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Schätzung

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Statistische und mathematische Methoden in der Wirtschaft

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Einleitung des Herausgebers

Bei der Schätzung handelt es sich um eine Analyse, die zur Interpretation der Daten für die weitere Verwendung genutzt wird. Zur Schätzung eines Wirtschaftsmodells werden ökonometrische Instrumente verwendet. Dieses Kapitel befasst sich mit verschiedenen Arbeiten zur Schätzung eines Wirtschaftsmodells. Es behandelt einige Theorien und die Anwendung in der Praxis.

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Literatur

  1. IPCC (2012) Renewable energy sources and climate change mitigation – special report of the intergovernmental panel on climate change. Cambridge University Press, New York

    Google Scholar 

  2. Duchin F, Steenge AE (2007) Mathematical models in input–output economics. Rensselaer Polytechnic Institute, working papers in economics, 0703

    Google Scholar 

  3. West GR (1995) Comparison of input–output, input–output + econometric and computable general equilibrium impact models at the regional level. Econ Syst Res 7:209–227. https://doi.org/10.1080/09535319500000021

    Article  Google Scholar 

  4. Rey SJ (2000) Integrated regional econometric + input–output modeling: issues and opportunities. Pap Reg Sci 79:271–292

    Article  Google Scholar 

  5. Eurostat (2008) Eurostat manual of supply, use and input–output tables

    Google Scholar 

  6. Gillingham K, Kotchen M, Rapson D, Wagner G (2013) The rebound effect is over-played. Nature 493:475–476

    Article  Google Scholar 

  7. Lindsey, D. E., Orphanides, A., & Rasche, R. H. (2005). The Reform of October 1979: How it happened and why. Finance and Economics Discussion Series Division of Research & Statistics, and Monetary affairs Federal Reserve Board, Washington.

    Google Scholar 

  8. Chan, N. H., & Palma, W. (1998). State space modeling of long-memory processes. Annals of Statistics, 26(2), 719–740.

    Article  Google Scholar 

  9. Grassi, S., & Magistris, P. S. (2014). When long memory meets the Kalman filter: A comparative study. Computational Statistics & Data Analysis, 76, 301–319.

    Article  Google Scholar 

  10. Scollnik, D. P. M. (1996). An introduction to Markov Chain Monte Carlo methods and their actuarial applications. Proceedings of the Casualty Actuarial Society, 83, 114–165.

    Google Scholar 

  11. Brooks, S. P. (1998). Markov Chain Monte Carlo method and its application. The Statistician, 47(1), 69–100.

    Google Scholar 

  12. Besag, J. (2004). An introduction to Markov Chain Monte Carlo methods. In M. Johnson, S. P. Khudanpur, M. Ostendorf, & R. Rosenfeld (Eds.), Mathematical foundations of speech and language processing (pp. 247–270). New York: Springer.

    Chapter  Google Scholar 

  13. Han, X., and Lung-fei Lee. 2013. Bayesian estimation and model selection for spatial Durbin error model with finite distributed lags. Regional Science and Urban Economics 43: 816–837.

    Article  Google Scholar 

  14. Stein, C. 1973. Estimation of the mean of a multivariate normal distribution. In Proceedings of Prague Symposium of Asymptotic Statistics (pp. 345–381).

    Google Scholar 

  15. Chaturvedi, A., and S. Mishra. 2019. Generalized Bayes estimation of spatial autoregressive models. Statistics in Transition New Series 20 (2): 15–32.

    Article  Google Scholar 

  16. Diebold, F. X., & Li, C. (2006). Forecasting the term structure of government bond yields. Journal of Econometrics, 130(2), 337–364.

    Article  Google Scholar 

  17. Diebold, F. X., Rudebusch, G. D., & Aruoba, S. B. (2006). The macroeconomy and the yield curve: A dynamic latent factor approach. Journal of Econometrics, 131(1–2), 309–338.

    Article  Google Scholar 

  18. Ludvigson, S. C., & Ng, S. (2007). The empirical risk–return relation: A factor analysis approach. Journal of Financial Economics, 83(1), 171–222.

    Article  Google Scholar 

  19. Bernanke, B. S., Boivin, J., & Eliasz, P. (2005). Measuring the effects of monetary policy: A factor-augmented vector autoregressive (FAVAR) approach. The Quarterly Journal of Economics, 120(1), 387–422.

    Google Scholar 

  20. Boivin, J., Giannoni, M. P., & Mihov, I. (2009). Sticky prices and monetary policy: Evidence from disaggregated us data. The American Economic Review, 99(1), 350–384.

