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The purpose of this paper is to discuss the applicability of Global Positioning System (GPS) ionospheric delay correction models. Ionospheric delay is the most influential error source in GPS positioning, and ionospheric refraction is difficult to be corrected by dual frequency measurement for the common single frequency GPS receivers. Generally, ionospheric models are employed to correct errors. In order to analyze the ionospheric influence to GPS signals and the accuracy and adaptability of GPS ionospheric error correction models a quantificational analysis for ionospheric error correction models is absolutely necessary.

On the base of the mechanism of ionospheric error, the Klobuchar model that is widely used and actual measured correction model (including local and global ionospheric error correction models) are analyzed in detail. With the data about ionosphere obtained from GPS authority Crustal Dynamics Data Information System, the precision and adaptability of two kinds of ionospheric error correction model are validated, and a predigested method of investigating precision of local ionospheric error correction model is presented.

Klobuchar model has higher precision in middle or low latitude than in high latitude, and ionospheric delay fluctuates acutely in a day with a day‐cycle. Ionospheric delay varies as the latitude changes: ionospheric delay is largest around equator and smallest in the areas of two poles, which shows symmetry. The relationship between ionospheric delay and longitude is similar to the relationship between ionospheric delay and latitude. The fitting model has better effect than Klobuchar model.

This paper thoroughly researches GPS ionospheric error correction models. The conclusions are presented for the selection of GPS correction models, that it is useful for practical engineering application and will be the theoretic foundation for the improvement of the GPS accurate positioning.

Aerospace assembly demands high drilling position accuracy for fastener holes. Hole position error correction is a key issue to meet the required hole position accuracy. This paper aims to propose a combined hole position error correction method to achieve high positioning accuracy.

The bilinear interpolation surface function based on the shape of the aerospace structure is capable of dealing with position error of non-gravity deformation. A gravity deformation model is developed based on mechanics theory to efficiently correct deformation error caused by gravity. Moreover, three solution strategies of the average, least-squares and genetic optimization algorithms are used to solve the coefficients in the gravity deformation model to further improve position accuracy and efficiency.

Experimental validation shows that the combined position error correction method proposed in this paper significantly reduces the position errors of fastener holes from 1.106 to 0.123 mm. The total position error is reduced by 43.49% compared with the traditional mechanics theory method.

The position error correlation method could reach an accuracy of millimeter or submillimeter scale, which may not satisfy higher precision.

The proposed position error correction method has been integrated into the automatic drilling machine to ensure the drilling position accuracy.

The proposed position error method could promote the wide application of automatic drilling and riveting machining system in aerospace industry.

A combined position error correction method and the complete roadmap for error compensation are proposed. The position accuracy of fastener holes is reduced stably below 0.2 mm, which can fulfill the requirements of aero-structural assembly.

Demand forecasting has long been an imperative tenet in production planning especially in a make-to-order environment where a typical manufacturer has to balance the issues of holding excessive safety stocks and experiencing possible stockout. Many studies provide pragmatic paradigms to generate demand forecasts (mainly based on smoothing forecasting models.) At the same time, artificial neural networks (ANNs) have been emerging as alternatives. In this chapter, we propose a two-stage forecasting approach, which combines the strengths of a neural network with a more conventional exponential smoothing model. In the first stage of this approach, a smoothing model estimates the series of demand forecasts. In the second stage, general regression neural network (GRNN) is applied to learn and then correct the errors of estimates. Our empirical study evaluates the use of different static and dynamic smoothing models and calibrates their synergies with GRNN. Various statistical tests are performed to compare the performances of the two-stage models (with error correction by neural network) and those of the original single-stage models (without error-correction by neural network). Comparisons with the single-stage GRNN are also included. Statistical results show that neural network correction leads to improvements to the forecasts made by all examined smoothing models and can outperform the single-stage GRNN in most cases. Relative performances at different levels of demand lumpiness are also examined.

The Factor-augmented Error-Correction Model (FECM) generalizes the factor-augmented VAR (FAVAR) and the Error-Correction Model (ECM), combining error-correction, cointegration and dynamic factor models. It uses a larger set of variables compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latter’s specification in differences. In this paper, we review the specification and estimation of the FECM, and illustrate its use for forecasting and structural analysis by means of empirical applications based on Euro Area and US data.

