The Experts below are selected from a list of 15633 Experts worldwide ranked by ideXlab platform

Jozef Zbigniew Dziechciarz - One of the best experts on this subject based on the ideXlab platform.

Simonetta Longhi - One of the best experts on this subject based on the ideXlab platform.

  • forecasting Regional Labor Market developments under spatial autocorrelation
    International Regional Science Review, 2007
    Co-Authors: Simonetta Longhi, Peter Nijkamp
    Abstract:

    Because of heterogeneity across regions, economic policy measures are increasingly targeted at the Regional level and, therefore, require Regional forecasts. The data available to compute Regional forecasts are usually a pseudo panel of a limited number of observations over time and a large number of regions strongly interacting with each other. Traditional time-series techniques applied to distinct time series of Regional data are probably a suboptimal forecasting strategy. Although both linear and nonlinear models have been applied and evaluated to forecast socioeconomic variables, spatial interactions among regions are often ignored. This article evaluates the ability of spatial error and spatial lag models to correct for misspecifications due to neglected spatial autocorrelation in the data. The empirical application on short-term forecasts of employment in 326 West German regions shows that the superimposed spatial structure that is required for the estimation of spatial models improves the forecasting performance of nonspatial models.

  • forecasting Regional Labor Market developments under spatial autocorrelation
    International Regional Science Review, 2007
    Co-Authors: Simonetta Longhi, Peter Nijkamp
    Abstract:

    Because of heterogeneity across regions, economic policy measures are increasingly targeted at the Regional level and, therefore, require Regional forecasts. The data available to compute Regional forecasts are usually a pseudo panel of a limited number of observations over time and a large number of regions strongly interacting with each other. Traditional time-series techniques applied to distinct time series of Regional data are probably a suboptimal forecasting strategy. Although both linear and nonlinear models have been applied and evaluated to forecast socioeconomic variables, spatial interactions among regions are often ignored. This article evaluates the ability of spatial error and spatial lag models to correct for misspecifications due to neglected spatial autocorrelation in the data. The empirical application on short-term forecasts of employment in 326 West German regions shows that the superimposed spatial structure that is required for the estimation of spatial models improves the forecasti...

  • a rank order test on the statistical performance of neural network models for Regional Labor Market forecasts
    Social Science Research Network, 2007
    Co-Authors: Roberto Patuelli, Simonetta Longhi, Peter Nijkamp, Aura Reggiani, Uwe Blien
    Abstract:

    Using a panel of 439 German regions we evaluate and compare the performance of various Neural Network (NN) models as forecasting tools for Regional employment growth. Because of relevant differences in data availability between the former East and West Germany, the NN models are computed separately for the two parts of the country. The comparisons of the models and their ex post forecasts are carried out by means of a non-parametric test: viz. the Friedman statistic. The Friedman statistic tests the consistency of model results obtained in terms of their rank order. Since there is no normal distribution assumption, this methodology is an interesting substitute for a standard analysis of variance. Furthermore, the Friedman statistic is indifferent to the scale on which the data are measured. The evaluation of the ex post forecasts suggests that NN models are generally able to correctly identify the fastest-growing and the slowest-growing regions, and hence predict rather well the correct ranking of regions in terms of their employment growth. The comparison among NN models – on the basis of several criteria – suggests that the choice of the variables used in the model may influence the model’s performance and the reliability of its forecasts.

  • forecasting Regional Labor Market developments under spatial heterogeneity and spatial correlation
    Research Papers in Economics, 2006
    Co-Authors: Simonetta Longhi, Peter Nijkamp
    Abstract:

    Because of heterogeneity across regions, economic policy measures are increasingly targeted at the Regional level, and the need for forecasts at the Regional level is rapidly increasing. The data available to compute Regional forecasts is usually based on a pseudo-panel of a limited number of observations over time, and a large number of areas (regions) strongly interacting with each other. The application of traditional time-series techniques to distinct time series of Regional data is likely to be a suboptimal forecasting strategy. In the field of Regional forecasting of socioeconomic variables, both linear and nonlinear models have recently been applied and evaluated. However, often such analyses ignore the spatial interactions among regions. We evaluate the ability of different statistical techniques - namely spatial error and spatial cross-regressive models - to correct for misspecifications due to neglected spatial correlation in the data. Our empirical application concerns short-term forecasts of employment in 326 West German regions; we find that the superimposed spatial structure that is required for the estimation of spatial models improves the forecasting performance of non-spatial models.