    Article  Google Scholar 

  21. Forni, M., & Reichlin, L. (1998). Let’s get real: A factor analytical approach to disaggregated business cycle dynamics. The Review of Economic Studies, 65(3), 453–473.

    Article  Google Scholar 

  22. Eickmeier, S. (2007). Business cycle transmission from the us to Germany – A structural factor approach. European Economic Review, 51(3), 521–551.

    Article  Google Scholar 

  23. Ritschl, A., Sarferaz, S., & Uebele, M. (2016). The U.S. business cycle, 1867–2006: A dynamic factor approach. The Review of Economics and Statistics, 98(1), 159–172.

    Article  Google Scholar 

  24. Stock, J. H., & Watson, M. W. (2002a). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97(460), 1167–1179.

    Article  Google Scholar 

  25. Stock, J. H., & Watson, M. W. (2002b). Macroeconomic forecasting using diffusion indexes. Journal of Business and Economic Statistics, 20(2), 147–162.

    Article  Google Scholar 

  26. Banbura, M., Giannone, D., Modugno, M., & Reichlin, L. (2012). Now-casting and the real-time data flow. In G. Elliott & A. Timmermann (Eds.), Handbook of economic forecasting (Vol. 2A). Amsterdam: Elsevier-North Holland.

    Google Scholar 

  27. Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1), 180–205.

    Article  Google Scholar 

  28. IHS Global Inc. (2015a). EViews 9 command and programming reference. Irvine: IHS Global Inc.

    Google Scholar 

  29. IHS Global Inc. (2015b). EViews 9 object reference. Irvine: IHS Global Inc.

    Google Scholar 

  30. IHS Global Inc. (2015c). EViews 9 user’s guide I. Irvine: IHS Global Inc.

    Google Scholar 

  31. IHS Global Inc. (2015d). EViews 9 user’s guide II. Irvine: IHS Global Inc.

    Google Scholar 

  32. Stoica, P., & Jansson, M. (2009). On maximum likelihood estimation in factor analysis – An algebraic derivation. Signal Processing, 89(6), 1260–1262.

    Article  Google Scholar 

  33. Doz, C., Giannone, D., & Reichlin, L. (2012). A quasi-maximum likelihood approach for large, approximate dynamic factor models. The Review of Economics and Statistics, 94(4), 1014–1024.

    Article  Google Scholar 

  34. Bai, J., & Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70(1), 191–221.

    Article  Google Scholar 

  35. Onatski, A. (2010). Determining the number of factors from empirical distribution of eigenvalues. The Review of Economics and Statistics, 92(4), 1004–1016.

    Article  Google Scholar 

  36. Ahn, S. C., & Horenstein, A. R. (2013). Eigenvalue ratio test for the number of factors. Econometrica, 81(3), 1203–1227.

    Article  Google Scholar 

  37. Hamilton, J. D. (1994). Time series analysis. New Jersey: Princeton University Press.

    Book  Google Scholar 

  38. Banbura, M., Giannone, D., & Reichlin, L. (2011). Nowcasting. In M. Clements & D. Hendry (Eds.), The Oxford handbook of economic forecasting. Oxford: Oxford University Press.

    Google Scholar 

  39. Giannone, D., Reichlin, L., & Small, D. (2008). Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55(4), 665–676.

    Article  Google Scholar 

  40. Hardy, M. R. (2001). A regime-switching model of long-term stock returns. North American Actuarial Journal, 5(2), 41–53.

    Article  Google Scholar 

  41. Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2), 223–236.

    Article  Google Scholar 

  42. Samanidou E, Zschischang E, Stauffer D, Lux T (2007) Agent-based models of financial markets. Rep Prog Phys 70:409–450

    Article  Google Scholar 

  43. Lux T (2009) Stochastic behavioral asset-pricing models and the stylized facts. In: Hens T, Schenk-Hoppé KR (eds) Handbook of financial markets: dynamics and evolution, handbooks in finance. North-Holland, San Diego, pp 161–215

    Google Scholar 

  44. LeBaron B (2000) Agent-based computational finance: suggested readings and early research. J Econ Dyn Control 24(5):679–702

    Article  Google Scholar 

  45. Franke R, Westerhoff F (2011) Estimation of a structural stochastic volatility model of asset pricing. Comput Econ 38(1):53–83

    Article  Google Scholar 

  46. Ghonghadze J, Lux T (2016) Bringing an elementary agent-based model to the data: estimation via GMM and an application to forecasting of asset price volatility. J Empir Finance 37:1–19

    Article  Google Scholar 

  47. Franke R, Westerhoff F (2012) Structural stochastic volatility in asset pricing dynamics: estimation and model contest. J Econ Dyn Control 36(8):1193–1211

    Article  Google Scholar 

  48. Salisu, A.A., K. Isah, and I. Ademuyiwa. 2017a. Testing for asymmetries in the predictive model of oil-inflation. Economics Bulletin 37 (03): 1797–1804.

