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Robust Inference for Inverse Stochastic Dominance
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| Pengarang | : | Andreoli, Francesco |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 36 (No. 1) |
| Halaman | : | 146-159 |
| Abstrak | : | The notion of inverse stochastic dominance is gaining increasing support in risk, inequality, and welfare analysis as a relevant criterion for ranking distributions, which is alternative to the standard stochastic dominance approach. Its implementation rests on comparisons of two distributions' quantile functions, or of their multiple partial integrals, at fixed population proportions. This article develops a novel statistical inference model for inverse stochastic dominance that is based on the influence function approach. The proposed method allows model-free evaluations that are limitedly affected by contamination in the data. Asymptotic normality of the estimators allows to derive tests for the restrictions implied by various forms of inverse stochastic dominance. Monte Carlo experiments and an application promote the qualities of the influence function estimator when compared with alternative dominance criteria. |
PERANAN JEPANG DALAM DUNIA PERMINYAKAN INDONESIA
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | III-8, AGUSTUS (No. 8) |
| Halaman | : | 63-77 |
| Abstrak | : | - |
INDONESIADAN PETABUMI POLITIK ENERGI DUNIA SELAMA 30 TAHUN MENDATANG
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | III-8, AGUSTUS (No. 8) |
| Halaman | : | 13-62 |
| Abstrak | : | - |
Combined Density Nowcasting in an Uncertain Economic Environment
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 36 (No. 1) |
| Halaman | : | 131-145 |
| Abstrak | : | We introduce a combined density nowcasting (CDN) approach to dynamic factor models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features to provide more accurate and complete density nowcasts. The combination weights are latent random variables that depend on past nowcasting performance and other learning mechanisms. The combined density scheme is incorporated in a Bayesian sequential Monte Carlo method which rebalances the set of nowcasted densities in each period using updated information on the time-varying weights. Experiments with simulated data show that CDN works particularly well in a situation of early data releases with relatively large data uncertainty and model incompleteness. Empirical results, based on U.S. real-time data of 120 monthly variables, indicate that CDN gives more accurate density nowcasts of U.S. GDP growth than a model selection strategy and other combination strategies throughout the quarter with relatively large gains for the two first months of the quarter. CDN also provides informative signals on model incompleteness during recent recessions. Focusing on the tails, CDN delivers probabilities of negative growth, that provide good signals for calling recessions and ending economic slumps in real time. |
SEGI INTERNASIONAL-STRATEGIS DARI PERSOALAN ENERGI
| Pengarang | : | Moertopo Ali |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | III-8, AGUSTUS (No. 8) |
| Halaman | : | 3-12 |
| Abstrak | : | - |
Confidence Band for ROC Curves With Serially Dependent Data
| Pengarang | : | Lahiri, Kajal,Yang Liu |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 36 (No. 1) |
| Halaman | : | 116-130 |
| Abstrak | : | Abstrak tidak tersedia. |
A Bayesian Markov Switching Correlation Model for Contangion Analysis on Exchange Rate Markets
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 36 (No. 1) |
| Halaman | : | 101-113 |
| Abstrak | : | This article develops a new Markov-switching vector autoregressive (VAR) model with stochastic correlation for contagion analysis on financial markets. The correlation and the log-volatility dynamics are driven by two independent Markov chains, thus allowing for different effects such as volatility spill-overs and correlation shifts with various degrees of intensity. We outline a suitable Bayesian inference procedure based on Markov chain Monte Carlo algorithms. We then apply the model to some major and Asian Pacific cross rates against the U.S. dollar and find strong evidence supporting the existence of contagion effects and correlation drops during crises, closely in line with the stylized facts outlined in the contagion literature. A comparison of this model with its closest competitors, such as a time-varying parameter VAR, reveals that our model has a better predictive ability. Supplementary materials for this article are available online |
Nonparametric Estimation and Forecasting for Time-Varying Coefficient Realized Volatility Models
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 36 (No. 1) |
| Halaman | : | 88-100 |