Applies an error‐correction model to demand for money in five African economies: Congo, Côte d′Ivoire, Mauritius, Morocco and Tunisia. Attention is given to a set of opportunity cost variables including expected inflation, domestic interest rate, foreign interest rate and expected exchange‐rate depreciation. The empirical results show that the domestic interest rate plays a significant role in the demand for money functions for three of the five countries and external opportunity cost variables are significant for one of the others. The results show some diversity in money demand behaviour in the countries studied, but the error correction mechanism is always significant and in four out of five cases there is a short‐run inflation impact. The equations are subjected to a battery of tests and found to be statistically well‐behaved.

The objective of this study is to propose an economic model of the nominal money balances and reserves in the Turkish economy during the period 1960‐1988. As most of the variables show unit root non‐stationarity, an approach based on the error correction system (Phillips, 1991) is adopted. The estimated parameters of the long‐run money balance relationship based on this error correction system are very close to the Johansen‐Juselius (1990) vector autoregressive modelling approach. An error correction system and the vector autoregressive modelling approaches are alternative representations of the cointegrated systems. This study empirically demonstrates the closeness of the two systems using the data from the Turkish monetary sector. The econometric estimates of the elasticities are plausible. In small samples, both approaches may not yield almost identical estimates since the theory underlying these approaches is asymptotic.

This survey focuses on two families of nonlinear vector time series models, the family of vector threshold regression (VTR) models and that of vector smooth transition regression (VSTR) models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the equations. The emphasis is on stationary models, but the considerations also include nonstationary VTR and VSTR models with cointegrated variables. Model specification, estimation and evaluation is considered, and the use of the models illustrated by macroeconomic examples from the literature.

A panel error correction model has been developed to investigate the spatial correlation patterns among house prices. This paper aims to identify a dominant housing market in the ripple down process.

Seemingly unrelated regression estimators are adapted to deal with the contemporary correlations and heterogeneity across cities. Impulse response functions are subsequently implemented to simulate the spatial correlation patterns. The newly developed approach is then applied to the Australian capital city house price indices.

The results suggest that Melbourne should be recognised as the dominant housing market. Four levels were classified within the Australian house price interconnections, namely: Melbourne; Adelaide, Canberra, Perth and Sydney; Brisbane and Hobart; and Darwin.

This research develops a panel regression framework in addressing the spatial correlation patterns of house prices across cities. The ripple-down process of house price dynamics across cities was explored by capturing both the contemporary correlations and heterogeneity, and by identifying the dominant housing market.

Several recent studies have indicated the existence of a predictable component in stock prices. This study examines the sources of this serial correlation using error‐correction models. The results show that autocorrelated economic variables can generate serial correlation in stock returns. After these effects are accounted for, however, significant serial correlation in stock prices remains. The activities of noise traders and inefficiencies in the pricing of securities, within the context of limitations to the arbitrage process, are suggested as additional sources of serial correlation in stock prices.

The purpose of this article is to examine the causal relationship between crime rate measures (per 100,000 population) and police force size (full‐time employees per 100,000) within Milwaukee, Wisconsin. The data are annual, covering the years 1930 to 2004.

The authors specify and estimate ARIMA and error correction models to examine the bivariate association between police force strength and total, property, and personal crime rates for a large, mid‐western city.

Consistent with past research, the bivariate ARIMA analyses yield no evidence of a short‐term association between police force size and crime. However, the parameter estimates from error correction models indicate that changes in the level of crime have a longer‐term impact on police force strength.

This study focuses on a single municipality. Hence, before one can generalize to cities as a whole, the findings need to be replicated in other jurisdictions. Nonetheless, the findings do suggest that municipalities are more responsive to changes in the level of crime than prior ARIMA analyses seemed to indicate.

The findings point to the conclusion that, when studying causal processes that operate over time, one must be careful not to remove long run information from the data in the attempt to control for the spurious effects of autocorrelation.

This paper represents the first attempt to apply error correction models to the examination of the longitudinal relationship between crime and police force size.