Accessible Unlicensed Requires Authentication Published by De Gruyter May 26, 2018

Forecasting the unemployment rate over districts with the use of distinct methods

Marcin Wozniak

Abstract

Interdependencies among neighboring regions appear to be important in forming the shape of local labor markets. Nevertheless, only a few studies exist which have applied spatial models to forecast over small spatial units such as cities, districts or counties. The majority of predictions are developed with quarterly or yearly time series for a country or at regional level. The paper presents the above phenomena and deals with the problem of simultaneous forecasting of the unemployment rate over 35 poviats (districts and cities) in one of the Polish provinces. Two extremely different models with spatial dependencies were developed and estimated in this paper: the Spatial Vector Autoregressions (SpVAR) and the Spatial Artificial Neural Network (SpANN). The 13-month out-of-sample forecast is based on high frequency, raw, monthly panel data extracted from 31 local labor offices. The procedure worked out here allows comparing the forecasting performance of spatial models with their non-spatial and seasonal equivalents. The inclusion of a spatial component into the models significantly improves the accuracy of forecasts; however, the overall performance of SpVAR is 30% better than SpANN.

References

Adhikari, R., and R. Agrawal. 2012. “Forecasting Strong Seasonal Time Series with Artificial Neural Networks.” Journal of Scientific and Industrial Research 71: 657–666.Search in Google Scholar

Anselin, L. 1988. Spatial Econometrics: Methods and Models. Springer Science & Business Media.Search in Google Scholar

Arbia, G., and B. Fingleton. 2008. “New Spatial Econometric Techniques and Applications in Regional Science.” Papers in Regional Science 87: 311–317.Search in Google Scholar

Arellano, M., and S. Bond. 1991. “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations.” Review of Economic Studies 58 (2): 277–297.Search in Google Scholar

Baltagi, B. H., B. Fingleton, and A. Pirotte. 2014. “Estimating and Forecasting with a Dynamic Spatial Panel Data Model.” Oxford Bulletin of Economics and Statistics 76 (1): 112–138.Search in Google Scholar

Beck, N., K. Gleditsch, and K. Beardsly. 2006. “Space is more than Geography: Using Spatial Econometrics in the Study of Political Economy.” International Studies 50: 27–44.Search in Google Scholar

Beenstock, M., and D. Felsenstein. 2007. “Spatial Vector Autoregressions.” Spatial Economic Analysis 2 (2): 167–196.Search in Google Scholar

Blanchard, O., and P. Diamond. 1990. “The Cyclical Behovior of the Gross Flows of U.S. Workers.” Brookings Papers on Economic Activity 21 (2): 85–156.Search in Google Scholar

Blanchard, O., and L. F. Katz. 1992. “Regional Evolutions.” Brookings Papers on Economic Activity 19921: 1–75.Search in Google Scholar

Bottou, L., O. Chapelle, D. DeCoste, and J. Weston. 2007. Large-Scale Kernel Machines. Cambridge, Massachusetts: MIT Press.Search in Google Scholar

Canova, F., and M. Ciccarelli. 2013. “Panel Vector Autoregressive Models A Survey.” ECB Working Paper Series 15: 1–53. .Search in Google Scholar

Chen, Y. 2012. “On the Four Types of Weight Functions for Spatial Contiguity Matrix.” Letters in Spatial and Resource Sciences 5 (2): 65–72.Search in Google Scholar

Cleveland, R. B., W. S. Cleveland, J. E. McRae, and I. Terpenning. 1990. “STL: A Seasonal-Trend Decomposition Procedure Based on Loess.” Journal of Official Statistics 6 (1): 3–73.Search in Google Scholar

Cliff, A., and J. K. Ord. 1973. Spatial Autocorrelation. London: Pion.Search in Google Scholar

Corrado, L., and B. Fingleton. 2012. “Where is the Economics in Spatial Econometrics.” Journal of Regional Science 52 (2): 210–239.Search in Google Scholar

Dewachter, H., R. Houssa, and P. Toffano. 2012. “Spatial Propagation of Macroeconomic Shocks in Europe.” Review of World Economics 148 (2): 377–402.Search in Google Scholar

Feng, C., H. Wang, N. Lu, T. Chen, H. He, Y. Lu, and X. Tu. 2014. “Log-Transformation and its Implications for Data Analysis.” Shanghai Arch Psychiatry 26 (2): 105–109.Search in Google Scholar

Fotheringham, S., and P. Rogerson. 2009. The SAGE Handbook of Spatial Analysis. London: SAGE Publications Ltd.Search in Google Scholar

Giacomini, R., and C. Granger. 2004. “Aggregation of Space-Time Processes.” Journal of Econometrics 118 (1–2): 7–26.Search in Google Scholar

Girardin, E., and K. A. Kholodilin. 2011. “How Helpful are Spatial Effects in Forecasting the Growth of Chinese Provinces?” Journal of Forecast 30: 622–643.Search in Google Scholar

