广义空间调制系统的正则化OMP检测算法

在广义空间调制（GSM）系统中,最大似然（ML）检测可以取得最优的检测性能,然而其计算复杂度随激活天线数的增加急剧增长。针对这一问题,提出了一种基于稀疏重构理论的低复杂度检测算法——正则化正交匹配追踪（ROMP）算法。该算法首先根据信道矩阵和当前残差的内积选取多个候选激活天线索引,接着对候选天线索引按正则化标准进行可靠性验证,剔除错误索引,缩小信号的搜索空间,最后通过求解最小二乘问题估计信号。仿真结果表明,与经典的正交匹配追踪（OMP）算法相比,所提算法以少许复杂度的增加为代价极大提升了检测性能,能够在检测性能与复杂度之间取得更好的折中。     

The effects of China's Sloping Land Conversion Program on agricultural households

In the late 1990s, China aimed to mitigate environmental degradation from agricultural production activities by introducing the world's largest “Payments for Environmental Services? program: the Sloping Land Conversion Program (SLCP). We develop a microeconomic Agricultural Household Model, which can model the production, consumption, and nonfarm labor supply decisions of agricultural households in rural China in a theoretically consistent fashion. Based on this theoretical model, we derive an empirical specification, which we econometrically estimate using the Hausman–Taylor method and a large longitudinal farm household data set. The empirical results significantly differ between regions, but are generally consistent with the results of our theoretical comparative static analysis, for example, that the SLCP significantly decreases agricultural production. While the SLCP only increases nonfarm labor supply and total consumption in some regions, these effects could not be observed in others. The recent reduction of the SLCP compensation payment rates generally had negligible effects on agricultural production and off‐farm work and only very small effects on household consumption.     

Gains from horizontal collaboration among ski areas

Ski areas are known to expand by linking their lifts to neighbouring systems. Based on data from approximately 250 winter sport destinations in Austria, pooled over the years 1998–2014, this study explores the effects of such horizontal collaboration on the number of overnights stays in the area. A difference-in-differences (DID) approach combined with propensity score matching shows that new lift-linkages or expansions lead to a consolidation in the number of overnight stays at a level 12 per cent higher than before the introduction of the lift-link. However, there is a certain degree of heterogeneity in the causal effects. Satellite ski areas, remote villages and those who combine lift-linking with new connecting slopes benefit the most. More recent lift-linkages seem to lead to smaller gains than those established in the early years.     

A new energy model to capture the behavior of energy price processes

Owing to the vague fluctuation of energy prices from time to time, a new energy model, which considers both the mean-reverting behavior and the long memory property, is proposed in this paper. Since the problem of estimating parameters, in discrete time for this model, plays a central role in forecast inference, the problem of estimating the unknown parameters has been dealt with for the fractional Ornstein–Uhlenbeck process observed discretely. The asymptotic properties of these estimates are also provided. The numerical simulation results confirm the theoretical analysis and show that our method is effective. To show how to apply our approach in realistic contexts, an empirical study of energy in China, namely Daqing crude oil, is presented. The empirical results seem reasonable when compared to the real data.     

Leaving EMU: a real options perspective

The real option implicit in a country's decision of whether to leave an existing monetary union when there is uncertainty over the future benefits of this move is examined. The theoretical model used is calibrated for the current Euro-12 area by proxying policymakers’ inflation preferences with unemployment rates and debt-to-GDP ratios. A robust group of countries is observed that would choose to remain within EMU consisting of Belgium, Finland, Greece and Italy; France and Spain loosely also belong to this core. Only Luxembourg would robustly want to leave EMU; Ireland and The Netherlands, however, complement that core closely.     

Calibration Weighting Methods for Complex Surveys

This paper presents a careful investigation of the three popular calibration weighting methods: (i) generalised regression; (ii) generalised exponential tilting and (iii) generalised pseudo empirical likelihood, with a major focus on computational aspects of the methods and some empirical evidences on calibrated weights. We also propose a simple weight trimming method for range‐restricted calibration. The finite sample behaviour of the weights obtained by the three calibration weighting methods and the effectiveness of the proposed weight trimming method are examined through limited simulation studies.     

Small-sample Properties of Panel Spatial Autoregressive Models: Comparison of the Bayesian and Maximum Likelihood Methods

Abstract

This article considers autoregressive (SAR) models. We method to estimate the parameters of likelihood (ML) method. Our Bayesian by the Monte Carlo studies. We found the efficient as the ML estimators.     

Proportional Odds Models with High‐Dimensional Data Structure

The proportional odds model is the most widely used model when the response has ordered categories. In the case of high‐dimensional predictor structure, the common maximum likelihood approach typically fails when all predictors are included. A boosting technique pomBoost is proposed to fit the model by implicitly selecting the influential predictors. The approach distinguishes between metric and categorical predictors. In the case of categorical predictors, where each predictor relates to a set of parameters, the objective is to select simultaneously all the associated parameters. In addition, the approach distinguishes between nominal and ordinal predictors. In the case of ordinal predictors, the proposed technique uses the ordering of the ordinal predictors by penalizing the difference between the parameters of adjacent categories. The technique has also a provision to consider some mandatory predictors (if any) that must be part of the final sparse model. The performance of the proposed boosting algorithm is evaluated in a simulation study and applications with respect to mean squared error and prediction error. Hit rates and false alarm rates are used to judge the performance of pomBoost for selection of the relevant predictors.     

Full versus quasi MLE for ARMA-GARCH models with infinitely divisible innovations

We compare the backtesting performance of ARMA-GARCH models with the most common types of infinitely divisible innovations, fit with both full maximum likelihood estimation (MLE) and quasi maximum likelihood estimation (QMLE). The innovation types considered are the Gaussian, Student’s t, α-stable, classical tempered stable (CTS), normal tempered stable (NTS) and generalized hyperbolic (GH) distributions. In calm periods of decreasing volatility, MLE and QMLE produce near identical performance in forecasting value-at-risk (VaR) and conditional value-at-risk (CVaR). In more volatile periods, QMLE can actually produce superior performance for CTS, NTS and α-stable innovations. While the t-ARMA-GARCH model has the fewest number of VaR violations, rejections by the Kupeic and Berkowitz tests suggest excessively large forecasted losses. The α-stable, CTS and NTS innovations compare favourably, with the latter two also allowing for option pricing under a single market model.     

Generalized beta distributions for describing and analysing intraday stock market data: testing the U-shape pattern

There is an impressive body of empirical evidence which indicates the existence of an intraday U-shaped curve in stock prices. In an effort to shed additional light on the U-shaped curve a new procedure for U-shape testing is introduced. From careful analysis of intraday data it is observed that minimum or maximum stock prices can occur several times during the day. Here, attention is focused on the first time during the day that the maximum or minimum stock price occurred. Because of the importance of the first time during the day that the maximum or minimum stock price occurred, an attempt is made to model these two characteristics with probability distributions. The objective of this study is to use a generalized beta distribution to examine the intradaily behaviour of stocks, using closing stock prices for each one-minute interval, using data from Athens Stock Exchange (ASE). This generalized beta distribution has not been used before to model U-shaped behaviour. The results are consistent with the intraday U-shaped curves, i.e. the time to first maximum (or minimum) stock prices follows a U-shaped pattern. In addition, some potential applications of the generalized beta distribution are discussed and exemplified by analysing the relationship between herd behaviour and U-shaped.