高炮随动系统自动测试中正弦机发送频率的确定

在某型高炮随动系统测试中,正弦机的发送频率直接影响着输出信号的平滑度,从而会影响测试的精度。为了避免在正弦机运行过程中,计算耗用过多机时,在正弦机发送之前,先计算出每个位置信号、前馈信号的数值,存放于内存中。本文根据该正弦机信号的特点,分析了几种正弦机角位置信号发送频率方式的优缺点,确定了正弦机发送频率的方式。     

基于系统动力学的短生命周期产品供应链利润分析

文中首先回顾了国内外对短生命周期产品的研究,分析了系统动力学的特点,然后基于系统动力学理论建立了一个二阶短生命周期产品的供应链模型,该供应链模型中包含了一个制造商和一个零售商,最后利用系统动力学软件Vensim_PLE对模型进行模拟仿真,得出模拟结果,并在此基础上对模型进行分析,阐述了短生命周期产品供应链上的产品价格变化、制造商生产能力的投资决策对短生命周期产品供应链在产品的整个生命周期内的总利润的影响。     

深化中国—东盟区域金融合作的可行性条件:经济周期同步性研究

中国—东盟自由贸易区的全面启动,使得该区域内金融合作落后于贸易合作的问题越发凸显。根据蒙代尔的最优货币区理论,经济周期是否同步决定区域金融合作是否可行。文章通过建立多变量向量误差修正模型,依据共同趋势和共同周期理论,对中国与东盟经济周期的同步性进行了经验分析。结果表明,中国与东盟经济周期不仅在长期内具有共同的随机发展趋势,短期内也显示出明显的同步性,因而满足加深区域金融合作的可行条件。     

基于Multisim10的RC正弦波振荡电路仿真设计

本文通过EDA软件Multisim10仿真模拟电子技术中的难点——RC正弦波振荡电路。文章结合Multisim10软件分析了非线性元件二极管、JFET作为稳幅元件建立振荡的过程,还分析了移相式正弦波振荡电路的工作原理。引入仿真过程,激发了学生学习的兴趣,强化了学生对理论知识的理解。     

Monetary policy transmission in a model with animal spirits and house price booms and busts

Can monetary policy trigger pronounced boom-bust cycles in house prices and create persistent business cycles? We address this question by building heuristics into an otherwise standard DSGE model. As a result, monetary policy sets off waves of optimism and pessimism (“animal spirits”) that drive house prices, that, in turn, have strong repercussions on the business cycle. We compare our findings to a standard model with rational expectations by means of impulse responses. We suggest that a standard Taylor rule is not well-suited to maintain macroeconomic stability. Instead, an augmented rule that incorporates house prices is shown to be superior.     

DDS技术正弦信号发生器的实现

该系统基于DDS技术的正弦信号发生器,其主要模块有DDS部分正弦波生成、频率控制、幅度控制、D/A转换和后级处理以实现AM、FM、ASK、PSK、FSK等功能)。各模块均通过Verilog语言编程在FPGA上实现然后在后级处理采用低通滤波器和功率放大电路来提高波形质量和负载能力。最终得到所要求的正弦信号发生器,在现代通信中具有良好的使用性。     

Markov-switching and the Beveridge–Nelson decomposition: Has US output persistence changed since 1984?

We show that, for a class of univariate and multivariate Markov-switching models, exact calculation of the Beveridge–Nelson (BN) trend/cycle components is possible. The key to exact BN trend/cycle decomposition is to recognize that the latent first-order Markov-switching process in the model has an AR(1) representation, and that the model can be cast into a state-space form. Given the state-space representation, we show that impulse-response function analysis can be processed with respect to either an asymmetric discrete shock or to a symmetric continuous shock. The method presented is applied to Kim, Morley, Piger’s [Kim, C.-J., Morley, J., Piger, J., 2005. Nonlinearity and the permanent effects of recessions. Journal of Applied Econometrics 20, 291–309] univariate Markov-switching model of real GDP with a post-recession ‘bounce-back’ effect and Cochrane’s [Cochrane, J.H., 1994. Permanent and transitory components of GNP and stock prices. Quarterly Journal of Economics 109, 241–263] vector error correction model of real GDP and real consumption extended to incorporate Markov-switching. The parameter estimates, the BN trend/cycle components, and the impulse-response function analysis for each of these empirical models suggest that the persistence of US real GDP has increased since the mid-1980’s.     