Gnana, K., and S. N. Deepa. 2013. “Review on Methods to Fix Number of Hidden Neurons in Neural Networks.” Mathematical Problems in Engineering 2013, Article ID 425740, 11 pages. doi:10.1155/2013/425740.Search in Google Scholar

Griffith, D. A. 2003 Spatial Autocorrelation and Spatial Filtering. Berlin: Springer.Search in Google Scholar

Gurney, K. N. 2006. “Neural Networks for Perceptual Processing: From Simulation Tools to Theories.” Philosophical Transactions of the Royal Society London B 362: 339–353.Search in Google Scholar

Hampel, K., E. Kunz, M. Schanne, G. Norbert, R. Wapler, and A. Weyh. 2007. “Regional Employment Forecasts with Spatial Interdependencies.” Discussion Paper 02/2007, IAB.Search in Google Scholar

Holly, S., M. Pesaran, and T. Yamagata. 2011. “The Spatial and Temporal Diffusion of House Prices in the UK.” Journal of Urban Economics 69 (1): 2–23Search in Google Scholar

Hong, J., S. Lee, J. Lim, and J. Kim. 2013. “Application of Spatial Econometrics Analysis for Traffic Accident Prediction Models in Urban Areas.” Proceedings of the Eastern Asia Studies 9: 390–397.Search in Google Scholar

Hyndman, R. 2015. “High-dimensional Autocovariance Matrices and Optimal Linear Prediction.” Electronic Journal of Statistics 9 (1): 792–796. DOI: doi:10.1214/14-EJS953.Search in Google Scholar

Igel, Ch., and M. Hüsken. 2000. “Improving the Rprop Learning Algorithm.” Second International Symposium on Neural Computation, pp. 115–121, ICSC Academic Press.Search in Google Scholar

Igel, Ch, and M. Hüsken. 2003. “Empirical Evaluation of the Improved Rprop Learning Algorithm.” Neurocomputing 50: 105–123. DOI: 10.1016/S0925-2312(01)00700-7.Search in Google Scholar

James, G., D. Witten, T. Hastie, and R. Tibshirani. 2013. An Introduction to Statistical Learning with Applications in R. New York: Springer.Search in Google Scholar

Kaastra, I., and M. Boyd. 1996. “Designing a Neural Network for Forecasting Financial and Economic Time Series.” Neurocomputing 10 (3): 215–236.Search in Google Scholar

Khalik Salman, A., L. Arnesson, A. Sörensson, and G. Shukur. 2010. “Estimating International Tourism Demand for Selected Regions in Sweden and Norway with Iterative Seemingly Unrelated Regressions (ISUR).” Scandinavian Journal of Hospitality and Tourism 10 (4): 395–410.Search in Google Scholar

Kholodin, K., B. Siliverstos, and S. Kooths. (2007). A Dynamic Panel Data Approach to the Forecasting of the GDP of German Lander. German Institute for Economic Research.Search in Google Scholar

Kriesel, D. 2007. A Brief Introduction to Neural Networks, .Search in Google Scholar

Kuethe, T. H., and V. Pede. 2009. Regional Housing Price Cycles: A Spatio-Temporal Analysis Using Us State Level, (09-04). .Search in Google Scholar

Lee, L. F., and J. Yu. 2010. “Estimation of Spatial Autoregressive Panel Data Models with Fixed Effects.” Journal of Econometrics 154: 165–185.Search in Google Scholar

Lee, L., and J. Yu. 2015. “Identification of Spatial Durbin Panel Models.” Journal of Applied Econometrics. 31: 133–162. DOI: 10.1002/jae.2450.Search in Google Scholar

Lehmann, R., and K. Wohlrabe. 2015. “Regional Economic Forecasting: State-of-the-Art Methodology and Future Challenges.” Economics and Business Letters 3 (4): 218–231.Search in Google Scholar

LeSage, J. P. 1999. “The Theory and Practice of Spatial Econometrics.” International Journal of Forecasting 2 (2): 245–246.Search in Google Scholar

Levin, A., Chien-Fu Lin, and Chia-Shang Chu. 2002. “Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties.” Journal of Econometrics 108 (1): 1–24.Search in Google Scholar

Lo, S., and S. Andrews. 2015. “To transform or not to transform: using generalized linear mixed models to analyse reaction time data.” Frontiers in Psychology 6: 1171. DOI: 10.3389/fpsyg.2015.01171.Search in Google Scholar

Lütkepohl, H., and F. Xu. 2012. “The Role of the Log Transformation in Forecasting Economic Variables.” Empirical Economics 42 (3): 619–638.Search in Google Scholar

May, B., N. Korda, A. Lee, and D. Leslie. 2012. “Optimistic Bayesian Sampling in Contextual-Bandit Problems.” The Journal of Machine Learning Research 13 (1): 2069–2106.Search in Google Scholar

