Recent advances in deep learning, especially the long short-term memory (LSTM) networks, provide some useful insights on how to tackle time series prediction problems, not mention development of a model itself for prediction. Runoff forecasting is problem with past runoff data (water level and discharge data) as inputs fixed-length future output. Most previous work paid attention sufficiency in...

In this paper we present the different approaches to modelling dynamic regression and structural equations• First, we give a survey of the traditional econometrie and the time series approaches to specification analysis of dynamic econometrie models. Then, we describe the three testing procedures that are presently available to the econometrician: (1) testing for misspecification or diagnostic ...

This paper focuses on the application of modern econometric techniques to a model of the demand for private housing in Ireland. The paper will firstly outline the theoretical econometric framework in order to explain the concept of cointegration within an economic model framework. The Johansen method for analyzing economic models is demonstrated and it is shown how the method can be applied to ...

Macroeconometric and …nancial researchers often use binary data constructed in a way that creates serial dependence. We show that this dependence can be allowed for if the binary states are treated as Markov processes. In addition, the methods of construction ensure that certain sequences are never observed in the constructed data. Together these features make it di¢ cult to utilize static and ...