This paper derives several Lagrange Multiplier tests for the panel data regression model wih spatial error correlation. These tests draw upon two strands of earlier work. The Þrst is the LM tests for the spatial error correlation model discussed in Anselin (1988, 1999) and Anselin, Bera, Florax and Yoon (1996), and the second is the LM tests for the error component panel data model discussed in...

In this paper, we show that the Chapman-Kolmogorov formula could be used as a recursive formula for computing the m-step-ahead conditional density of a Markov bilinear model. The stationary marginal probability density function of the model may be approximated by the m-step-ahead conditional density for sufficiently large m.

We address the problem of compressing correlated distributed video signals that are captured from a dynamic scene. The correlated video signals originate from cameras that are not co-located or that cannot cooperate to directly exploit their correlation. However, the decoder is able to exploit the coded information from all cameras to achieve the best reconstruction of the correlated video sign...

A panel data regression model with heteroskedastic as well as spatially correlated disturbancesis considered, and a joint LM test for homoskedasticity and no spatial correlation is derived. In addition, a conditional LM test for no spatial correlation given heteroskedasticity, as well as a conditional LM test for homoskedasticity given spatial correlation, are also derived. These LM tests are c...

We propose a new method for multivariate forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the multivariate Generalized Autoregressive Conditionally Heteroskedastic (GARCH) model. We assume that the dynamic common factors are conditionally heteroskedastic. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the common an...

We show how the ARMA-Power GARCH model for the conditional mean and variance can be adapted to analyze times series data showing asymmetry. Dynamics is introduced in the location and the dispersion parameters of skewed location-scale distributions using the same type of structure found in the conditional mean and in the conditional variance in the ARMA-APARCH model. We also propose a general dy...

Economic development requires the growth of productive …rms. However, …nancing constraints may limit …rms’investment abilities. This paper estimates the cost of …nancing constraints to …rms, for example in terms of idle investment opportunities, and their aggregate implications. To this end, I develop and estimate a dynamic model of …rm-level investment. The model allows me to deal with the mai...

in this paper, we investigate variations of gold coin price and also probe to model the fluctuations and conditional variance of coin market returns. the data consist of daily market prices of gold coin over the 1380 – 1386 period. since volatility clustering is viewed in time series of returns, we employ arch (autoregressive conditional heteroskedasticity) methodology in order to model the var...

CpG islands are short stretches in DNA sequence whose frequency of cytosine(C)and guanine (G) is higher than background of DNA sequence. They are around the promoter of frequently expressed genes. The conventional way to recognize CpG islands is to use the hidden Markov models (HMMs). While HMMs are known to suffer from not being able to capture long dynamic range information, they usually does...

The paper considers a variable length Markov chain model associated with a group of stationary processes that share the same context tree but potentially different conditional probabilities. We propose a new model selection and estimation method, develop oracle inequalities and model selection properties for the estimator. These results also provide conditions under which the use of the group s...