Exponentially smoothed regression analysis for demand forecasting

This article proposes a new technique for estimating trend and multiplicative seasonality in time series data. The technique is computationally quite straightforward and gives better forecasts (in a sense described below) than other commonly used methods. Like many other methods, the one presented here is basically a decomposition technique, that is, it attempts to isolate and estimate the several subcomponents in the time series. It draws primarily on regression analysis for its power and has some of the computational advantages of exponential smoothing. In particular, old estimates of base, trend, and seasonality may be smoothed with new data as they occur. The basic technique was developed originally as a way to generate initial parameter values for a Winters exponential smoothing model [4], but it proved to be a useful forecasting method in itself.The objective in all decomposition methods is to separate somehow the effects of trend and seasonality in the data, so that the two may be estimated independently. When seasonality is modeled with an additive form (Datum = Base + Trend + Seasonal Factor), techniques such as regression analysis with dummy variables or ratio-to-moving-average techniques accomplish this task well. It is more common, however, to model seasonality as a multiplicative form (as in the Winters model, for example, where Datum = [Base + Trend] * Seasonal Factor). In this case, it can be shown that neither of the techniques above achieves a proper separation of the trend and seasonal effects, and in some instances may give highly misleading results. The technique described in this article attempts to deal properly with multiplicative seasonality, while remaining computationally tractable.The technique is built on a set of simple regression models, one for each period in the seasonal cycle. These models are used to estimate individual seasonal effects and then pooled to estimate the base and trend. As new data occur, they are smoothed into the least-squares formulas with computations that are quite similar to those used in ordinary exponential smoothing. Thus, the full least-squares computations are done only once, when the forecasting process is first initiated. Although the technique is demonstrated here under the assumption that trend is linear, the trend may, in fact, assume any form for which the curve-fitting tools are available (exponential, polynomial, etc.).The method has proved to be easy to program and execute, and computational experience has been quite favorable. It is faster than the RTMA method or regression with dummy variables (which requires a multiple regression routine), and it is competitive with, although a bit slower than, ordinary triple exponential smoothing.     

Revisiting the matching function

There is strong empirical evidence for Cobb–Douglas matching functions. We show in this paper that this widely found relation between matches on the one hand and unemployment and vacancies on the other hand can be the result of different underlying mechanisms. Obviously, it can be generated by assuming a Cobb–Douglas matching function. Less obvious, the same relationship results from a vacancy free-entry condition and idiosyncratic productivity shocks. A positive aggregate productivity shock leads to more vacancy posting, a shift of the idiosyncratic selection cutoff and thereby more hiring. We calibrate a model with both mechanisms to administrative German labor market data and show that idiosyncratic productivity for new contacts is an important driver of the elasticity of the job-finding rate with respect to the market tightness. Accounting for idiosyncratic productivity can explain the observed negative time trend in estimated matching efficiency and asymmetric business cycle responses to large aggregate shocks.     

PDF versus LP: An empirical aggregate planning comparison

Production Decision Framework (PDF) is a recently introduced heuristic method for solving the aggregate planning problem. The algorithm was developed and tested over a wide range of deterministic demand patterns and cost parameters. The decision by Wayn-Tex Inc., to adopt the methodology provided a real world opportunity to evaluate the performance of PDF under uncertainty over a one year's operating period.This paper concentrated on a study of (1) the effectiveness of the model in selecting a suitable rolling planning horizon, and (2) the performance of PDF as compared to a linear program solution using the same length horizon. The monthly sales forecast used in the study, developed by the sales department, took into consideration trend, seasonality, and known market conditions. The results were gratifying. Management has indicated their desire to continue using PDF as a guide in the production planning process through the use of the computerized program available.     

供应商管理库存对顾客服务水平影响的研究

在知识经济时代,传统的库存管理思想已经不能满足市场发展的需要,强调供应链整体合作是库存理论的进一步发展趋势。而供应商管理库存是未来供应链库存管理的一种重要模式,受到越来越多理论界的重视和研究。而库存管理思想对顾客服务水平有什么影响,却很少有人对此做出模型研究。本文基于提前期对此展开讨论,提出供应商管理库存能够提高补给周期供给水平(CSL)和产品供给率(Fr),降低供应链中总成本,保证整条链快速响应顾客需求,提高顾客服务水平。     

2009年中国经济增长率周期波动呈U型走势——利用景气指数和Probit模型的分析和预测

本文基于景气指数、"宏观经济监测预警信号系统"及Probit模型方法,对当前的经济景气状况和走势进行了分析和预测.结果表明:先行合成指数和先行扩散指数同时在2008年10月出现谷底,预示着本轮经济周期波动的谷将出现在2009年的4月或5月.在全球金融危机的背景下,中国经济仍会保持"一枝独秀",2009年下半年我国将处于经济周期波动的上升阶段.但是我国外需仍然不振,保增长将主要靠内需,经济增长周期波动的回升将呈现U型走势.     