  • a rank order analysis of learning models for Regional Labor Market forecasting
    Urban Regional, 2005
    Co-Authors: Roberto Patuelli, Simonetta Longhi, Peter Nijkamp, Aura Reggiani, Uwe Blien
    Abstract:

    Using a panel of 439 German regions we evaluate and compare the performance of various Neural Network (NN) models as forecasting tools for Regional employment growth. Because of relevant differences in data availability between the former East and West Germany, NN models are computed separately for the two parts of the country. The comparisons of the models and their ex-post forecasts have been carried out by means of a non-parametric test: viz. the Friedman statistic. The Friedman statistic tests the consistency of model results obtained in terms of their rank order. Since there is no normal distribution assumption, this methodology is an interesting substitute for a standard analysis of variance. Furthermore, the Friedman statistic is indifferent to the scale on which the data are measured. The evaluation of the ex-post forecasts suggests that NN models are generally able to correctly identify the fastest-growing and the slowest-growing regions, and hence predict rather well the correct ranking of regions in terms of their employment growth. The comparison among NN models – on the basis of several criteria – suggests that the choice of the variables used in the model may influence the model’s performance and the reliability of its forecasts.

Peter Nijkamp - One of the best experts on this subject based on the ideXlab platform.

  • forecasting Regional Labor Market developments under spatial autocorrelation
    International Regional Science Review, 2007
    Co-Authors: Simonetta Longhi, Peter Nijkamp
    Abstract:

    Because of heterogeneity across regions, economic policy measures are increasingly targeted at the Regional level and, therefore, require Regional forecasts. The data available to compute Regional forecasts are usually a pseudo panel of a limited number of observations over time and a large number of regions strongly interacting with each other. Traditional time-series techniques applied to distinct time series of Regional data are probably a suboptimal forecasting strategy. Although both linear and nonlinear models have been applied and evaluated to forecast socioeconomic variables, spatial interactions among regions are often ignored. This article evaluates the ability of spatial error and spatial lag models to correct for misspecifications due to neglected spatial autocorrelation in the data. The empirical application on short-term forecasts of employment in 326 West German regions shows that the superimposed spatial structure that is required for the estimation of spatial models improves the forecasting performance of nonspatial models.

  • forecasting Regional Labor Market developments under spatial autocorrelation
    International Regional Science Review, 2007
    Co-Authors: Simonetta Longhi, Peter Nijkamp
    Abstract:

    Because of heterogeneity across regions, economic policy measures are increasingly targeted at the Regional level and, therefore, require Regional forecasts. The data available to compute Regional forecasts are usually a pseudo panel of a limited number of observations over time and a large number of regions strongly interacting with each other. Traditional time-series techniques applied to distinct time series of Regional data are probably a suboptimal forecasting strategy. Although both linear and nonlinear models have been applied and evaluated to forecast socioeconomic variables, spatial interactions among regions are often ignored. This article evaluates the ability of spatial error and spatial lag models to correct for misspecifications due to neglected spatial autocorrelation in the data. The empirical application on short-term forecasts of employment in 326 West German regions shows that the superimposed spatial structure that is required for the estimation of spatial models improves the forecasti...

  • a rank order test on the statistical performance of neural network models for Regional Labor Market forecasts
    Social Science Research Network, 2007
    Co-Authors: Roberto Patuelli, Simonetta Longhi, Peter Nijkamp, Aura Reggiani, Uwe Blien
    Abstract:

    Using a panel of 439 German regions we evaluate and compare the performance of various Neural Network (NN) models as forecasting tools for Regional employment growth. Because of relevant differences in data availability between the former East and West Germany, the NN models are computed separately for the two parts of the country. The comparisons of the models and their ex post forecasts are carried out by means of a non-parametric test: viz. the Friedman statistic. The Friedman statistic tests the consistency of model results obtained in terms of their rank order. Since there is no normal distribution assumption, this methodology is an interesting substitute for a standard analysis of variance. Furthermore, the Friedman statistic is indifferent to the scale on which the data are measured. The evaluation of the ex post forecasts suggests that NN models are generally able to correctly identify the fastest-growing and the slowest-growing regions, and hence predict rather well the correct ranking of regions in terms of their employment growth. The comparison among NN models – on the basis of several criteria – suggests that the choice of the variables used in the model may influence the model’s performance and the reliability of its forecasts.

