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This study uses the neural network and econometric models to explore the importance of fiscal and monetary policy on GNP. The findings suggest that fiscal policy is more influential than monetary policy, and the neural network forecasts of GNP are more accurate and have less variation than those of the econometric approach.

In the context of competing theoretical economic–econometric models and corresponding estimators, we demonstrate a semiparametric combining estimator that, under quadratic loss, has superior risk performance. The method eliminates the need for pretesting to decide between members of the relevant family of econometric models and demonstrates, under quadratic loss, the nonoptimality of the conventional pretest estimator. First-order asymptotic properties of the combined estimator are demonstrated. A sampling study is used to illustrate finite sample performance over a range of econometric model sampling designs that includes performance relative to a Hausman-type model selection pretest estimator. An important empirical problem from the causal effects literature is analyzed to indicate the applicability and econometric implications of the methodology. This combining estimation and inference framework can be extended to a range of models and corresponding estimators. The combining estimator is novel in that it provides directly minimum quadratic loss solutions.

In an emerging global economy the ability of the telecommunications sector to provide an internationally competitive network for transferring information has significant implications for trade and economic growth. Because of recent large world‐wide investments in telecommunications infrastructure, quantifying the impact of telecommunications in economic growth has received much attention. However, economic analysts, in the absence of investment data for many developing countries, adopt the International Telecommunications (ITU) practice of using main telephone lines to measure the stock of telecommunications capital. The accuracy of this proxy has not been subject to careful statistical scrutiny. This study develops a supply‐side growth model which employs teledensity and the share of telecommunications investment in national income as telecommunications capital proxies. Estimation results suggest a significant positive cross‐country relationship between telecommunications capital and economic growth, when using alternative measures of telecommunications capital.

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Finally, some thoughts are presented that relate to the future of Bayesian econometrics.

Structural models' inflation forecasts are often inferior to those of naïve devices. This chapter theoretically and empirically assesses this for UK annual and quarterly inflation, using the theoretical framework in Clements and Hendry (1998, 1999). Forecasts from equilibrium-correction mechanisms, built by automatic model selection, are compared to various robust devices. Forecast-error taxonomies for aggregated and time-disaggregated information reveal that the impacts of structural breaks are identical between these, helping to interpret the empirical findings. Forecast failures in structural models are driven by their deterministic terms, confirming location shifts as a pernicious cause thereof, and explaining the success of robust devices.

The purpose of this paper is to predict the number of undergraduate applicants for the National Entrance Examination in Iran during 2012-2025 periods and to identity the factors affecting the demand for higher education in Iran.

In this paper, the method of cohort, participation rate, structural regression and time series econometric models have been used. In the present study, it has been predicted by using four methods mentioned and at the next step, in addition to identifying effective factors, the results given from these four methods have been compared with each other. Furthermore, the most important factors influencing university enrollment decision have been identified by econometric method.

The results of estimating the number of the criteria applicants, show that the tendency to pursue studies is different between males and females. Therefore, their structural models differ from each other. The results of forecast in structural method support the high effectiveness of economic growth index. Most predictions are often confirmed the reduction in the number of applicants during the 2012-2025 period, especially for men.

This paper can be helpful in opening up a discourse around cross-cultural elements in higher education demands and planning for higher education.

It’s important to forecast the demand for higher education using different methods, and to compare the results for specific countries.

This article reviews the literature on the econometric relationship between DSGE and VAR models from the point of view of estimation and model validation. The mapping between DSGE and VAR models is broken down into three stages: (1) from DSGE to state-space model; (2) from state-space model to VAR( ∞ ); (3) from VAR( ∞ ) to finite-order VAR. The focus is on discussing what can go wrong at each step of this mapping and on critically highlighting the hidden assumptions. I also point out some open research questions and interesting new research directions in the literature on the econometrics of DSGE models. These include, in no particular order: understanding the effects of log-linearization on estimation and identification; dealing with multiplicity of equilibria; estimating nonlinear DSGE models; incorporating into DSGE models information from atheoretical models and from survey data; adopting flexible modeling approaches that combine the theoretical rigor of DSGE models and the econometric model’s ability to fit the data.

Makes three contributions to the ongoing debate over whether racial discrimination is disappearing, and white privilege eroding. First, develops an argument concerning why many economists treat empirical evidence of racial discrimination with skepticism or indifference. Second, presents some new econometric results which provide empirical insight into whether racial inequality is disappearing in residential credit markets. These results suggest that for African Americans and Latinos, racial disadvantage remains statistically significant in most cities, though its magnitude has fallen during the 1990s in many cities. Third, suggests an empirical implementation of “white privilege” in the residential credit market. Consistently finds white advantage in credit markets to be statistically significant in an econometric model of residential loan approval and denial.