| Abstrak | : | This article introduces a new specification for the heterogenous autoregressive (HAR) model for the realized volatility of S&P 500 index returns. In this modeling framework, the coefficients of the HAR are allowed to be time-varying with unspecified functional forms. The local linear method with the cross- validation (CV) bandwidth selection is applied to estimate the time-varying coefficient HAR (TVC-HAR) model, and a bootstrap method is used to construct the point-wise confidence bands for the coefficient functions. Furthermore, the asymptotic distribution of the proposed local linear estimators of the TVC HAR model is established under some mild conditions. The results of the simulation study show that the local linear estimator with CV bandwidth selection has favorable finite sample properties. The outcomes of the conditional predictive ability test indicate that the proposed nonparametric TVC-HAR model outperforms the parametric HAR and its extension to HAR with jumps and/or GARCH in terms of multi step out-of- sample forecasting, in particular in the post-2003 crisis and 2007 global financial crisis (GFC) periods, during which financial market volatilities were unduly high. This article introduces a new specification for the heterogenous autoregressive (HAR) model for the realized volatility of S&P 500 index returns. In this modeling framework, the coefficients of the HAR are allowed to be time-varying with unspecified functional forms. The local linear method with the cross validation (CV) bandwidth selection is applied to estimate the time-varying coefficient HAR (TVC-HAR) model, and a bootstrap method is used to construct the point-wise confidence bands for the coefficient functions. Furthermore, the asymptotic distribution of the proposed local linear estimators of the TVC-HAR model is established under some mild conditions. The results of the simulation study show that the local linear estimator with CV bandwidth selection has favorable finite sample properties. The outcomes of the conditional predictive ability test indicate that the proposed nonparametric TVC-HAR model outperforms the parametric HAR and its extension to HAR with jumps and/or GARCH in terms of multi-step out-of- sample forecasting, in particular in the post-2003 crisis and 2007 global financial crisis (GFC) periods, during which financial market volatilities were unduly high. |
Stochastic Volatility Models Based on OU-Gamma Time Change: Theory and Estimation
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 36 (No. 1) |
| Halaman | : | 75-87 |
| Abstrak | : | We consider stochastic volatility models that are defined by an Ornstein-Uhlenbeck (OU)-Gamma time change. These models are most suitable for modeling financial time series and follow the general framework of the popular non-Gaussian OU models of Barndorff-Nielsen and Shephard. One current problem of these otherwise attractive nontrivial models is, in general, the unavailability of a tractable likelihood-based statistical analysis for the returns of financial assets, which requires the ability to sample from a nontrivial joint distribution. We show that an OU process driven by an infinite activity Gamma process, which is an OU-Gamma process, exhibits unique features, which allows one to explicitly describe and exactly sample from relevant joint distributions. This is a consequence of the OU structure and the calculus of Gamma and Dirichlet processes. We develop a particle marginal Metropolis-Hastings algorithm for this type of continuous-time stochastic volatility models and check its performance using simulated data. For illustration we finally fit the model to S&P500 index data. |
Max-Linear Competing Factor Models
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 36 (No. 1) |
| Halaman | : | 62-74 |
| Abstrak | : | Models incorporating "latent" variables have been commonplace in financial, social, and behavioral sciences. Factor model, the most popular latent model, explains the continuous observed variables in a smaller set of latent variables (factors) in a matter of linear relationship. However, complex data often simultaneously display asymmetric dependence, asymptotic dependence, and positive (negative) dependence between random variables, which linearity and Gaussian distributions and many other extant distributions are not capable of modeling. This article proposes a nonlinear factor model that can model the above-mentioned variable dependence features but still possesses a simple form of factor structure. The random variables, marginally distributed as unit Fréchet distributions, are decomposed into max linear functions of underlying Fréchet idiosyncratic risks, transformed from Gaussian copula, and independent shared external Frechet risks. By allowing the random variables to share underlying (latent) pervasive risks with random impact parameters. various dependence structures are created. This innovates a new promising technique to generate families of distributions with simple interpretations. We dive in the multivariate extreme value properties of the proposed model and investigate maximum composite like-lihood methods for the impact parameters of the latent risks. The estimates are shown to be consistent. The estimation schemes are illustrated on several sets of simulated data, where comparisons of performance are addressed. We employ a bootstrap method to obtain standard errors in real data analysis. Real application to financial data reveals inherent dependencies that previous work has not disclosed and demonstrates the model's interpretability to real data. Supplementary materials for this article are available online. |