  • forecasting Regional Labor Market developments under spatial heterogeneity and spatial correlation
    Research Papers in Economics, 2006
    Co-Authors: Simonetta Longhi, Peter Nijkamp
    Abstract:

    Because of heterogeneity across regions, economic policy measures are increasingly targeted at the Regional level, and the need for forecasts at the Regional level is rapidly increasing. The data available to compute Regional forecasts is usually based on a pseudo-panel of a limited number of observations over time, and a large number of areas (regions) strongly interacting with each other. The application of traditional time-series techniques to distinct time series of Regional data is likely to be a suboptimal forecasting strategy. In the field of Regional forecasting of socioeconomic variables, both linear and nonlinear models have recently been applied and evaluated. However, often such analyses ignore the spatial interactions among regions. We evaluate the ability of different statistical techniques - namely spatial error and spatial cross-regressive models - to correct for misspecifications due to neglected spatial correlation in the data. Our empirical application concerns short-term forecasts of employment in 326 West German regions; we find that the superimposed spatial structure that is required for the estimation of spatial models improves the forecasting performance of non-spatial models.

  • a rank order analysis of learning models for Regional Labor Market forecasting
    Urban Regional, 2005
    Co-Authors: Roberto Patuelli, Simonetta Longhi, Peter Nijkamp, Aura Reggiani, Uwe Blien
    Abstract:

    Using a panel of 439 German regions we evaluate and compare the performance of various Neural Network (NN) models as forecasting tools for Regional employment growth. Because of relevant differences in data availability between the former East and West Germany, NN models are computed separately for the two parts of the country. The comparisons of the models and their ex-post forecasts have been carried out by means of a non-parametric test: viz. the Friedman statistic. The Friedman statistic tests the consistency of model results obtained in terms of their rank order. Since there is no normal distribution assumption, this methodology is an interesting substitute for a standard analysis of variance. Furthermore, the Friedman statistic is indifferent to the scale on which the data are measured. The evaluation of the ex-post forecasts suggests that NN models are generally able to correctly identify the fastest-growing and the slowest-growing regions, and hence predict rather well the correct ranking of regions in terms of their employment growth. The comparison among NN models – on the basis of several criteria – suggests that the choice of the variables used in the model may influence the model’s performance and the reliability of its forecasts.

Uwe Blien - One of the best experts on this subject based on the ideXlab platform.

  • a rank order test on the statistical performance of neural network models for Regional Labor Market forecasts
    Social Science Research Network, 2007
    Co-Authors: Roberto Patuelli, Simonetta Longhi, Peter Nijkamp, Aura Reggiani, Uwe Blien
    Abstract:

    Using a panel of 439 German regions we evaluate and compare the performance of various Neural Network (NN) models as forecasting tools for Regional employment growth. Because of relevant differences in data availability between the former East and West Germany, the NN models are computed separately for the two parts of the country. The comparisons of the models and their ex post forecasts are carried out by means of a non-parametric test: viz. the Friedman statistic. The Friedman statistic tests the consistency of model results obtained in terms of their rank order. Since there is no normal distribution assumption, this methodology is an interesting substitute for a standard analysis of variance. Furthermore, the Friedman statistic is indifferent to the scale on which the data are measured. The evaluation of the ex post forecasts suggests that NN models are generally able to correctly identify the fastest-growing and the slowest-growing regions, and hence predict rather well the correct ranking of regions in terms of their employment growth. The comparison among NN models – on the basis of several criteria – suggests that the choice of the variables used in the model may influence the model’s performance and the reliability of its forecasts.

