cryptocurrencies are identified as the predictors of US stock returns. The inherent statistical properties of cryptocurrencies such as persistence, endogeneity, and conditional heteroscedasticity are being accounted for in the Westerlund and Narayan (2015) estimator. Three salient results emanated from our estimations. First, we validated the importance of cryptocurrencies in predicting US stock prices; second, the cryptocurrencies predictive model outperforms the conventional time-series models such as Autoregressive Integrated Moving Average (ARIMA) model and the Autoregressive Fractionally Integrated Moving Average (ARFIMA); third, our results are robust to different method of forecast performance evaluation measures and different sub-sample periods. These results have important policy implications for the investors and policymakers.
Kazeem Isah
Ibrahim D. Raheem
Stock Prices, Cryptocurrency, Digital Asset Prices, Predictive Model, Forecast Evaluation
2018-05
ANALYSING Inflation in Nigeria: A Fractionally Integrated ARFIMA-GARCH Modelling Approach
http://d.repec.org/n?u=RePEc:pra:mprapa:85655&r=ore
The study looked into the stochastic properties of CPI-inflation rate for Nigeria from 1995Q1 to 2016Q4. The study employed an autoregressive fractionally integrated moving average and a general autoregressive conditional heteroskedasticity (ARFIMA-GARCH) methodology as well as ADF/KPSS to investigate the long-memory properties of CPI-Inflation for Nigeria. The study found that CPI-inflation in Nigeria is shock dissipating at a geometric rate (fast mean reverting ability). The ARFIMA-GARCH process showed that CPI inflation in Nigeria is a heteroskedastic fractionally integrated process with quick mean reverting ability. The study therefore concludes that shocks to CPI-inflation in Nigeria such as sudden hikes in prices of energy products will not cause a permanent change in general price level but will eventually return to its mean state, and therefore having an implication for the Inflation-Unemployment tradeoff of the Philips curve.
Iorember, Paul
Usar, Terzungwe
Ibrahim, Kabiru
Inflation, AFIMA, GARCH, Fractional Integrated and Long Memory, ADF and KPSS
2018-01-07