Trend/cycle decomposition of regime-switching processes

We present a new approach to trend/cycle decomposition of time series that follow regime-switching processes. The proposed approach, which we label the “regime-dependent steady-state” (RDSS) decomposition, is motivated as the appropriate generalization of the Beveridge and Nelson decomposition [Beveridge, S., Nelson, C.R., 1981. A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the business cycle. Journal of Monetary Economics 7, 151–174] to the setting where the reduced-form dynamics of a given series can be captured by a regime-switching forecasting model. For processes in which the underlying trend component follows a random walk with possibly regime-switching drift, the RDSS decomposition is optimal in a minimum mean-squared-error sense and is more broadly applicable than directly employing an Unobserved Components model.     

燃气蒸汽联合循环的技术探讨

发电厂是我国电能生产的主要场所,其持续发电作业对于维持社会的用电有决定性作用。由于市场经济的快速发展,实现工业化大生产是未来国家经济发展的必然趋势,这对发电厂的供电能力提出了严峻的挑战。从供电性能、节能降耗两个角度考虑,热电厂必须采用燃气蒸汽联合循环技术。文章在分析燃气蒸汽联合循环技术发展前景的基础上,从环境保护角度介绍了燃煤锅炉几种主要的烟气脱硫除尘技术。     

Estimating the U.S. output gap with state‐level data

This paper develops a method to estimate the U.S. output gap by exploiting the cross‐sectional variation of state‐level output and unemployment rate data. The model assumes that there are common output and unemployment rate trend and cycle components, and that each state's output and unemployment rate are subject to idiosyncratic trend and cycle perturbations. I estimate the model with Bayesian methods using quarterly data from 2005:Q1 to 2018:Q2 for the 50 states and the District of Columbia. Results show that the U.S. output gap reached about negative 4.6% around the years of the Great Recession and was about 0.9% in 2018:Q2.     

Output and unemployment dynamics in the United States: 1950–1985

For US data over 1950–1985 the stochastic components of GNP growth and the unemployment rate appear to be stationary, and there is substantial feedback between these variables. The unconditional mean rate of unemployment in a joint model thus provides a natural benchmark in discussions of the ‘business cycle’. A bivariate VAR model is used to describe output–unemployment dynamics, to estimate the degree of persistence of output innovations, and to decompose output into trend and cycle. The bivariate results are interpreted using a restricted VAR and it is shown that a closely related cyclical measure can be obtained directly from the Okun's Law equation.     

Stochastic trends,deterministic trends,and business cycle turning points

This study examines the relationship between specifications for long-run output patterns and specifications for business cycle dynamics. In an application to US GDP, it is found that inferences about the nature of the trend in output are not robust to changes in the specification for short-run fluctuations. Similarly, the choice of which model best describes the transitory movements in output depends on the way in which the trend is specified. The empirical analysis makes use of Bayesian methods to compare time series models for US GDP. Inspection of the predictive densities for the individual data points suggests that the information contained in the data is largely limited to the observations associated with business cycle turning points. © 1997 John Wiley & Sons, Ltd.     

Estimating Trends with Percentage of Smoothness Chosen by the User

This work presents a method for estimating trends of economic time series that allows the user to fix at the outset the desired percentage of smoothness for the trend. The calculations are based on the Hodrick-Prescott (HP) filter usually employed in business cycle analysis. The situation considered here is not related to that kind of analysis, but with describing the dynamic behaviour of the series by way of a smooth curve. To apply the filter, the user has to specify a smoothing constant that determines the dynamic behaviour of the trend. A new method that formalizes the concept of trend smoothness is proposed here to choose that constant. Smoothness of the trend is measured in percentage terms with the aid of an index related to the underlying statistical model of the HP filter. Empirical illustrations are provided using data on Mexico's GDP.     

Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment*

We introduce a general class of periodic unobserved component (UC) time series models with stochastic trend and seasonal components and with a novel periodic stochastic cycle component. The general state space formulation of the periodic model allows for exact maximum likelihood estimation, signal extraction and forecasting. The consequences for model‐based seasonal adjustment are discussed. The new periodic model is applied to postwar monthly US unemployment series from which we identify a significant periodic stochastic cycle. A detailed periodic analysis is presented including a comparison between the performances of periodic and non‐periodic UC models.     

Evaluating alternative models of trend inflation

With the concept of trend inflation now being widely understood to be important to the accuracy of longer-term inflation forecasts, this paper assesses alternative models of trend inflation. Reflecting the models which are common in reduced-form inflation modeling and forecasting, we specify a range of models of inflation that incorporate different trend specifications. We compare the models on the basis of their accuracies in out-of-sample forecasting, both point and density. Our results show that it is difficult to say that any one model of trend inflation is the best. Several different trend specifications seem to be about equally accurate, and the relative accuracy is somewhat prone to instabilities over time.