Vector AR Implementation for Rain Rate Space Time Series Modeling in Surabaya

Site diversity is one of the Fading Mitigation Techniques (FMT) that is a base system design on the nature of rain rate that change to the time and space. However, to get appropriate site diversity needs deep knowledges about rain rate dynamic and statistical characteristic. In this research, rain rate space-time series modeling in 4 rain gauges location studied by using Vector AR (VAR) model. To validate VAR model, it used 3 methods; ecdf graphic comparison, qqplot method and model residual analysis. The result showed that VAR model is correct and appropriate model for rain rate space time series modeling in 4 rain gauges location. These VAR models have good accuracy with Spatial RMSE Mean between 0.273 - 0.763

Forecasting Based On Open Var Model

Considering as a starting point certain advantages and limits of the VAR model, we propose an opening to include some approaches suggested particularly by economic theory, such as economic policy role and that concerning corrections applied to restore an equilibrium state or a forecast error. In order to improve the forecasting quality we introduced in the VAR model certain variables that express previous approaches. The open VAR model was applied to short-time prognoses regarding the main prices in economy (consumer price index, exchange rate, monthly wage, interest rate).interdependence, autoregressive, simultaneous equations model, structural form, reduce form, lagged variables, error correction, test, ex-post forecast, system, intercept parameter, qualitative variable

Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks

This paper builds a model which has two extensions over a standard VAR. The …rst of these is stochastic search variable selection, which is an automatic model selection device which allows for coefficients in a possibly over-parameterized VAR to be set to zero. The second allows for an unknown number of structual breaks in the VAR parameters. We investigate the in-sample and forecasting performance of our model in an application involving a commonly-used US macro-economic data set. We …nd that, in-sample, these extensions clearly are warranted. In a recursive forecasting exercise, we …nd moderate improvements over a standard VAR, although most of these improvements are due to the use of stochastic search variable selection rather than the inclusion of breaks

Forecasting the South African Economy: A DSGE-VAR Approach

This paper develops an estimable hybrid model that combines the micro-founded DSGE model with the flexibility of the theoretical VAR model. The model is estimated via the maximum likelihood technique based on quarterly data on real Gross National Product (GNP), consumption, investment and hours worked, for the South African economy, over the period of 1970:1 to 2000:4. Based on a recursive estimation using the Kalman filter algorithm, the out-of-sample forecasts from the hybrid model are then compared with the forecasts generated from the Classical and Bayesian variants of the VAR for the period 2001:1-2005:4. The results indicate that, in general, the estimated hybrid DSGE model outperforms the Classical VAR, but not the Bayesian VARs in terms of out-of-sample forecasting performances.DSGE Model; VAR and BVAR Model; Forecast Accuracy; DSGE Forecasts; VAR Forecasts; BVAR Forecasts.

Vector autoregressions as a tool for forecast evaluations

In his article, “Vector Autoregressions as a Tool for Forecast Evaluation,” Roy H. Webb proposes that VAR forecasts be used as a standard of comparison for other forecasts. He begins by explaining how conventional forecasting models are constructed and used, and summarizes a few common objections to these models. He then describes the VAR methodology and compares forecasts from a simple VAR model with those from a consulting firm that uses a conventional model and with a series of consensus forecasts. The VAR model holds its own in this competition; in fact, only the VAR model is able to predict the 1981-1982 recession one year before its occurrence.Forecasting

Toward more accurate macroeconomic forecasts

A growing disenchantment with conventional economic models has resulted in increased interest in forecasting with vector autoregressive (VAR) models. In this article, Roy H. Webb develops a statistical procedure for determining the best configuration of explanatory variables in the equations of a VAR model. The resulting model forecasts more accurately than a conventional VAR model and is comparable to VARs improved through other popular methods. In addition, Webb’s procedure lets the data determine the form of the model and reduces the role of judgment in specifying equations, consistent with the atheoretical spirit of VAR models.Forecasting

VAR Models as Structural Approximations

This paper presents a way of estimating how accurate VAR models are likely to be for answering structural questions. Data are generated from a dynamic deterministic solution of a structural model; a VAR model is estimated using a subset of these data; and the properties of the VAR model are compared to the properties of the structural model. This procedure has the advantage of eliminating the effects of error terms, since the data are generated from a deterministic simulation. The results show that the VAR models do not seem to be good structural approximations.

Bayesian Analysis of Time-Varying Parameter Vector Autoregressive Model for the Japanese Economy and Monetary Policy

This paper analyzes the time-varying parameter vector autoregressive (TVP-VAR) model for the Japanese economy and monetary policy. The time-varying parameters are estimated via the Markov chain Monte Carlo method and the posterior estimates of parameters reveal the time-varying structure of the Japanese economy and monetary policy during the period from 1981 to 2008. The marginal likelihoods of the TVP-VAR model and other VAR models are also estimated. The estimated marginal likelihoods indicate that the TVP-VAR model best fits the Japanese economic data.Bayesian inference, Markov chain Monte Carlo, Monetary policy, State space model, Structural vector autoregressive model, Stochastic volatility, Time-varying parameter