    Google Scholar 

  49. Salisu, A.A., I. Ademuyiwa, and K. Isah. 2018. Revisiting the forecasting accuracy of Phillips curve: the role of oil price. Energy Economics. https://doi.org/10.1016/j.eneco.2018.01.018.

  50. Salisu, A.A., and K.O. Isah. 2018. Predicting US inflation: Evidence from a new approach. Economic Modelling. https://doi.org/10.1016/j.econmod.2017.12.008.

  51. Bjørnland, H.C. 2009. Oil price shocks and stock market booms in an oil exporting country. Scottish Journal of Political Economy 56 (2): 232–254.

    Article  Google Scholar 

  52. Filis, G., S. Degiannakis, and C. Floros. 2011. Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries. International Review of Financial Analysis 20: 152–164.

    Article  Google Scholar 

  53. Aloui, C., D.K. Nguyen, and H. Njeh. 2012. Assessing the impacts of oil price fluctuations on stock returns in emerging markets. Economic Modelling 29: 2686–2695.

    Article  Google Scholar 

  54. Guesmi, K., and S. Fattoum. 2014. Return and volatility transmission between oil prices and oil-exporting and oil-importing countries. Economic Modelling 38: 305–310.

    Article  Google Scholar 

  55. Salisu, A.A., O.K. Isah, J.O. Oyewole, and O.L. Akanni. 2017b. Modelling oil price-inflation nexus: The role of asymmetries. Energy 125: 97–106.

    Article  Google Scholar 

  56. Salisu, A.A., and K.O. Isah. 2017. Revisiting the oil price and stock market nexus: A nonlinear Panel ARDL approach. Economic Modelling 66 (C): 258–271.

    Article  Google Scholar 

  57. Hooker, M.A. 2002. Are oil shocks inflationary? Asymmetric and nonlinear specifications versus changes in regime. Journal of Money, Credit, Bank 34 (2): 540–561.

    Article  Google Scholar 

  58. Luitel, H. S., & Mahar, G. J. (2015). A short note on the application of chow test of structural break in U.S. GDP. International Business Research, 8(10), 112–116.

    Google Scholar 

  59. Harris, H. & Sollis, R. (2003). Applied Time Series Modeling and Forecasting. Wiley, West Essex.

    Google Scholar 

  60. Hansen PR, Lunde A, Nason JM (2011) The model confidence set. Econometrica 79(2):453–497

    Article  Google Scholar 

  61. Lu YK, Perron P (2010) Modeling and forecasting stock return volatility using a random level shift model. J Empir Financ 17:138–156

    Article  Google Scholar 

  62. Li Y, Perron P, Xu J (2017) Modeling exchange rate volatility with random level shifts. Appl Econ 49:2579–2589

    Article  Google Scholar 

  63. Xu J, Perron P (2014) Forecasting return volatility: Level shifts with varying jump probability and mean reversion. Int J Forecast 30:449–463

    Article  Google Scholar 

  64. Varneskov RT, Perron P (2018) Combining long memory and level shifts in modeling and forecasting the volatility of asset returns. Quant Econ 18(3):371–393

    Google Scholar 

  65. Beine M, Laurent S (2000) Structural change and long memory in volatility: new evidence from daily exchange rates, Econometric Society World Congress 2000 Contributed Papers 0312, Econometric Society

    Google Scholar 

  66. Klaassen F (2002) Improving GARCH volatility forecasts with regime-switching GARCH. Empirical Economics 27(2):363–394

    Article  Google Scholar 

  67. Morana C, Beltratti A (2004) Structural change and long-range dependence in volatility of exchange rates: either, neither or both. J Empir Financ 11:629–658

    Article  Google Scholar 

  68. Habibnia, A. (2016). Essays in high-dimensional nonlinear time series analysis. London, UK: London School of Economics and Political Science (LSE). http://etheses.lse.ac.uk/id/eprint/3485.

  69. He, K., X. Zhang, S. Ren and J. Sun. 2015. Deep residual learning for image recognition. arXiv:abs/1512.03385 [CoRR].

    Google Scholar 

  70. Hutter, F., H.H. Hoos and K. Leyton-Brown. 2011. Sequential model-based optimization for general algorithm configuration. In: International Conference on Learning and Intelligent Optimization, 507–523. Berlin, Heidelberg: Springer.

    Chapter  Google Scholar 

  71. Bergstra, J., R. Bardenet, Y. Bengio, and B. Kégl. 2011. Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems 24: 2546–2554.