Monteiro, J. 2009. “Pollution Havens: a Spatial Panel VAR Approach.” The European Trade Study Group, 1–26. Retrieved from: .Search in Google Scholar

Moran, P. A. P. 1948. “The Interpretation of Statistical Maps.” Journal of the Royal Statistical Society. Series B (Methodological) 10 (2): 243–251.Search in Google Scholar

Moran, P. A. P. 1950. “Notes on Continuous Stochastic Phenomena.” Biometrika 37: 17–23.Search in Google Scholar

Mutl, J. 2009. “Panel VAR Models with Spatial Dependence.” Institute for Advanced Studies, Economics Series 237: 1–38.Search in Google Scholar

Oancea, B., and S. Ciucu. 2013. “Time Series Forecasting using Neural Networks.” Proceedings of the CKS 2013 International Conference.Search in Google Scholar

O’Hara, R. B., and D. J. Kotze. 2010. “Do Not Log-Transform Count Data.” Methods in Ecology and Evolution 1: 118–122.Search in Google Scholar

Patuelli, R., and M. Mayor. 2012. “Short-Run Regional Forecasts: Spatial Models Through Varying Cross-Sectional and Temporal Dimensions.” Quaderni DSE Working Paper No. 835. .Search in Google Scholar

Patuelli, R., S. Longhi, A. Reggiani, and P. Nijkamp. 2005. Forecasting Regional Employment in Germany by Means of Neural Networks and Genetic Algorithms, (0511002), 1–23. .Search in Google Scholar

Pesaran, M. H. 2007. “A Simple Panel Unit Root Test in the Presence of Cross-Section Dependence.” Journal of Applied Econometrics 22: 265–312.Search in Google Scholar

Riedmiller, M. 1994. “Advanced Supervised Learning in Multi-Layer Perceptrons – from Backpropagation to Adaptive Learning Algorithms.” International Journal of Computer Standards and Interfaces 16: 265–278.Search in Google Scholar

Riedmiller, M., and H. Braun. 1993. “A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm.” EEE International Conference on Neural Networks, 586–599.Search in Google Scholar

Schanne, N., R. Wappler, and A. Weyh. 2010. “Regional Unemployment Forecasts with Spatial Interdependencies.” International Journal of Forecasting 26 (4): 908–926.Search in Google Scholar

Schurmann, T., and P. Grassberger. 1996. “Entropy Estimation of Symbol Sequences.” Chaos 6: 41–427.Search in Google Scholar

Seymen, A. 2008. “A Critical Note on the Forecast Error Variance Decomposition.” SSRN Electronic Journal 1–17. .Search in Google Scholar

Shimer, R. 2005. “The Cyclical Behavior of Equilibrium Unemployment and Vacancies.” American Economic Review 95 (1): 25–49.Search in Google Scholar

Sims, Ch. A., ed. 1994. Advances in Econometrics. Cambridge Books, Cambridge University Press.Search in Google Scholar

Stoyan, D., G. Upton, and B. Fingleton. 1986. Spatial Data Analysis, vol. 1: Point Pattern and Quantitative Data. Chichester/New York/Brisbane/Toronto/Singapore: J. Wiley & Sons, 1985.Search in Google Scholar

Telser, L. G. 1964. “Iterative Estimation of a Set of Linear Regression Equations.” Journal of American Statistical Association 59: 845–862.Search in Google Scholar

Vega, S., and J. P. Elhorst. 2014. “Modelling Regional Labour Market Dynamics in Space and Time.” Papers in Regional Science 93 (4): 819–842.Search in Google Scholar

Viton, P. A. 2010. “Notes on Spatial Econometric Models.” City and Regional Planning 870 (3): 9–10.Search in Google Scholar

Wozniak, M. 2015. “Can Stochastic Equilibrium Search Model Fit Transition Economies?.” Acta Oeconomica 65 (4): 567–591.Search in Google Scholar

Xu, W., Z. Li, and Q. Chen. 2012. Forecasting the Unemployment Rate by Neural Networks Using Search Engine Query Data, Hawaii International Conference on System Sciences.Search in Google Scholar

Zhang, Z. 2016. “Neural Networks: Further Insights into Error Function, Generalized Weights and Others.” Annals of Translational Medicine 4 (16): 300.Search in Google Scholar

Zhang, G., B. Eddy Patuwo, and Michael Y. Hu. 1998. “Forecasting with Artificial Neural Networks: The State of the Art.” International Journal of Forecasting 14 (1): 35–62.Search in Google Scholar

Zhang, Y, Z. Zhang, and D. Wei. 2012. “Centrality Measure in Weighted Networks Based on an Amoeboid Algorithm.” Journal of Information & Computational Science 9 (2): 369–376.Search in Google Scholar

Published Online: 2018-05-26

©2020 Walter de Gruyter GmbH, Berlin/Boston