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1.
A new approach of model parameter estimation is used with simulated measurements to recover both biological and economic input parameters of a natural resource model. The data assimilation technique is the variational adjoint method (VAM) for parameter estimation. It efficiently combines time series of artificial data with a simple bioeconomic fisheries model to optimally estimate the model parameters. Using identical twin experiments, it is shown that the parameters of the model can be retrieved. The procedure provides an efficient way of calculating poorly known model parameters by fitting model results to simulated data. In separate experiments with exact and noisy data, we have demonstrated that the VAM can be an efficient method of analyzing bioeconomic data.  相似文献   

2.
In this paper, an information matrix (IM)-based test is developed for testing the hypothesis of constant relative risk aversion parameter in the GARCH-M set up. A detailed Monte Carlo study is then carried out to evaluate the performance of this test in terms of size and power. Further, a bootstrap technique is suggested to correct the over-size problem found in small samples. The proposed test is then applied to the time series of returns on stock markets of five important countries to examine whether this important hypothesis holds or not, and it is found that the relative risk aversion parameter is not time invariant for all the five time series.  相似文献   

3.
A common problem with micro‐level analysis is that capital stock data is missing. Typically, a feasible measure of capital is calculated by accumulating investment flows from an initial value of the capital stock. As the time dimension of most disaggregated data is rather short, the choice of this initial value can have significant effects on the resulting capital estimates. Most empirical studies impute the initial value using a single arbitrary proxy. In this paper, we propose a panel data framework that assigns weighting coefficients to multiple proxy variables. We conduct a series of Monte Carlo experiments to test the performance of the proposed method and apply the method to a U.S. manufacturing dataset. The results suggest that our method improves the approximation of the capital stock and thus in turn reduces the bias in the production function estimation.  相似文献   

4.
Japanese stock markets have two types of breaks, overnight and lunch, during which no trading occurs, causing an inevitable increased variance in estimating daily volatility via a naive realized variance (RV). In order to perform a more stabilized estimation, we modify Hansen and Lunde's weighting technique. As an empirical study, we estimate optimal weights by using a particular approach for Japanese stock data listed on the Tokyo Stock Exchange, and then compare the forecast performance of weighted and non‐weighted RV through an autoregressive fractionally integrated moving average model. The empirical result indicates that the appropriate use of the optimally weighted RV can lead to remarkably smaller estimation variance compared with the naive RV, in many series. Therefore a more accurate forecasting of daily volatility data is obtained. Finally, we perform a Monte Carlo simulation to support the empirical result.  相似文献   

5.
Based on the general time-varying parameter vector autoregressive model and data mining technology, this study proposes a new extension mixed innovation time-varying parameter stochastic volatility vector autoregressive model and investigates time-varying characteristics and efficiencies of different shock effects on China’s monetary policy towards inflation and GDP. Using sample monthly data for 1979–2014, we utilize typical time points to illustrate the mechanisms between different economic variables via the Markov Chain Monte Carlo method and impulse response function. The empirical results show that the monetary transmission mechanism in China can be effective in the real economy, but with delay and efficiency leakage. The average delay and maximum efficiency can be measured through the MI model, which can capture accurate information of economic variables, effectively improving the precision of macroeconomic regulation and control. Meanwhile, the difference between the impacts of different channels is obvious; while the impact of interest rates is not significant, the impact of stock market is significant. The action mechanism between GDP and the inflation rate undergoes a gradual structural change, evidently displaying time-varying characteristics and a gradually weakening impact over time.  相似文献   

6.
This paper proposes a focused information criterion for variable selection in partially linear models. Our criterion is designed to select an optimal model for estimating a focus parameter, which is a parameter of interest. We estimate the model using the series method and jointly select the variables in the linear part and the series length in the nonparametric part. A Monte Carlo simulation shows that the proposed focused information criterion successfully selects the model that has a relatively small mean squared error of the estimator for the focus parameter.  相似文献   

7.
I consider a bivariate stationary fractional cointegration system and I propose a quasi-maximum likelihood estimator based on the Whittle analysis of the joint spectral density of the regressor and errors. This allows to estimate jointly all parameters of interest of the model. I lead a Monte Carlo experiment to investigate the finite sample properties of this estimator when integration orders are less than 1/2. However, it is not so easy for practitioners to identify whether or not the observed time series are stationary. This issue is investigated by extending the numerical analysis to mean-reverting non-stationary region of the parameter space, although the proposed estimator is not theoretically designed to handle this case. The results display good finite sample properties in both cases, stationary and non-stationary. Thereby, it reveals that making a wrong decision on the stationarity of raw series does not lead to an erroneous conclusion. An application to the stock market synchronization is proposed to illustrate the empirical relevance of this estimator.  相似文献   

8.

We find the closed form solution for the joint probability of the running maximum and the drawdown of the Brownian motion with a non-zero drift parameter at a random time that is exponentially distributed and independent of the Brownian motion. This characterization leads us to come up with a robust method of estimating volatility using open, high, low and closing prices. We rigorously show the independence of robust volatility estimators based on extreme values of asset prices relative to the standard robust volatility estimator based on closing price alone. We further prove that the proposed robust volatility ratio is unbiased with no drift parameter. Moreover, we find that the robust volatility ratio with a non-zero drift parameter has only a second order effect. We have shown that our proposed extreme value robust volatility estimator is 2–3 times relatively more efficient when compared to the classical robust volatility estimator based on Monte Carlo simulation experiment. On the empirical side, we test the proposed robust volatility ratio based on high and low prices on different asset classes like stock indices, exchange rate and precious metals.

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9.
This paper develops a Bayesian model comparison of two broad major classes of varying volatility model, the generalized autoregressive conditional heteroskedasticity and stochastic volatility models, on financial time series. The leverage effect, jumps and heavy‐tailed errors are incorporated into the two models. For estimation, the efficient Markov chain Monte Carlo methods are developed and the model comparisons are examined based on the marginal likelihood. The empirical analyses are illustrated using the daily return data of US stock indices, individual securities and exchange rates of UK sterling and Japanese yen against the US dollar. The estimation results indicate that the stochastic volatility model with leverage and Student‐t errors yield the best performance among the competing models.  相似文献   

10.
This paper proposes a latent dynamic factor model for high-dimensional realized covariance matrices of stock returns. The approach is based on the matrix logarithm and combines common latent factors driven by HAR processes and idiosyncratic autoregressive dynamics. The model accounts for positive definiteness of covariance matrices without imposing parametric restrictions. Simulated Bayesian parameter estimates are obtained using basic Markov chain Monte Carlo methods. An empirical application to 5-dimensional and 30-dimensional realized covariance matrices shows remarkably good forecasting results, in-sample and out-of-sample.  相似文献   

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