Even though forecasting methods have advanced in the last few decades, economists still face a simple question: which prediction method gives most accurate results? Econometric can deal with different types of time series and good results, but specific cases, they may fail to provide predictions. Recently, new techniques borrowed from soft computing area were adopted for economic forecasting. S...

proposing a model to explain the concurrent decision making process in the labor and commodities markets in iranian urban households is the main objective of the present paper. the paper starts with introduction of the concepts of modem theories of family economics, with the view of expanding the scope of economics well into the households and it comes to conclusion with specification and fitti...

The ubiquity of strange attractors in nature suggests that nonlinear modeling techniques can improve performance in some signal processing applications. We introduce Mixed State Markov Models (MSMMs), a refinement of Hidden Filter HMMs, and apply both to a synthetic Double Scroll time series. Forecasts by HFHMMs diverge after a few steps. Using ad hoc procedures, forecasts by MSMMs, even models...

Many theories in political science predict the spatial clustering of similar behaviors among neighboring units of observation. This spatial autocorrelation poses implications for both inference and modeling that are distinct from the more familiar serial dependence in time series analysis. In this paper, I examine how political scientists can diagnose and model the spatial dependence that is pr...

We present an econometric technique for modeling endogenous selectivity in households’ quantity outcomes as observed in scanner panel data. Simultaneous models of incidence, brand choice and quantity, that treat quantity outcomes as count data, ignore such self-selectivity considerations in quantity outcomes. Previously proposed approaches to modeling selectivity in continuous quantity outcomes...

It has long been a theme of real estate professionals that real estate markets are economically fragmented. These markets are presumed to be characterized by product differentiation, by small and isolated sub-pools of participants, and by market segmentation. This view is intuitively compelling, but sobering. It taints the prospects for modern econometric forecasting of office markets because e...

This paper provides an example of several modeling and econometric advances used in the panel estimation of energy demand elasticities. The paper models the demand of total, industrial, and transport energy consumption and residential and commercial electricity consumption by analyzing US state-based panel data. The paper employs recently developed dynamic panel methods that address heterogenei...

OPTCON is an algorithm for the optimal control of nonlinear stochastic systems which is particularly applicable to econometric models. It delivers approximate numerical solutions to optimum control problems with a quadratic objective function for nonlinear econometric models with additive and multiplicative (parameter) uncertainties. The algorithm was programmed in C# and allows for determinist...

We describe the use of the wavelet transform for multivariate data analysis problems. In prediction, a multiscale transform of time-varying data can allow forecasts of each scale, followed by combining of the individual forecasts. The use of a wavelet transform with noise modeling for point pattern clustering can lead to the result, which initially appears counter-intuitive, of clustering in co...

This paper provides a survey of three families of flexible parametric probability density functions (the skewed generalized t, the exponential generalized beta of the second kind, and the inverse hyperbolic sine distributions) which can be used in modeling a wide variety of econometric problems. A figure, which can facilitate model selection, summarizing the admissible combinations of skewness ...