  • a rank order analysis of learning models for Regional Labor Market forecasting
    Urban Regional, 2005
    Co-Authors: Roberto Patuelli, Simonetta Longhi, Peter Nijkamp, Aura Reggiani, Uwe Blien
    Abstract:

    Using a panel of 439 German regions we evaluate and compare the performance of various Neural Network (NN) models as forecasting tools for Regional employment growth. Because of relevant differences in data availability between the former East and West Germany, NN models are computed separately for the two parts of the country. The comparisons of the models and their ex-post forecasts have been carried out by means of a non-parametric test: viz. the Friedman statistic. The Friedman statistic tests the consistency of model results obtained in terms of their rank order. Since there is no normal distribution assumption, this methodology is an interesting substitute for a standard analysis of variance. Furthermore, the Friedman statistic is indifferent to the scale on which the data are measured. The evaluation of the ex-post forecasts suggests that NN models are generally able to correctly identify the fastest-growing and the slowest-growing regions, and hence predict rather well the correct ranking of regions in terms of their employment growth. The comparison among NN models – on the basis of several criteria – suggests that the choice of the variables used in the model may influence the model’s performance and the reliability of its forecasts.

Prakash Loungani - One of the best experts on this subject based on the ideXlab platform.

  • Regional Labor Market adjustment in the united states trend and cycle
    The Review of Economics and Statistics, 2017
    Co-Authors: Mai Dao, Davide Furceri, Prakash Loungani
    Abstract:

    We present new evidence on the evolution of Labor mobility in the United States over the past four decades. Building on the seminal methodology by Blanchard and Katz (1992), combined with multiple sources of Regional population and migration data, we show that interstate mobility in response to relative Labor demand conditions is not as high as previously established and has been weakening since the early 1990s. In addition, we find that mobility is countercyclical: net migration across regions responds more strongly to spatial disparities in recessions than in normal times. While the declining trend in mobility has been driven by weaker out-migration from states experiencing negative relative shocks, the mobility surge in recessions is mostly accounted for by temporarily stronger in-migration to better-performing states.

  • Regional Labor Market adjustments in the united states
    Regional Labor Market Adjustments in the United States, 2014
    Co-Authors: Mai Dao, Davide Furceri, Prakash Loungani
    Abstract:

    We examine patterns of Regional adjustments to shocks in the US during the past four decades. We find that the response of interstate migration to relative Labor Market conditions has decreased, while the role of the unemployment rate as absorber of Regional shocks has increased. However, the response of net migration to Regional shocks is stronger during aggregate downturns and increased particularly during the Great Recession. We offer a potential explanation for the cyclical pattern of migration response based on the variation in consumption risk sharing.

  • Regional Labor Market adjustments in the united states and europe
    Regional Labor Market Adjustments in the United States and Europe, 2014
    Co-Authors: Mai Dao, Davide Furceri, Prakash Loungani
    Abstract:

    We examine patterns of Regional adjustments to shocks in the US during the past 40 years. Using state-level data, we estimate the dynamic response of Regional employment, unemployment, participation rates and net migration to state-relative Labor demand shocks. We find that (i) the long-run effect of a state-specific shock on the state employment level has decreased over time, suggesting less overall net migration in response to a Regional shock, (ii) the role of the participation rate as absorber of Regional shocks has increased, (iii) the response of net migration to Regional shocks is stronger, while that of relative unemployment is weaker during aggregate downturns, and (iv) the change in the response intensity of migration is related to the declining trend in Regional dispersion of Labor Market conditions. Finally, using Regional data for a set of 21 European countries, we show that while the short-term response of participation rates to Labor demand shocks is typically larger in Europe than in the US, the immediate response of net migration in Europe has increased over time JEL Classification Numbers: F31, F4, J30

  • Regional Labor Market adjustments in the united states and europe
    Research Papers in Economics, 2014
    Co-Authors: Mai Dao, Davide Furceri, Prakash Loungani
    Abstract:

    We examine patterns of Regional adjustments to shocks in the US during the past 40 years. Using state-level data, we estimate the dynamic response of Regional employment, unemployment, participation rates and net migration to state-relative Labor demand shocks. We find that (i) the long-run effect of a state-specific shock on the state employment level has decreased over time, suggesting less overall net migration in response to a Regional shock, (ii) the role of the participation rate as absorber of Regional shocks has increased, (iii) the response of net migration to Regional shocks is stronger, while that of relative unemployment is weaker during aggregate downturns, and (iv) the change in the response intensity of migration is related to the declining trend in Regional dispersion of Labor Market conditions. Finally, using Regional data for a set of 21 European countries, we show that while the short-term response of participation rates to Labor demand shocks is typically larger in Europe than in the US, the immediate response of net migration in Europe has increased over time.