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on Econometric Time Series |
| By: | Millimet, Daniel L. (Southern Methodist University); McDonough, Ian K. (Southern Methodist University) |
| Abstract: | With the increased availability of longitudinal data, dynamic panel data models have become commonplace. Moreover, the properties of various estimators of such models are well known. However, we show that these estimators breakdown when the data are irregularly spaced along the time dimension. Unfortunately, this is an increasingly frequent occurrence as many longitudinal surveys are collected at non-uniform intervals and no solution is currently available when time-varying covariates are included in the model. In this paper, we propose several new estimators for dynamic panel data models when data are irregularly spaced and compare their finite sample performance to the naïve application of existing estimators. We illustrate the practical importance of this issue by turning to two applications on early childhood development. |
| Keywords: | panel data, irregular spacing, interactive fixed effects, student achievement, obesity |
| JEL: | C23 C51 I21 |
| Date: | 2013–04 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp7359&r=ets |
| By: | Boonsoo Koo; Myung Hwan Seo |
| Abstract: | This paper revisits the least squares estimator of the linear regression with a structural break. We view the model as an approximation to the true data generating process whose exact nature is unknown but perhaps changing over time either continuously or with some jumps. This view is widely held in the forecasting literature and under this view, the time series dependence property of all the observed variables is unstable as well. We establish that the rate of convergence of the estimator to a properly defined limit is much slower than the standard super consistent rate, even slower than the square root of the sample size T and as slow as the cube root of T. We also provide an asymptotic distribution of the estimator and that of the Gaussian quasi likelihood ratio statistic for a certain class of true data generating process. We relate our finding to current forecast combination methods and bagging and propose a new averaging scheme. The performance of various contemporary forecasting methods is compared to ours using a number of macroeconomic data. |
| Keywords: | structural breaks, forecasting, mis-specification, cube-root asymptotics, bagging |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2013-11&r=ets |
| By: | Nazarian, Rafik; Gandali Alikhani, Nadiya; Naderi, Esmaeil; Amiri, Ashkan |
| Abstract: | Recently, with the development of financial markets and due to the importance of these markets and their close relationship with other macroeconomic variables, using advanced mathematical models with complicated structures for forecasting these markets has become very popular. Besides, neural network models have gained a special position compared to other advanced models due to their high accuracy in forecasting different variables. Therefore, the main purpose of this study was to forecast the volatilities of TSE index by regressive models with long memory feature, feed forward neural network and hybrid models (based on forecast combination approach) using daily data. The results were indicative of the fact that based on the criteria for assessing forecasting error, i.e., MSE and RMSE, although forecasting errors of the feed forward neural network model were less than ARFIMA-FIGARCH model, the accuracy of the hybrid model of neural network and best GARCH was higher than each one of these models. |
| Keywords: | Stock Return, Long Memory, Neural Network, Hybrid Models. |
| JEL: | C14 C22 C45 C53 |
| Date: | 2013–03–15 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:46786&r=ets |
| By: | Tommaso Proietti (University of Rome "Tor Vergata"); Alessandra Luati (University of Bologna) |
| Abstract: | The exponential model for the spectrum of a time series and its fractional extensions are based on the Fourier series expansion of the logarithm of the spectral density. The coefficients of the expansion form the cepstrum of the time series. After deriving the cepstrum of important classes of time series processes, also featuring long memory, we discuss likelihood inferences based on the periodogram, for which the estimation of the cepstrum yields a generalized linear model for exponential data with logarithmic link, focusing on the issue of separating the contribution of the long memory component to the log-spectrum. We then propose two extensions. The first deals with replacing the logarithmic link with a more general Box-Cox link, which encompasses also the identity and the inverse links: this enables nesting alternative spectral estimation methods (autoregressive, exponential, etc.) under the same likelihood-based framework. Secondly, we propose a gradient boosting algorithm for the estimation of the log-spectrum and illustrate its potential for distilling the long memory component of the log-spectrum. |
| Keywords: | Frequency Domain Methods; Generalized linear models; Long Memory; Boosting. |
| Date: | 2013–04–19 |
| URL: | http://d.repec.org/n?u=RePEc:rtv:ceisrp:272&r=ets |
| By: | Tommaso Proietti (University of Rome "Tor Vergata"); Alessandra Luati (University of Bologna) |
| Abstract: | The generalised autocovariance function is defined for a stationary stochastic process as the inverse Fourier transform of the power transformation of the spectral density function. Depending on the value of the transformation parameter, this function nests the inverse and the traditional autocovariance functions. A frequency domain non-parametric estimator based on the power transformation of the pooled periodogram is considered and its asymptotic distribution is derived. The results are employed to construct classes of tests of the white noise hypothesis, for clustering and discrimination of stochastic processes and to introduce a novel feature matching estimator of the spectrum. |
| Keywords: | Stationary Gaussian processes. Non-parametric spectral estimation. White noise tests. Feature matching. Discriminant Analysis |
| Date: | 2013–04–30 |
| URL: | http://d.repec.org/n?u=RePEc:rtv:ceisrp:276&r=ets |