    Google Scholar 

  72. Bergstra, J., D. Yamins, and D. Cox. 2013. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In: International Conference on Machine Learning, 115–123. PMLR.

    Google Scholar 

  73. Snoek, J., H. Larochelle, and R.P. Adams. 2012. Practical Bayesian optimization of machine learning algorithms. Advances in Neural Information Processing Systems 25: 2960–2968.

    Google Scholar 

  74. Tsay, R.S. 2005. Analysis of financial time series, 3rd ed. Hoboken: Wiley.

    Book  Google Scholar 

  75. Campbell, J.Y., A.W. Lo, and C.A. MacKinlay. 1996. The econometrics of financial markets, 2nd ed. Princeton: Princeton University Press.

    Google Scholar 

  76. Sharpe, W.F. 1964. Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance 19 (3): 425.

    Google Scholar 

  77. Lintner, J. 1965. The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The Review of Economics and Statistics 47 (1): 13.

    Article  Google Scholar 

  78. Berkowitz, J., Christoffersen, P., & Pelletier, D. (2011). Evaluating value-at-risk models with desk-level data. Management Science, 57(12), 2213–2227

    Article  Google Scholar 

  79. Angelidis, T., Benos, A., & Degiannakis, S. (2004). The use of GARCH models in VaR estimation. In Statistical methodology (vol. 1, pp. 105–128).

    Google Scholar 

  80. Degiannakis, S., Floros, C., & Livada, A. (2012). Evaluating value-at-risk models before and after the financial crisis of 2008: International evidence. Managerial Finance, 38(4), 436–452

    Article  Google Scholar 

  81. Engle, R. (2004). Risk and volatility: Econometric models and financial practice. The American Economic Review, 94(3), 405–420

    Article  Google Scholar 

  82. Billio, M., & Pelizzon, L. (2000). Value-at-risk: a multivariate switching regime approach. Journal of Empirical Finance, 7(5), 531–554

    Article  Google Scholar 

  83. Basel Committee on Banking Supervision. (1995). An internal model-based approach to market risk capital requirements. Basel, Switzerland. Retrieved from https://www.bis.org/publ/bcbsc224.pdf.

    Google Scholar 

  84. Committee of European Securities Regulators. (2010). CESR’s guidelines on risk measurement and the calculation of global exposure and counterparty risk for UCITS. CESR/10-788.

    Google Scholar 

  85. Lundbergh, S., & Teräsvirta, T. (2002). Evaluating GARCH models. Journal of Econometrics, 110(2), 417–435

    Article  Google Scholar 

  86. Goodman AC, Thibodeau TG (1998) Housing market segmentation. Journal of Housing Economics 7(2):121–143

    Article  Google Scholar 

  87. Goodman AC, Thibodeau TG (2003) Housing market segmentation and hedonic prediction accuracy. Journal of Housing Economics 12(3):181–201

    Article  Google Scholar 

  88. Brasington D, Haurin DR (2006) Educational outcomes and house values: A test of the value added approach. Journal of Regional Science 46(2):245–268

    Article  Google Scholar 

  89. Gibson C (1998) Population of the 100 largest cities and other urban places in the United States: 1790–1990. US Bureau of the Census, Washington DC

    Google Scholar 

  90. Burstein L (1980) The analysis of multilevel data in educational research and evaluation. Review of Research in Education 8(1):158–233

    Article  Google Scholar 

  91. Bryk AS, Raudenbush SW (1988) Toward a more appropriate conceptualization of research on school effects: A three-level hierarchical linear model. American Journal of Education 97(1):65–108

    Article  Google Scholar 

  92. Hill PW, Rowe KJ (1996) Multilevel modelling in school effectiveness research. School Effectiveness and School Improvement 7(1):1–34

    Article  Google Scholar 

  93. Wong A, van Baal PHM, Boshuizen HC, Polder JJ (2011) Exploring the influence of proximity to death on disease-specific hospital expenditures: a carpaccio of red herrings. Health Econ 20(4):379–400

    Article  Google Scholar 

  94. Deb P, Munkin MK, Trivedi PK (2006) Bayesian analysis of the two-part model with endogeneity: application to health care expenditure. J Appl Econ 21(7):1081–1099

    Article  Google Scholar 

  95. LeSage JP, Pace RK (2008) Spatial econometric modeling of origin-destination flows. J Reg Sci 5:941.967

    Google Scholar 

  96. LeSage J, Pace RK (2009) Introduction to spatial econometrics. Chapman and Hall/CRC, London

    Book  Google Scholar 

  97. Martin RJ (1992) Approximations to the determinant term in Gaussian maximum likelihood estimation of some spatial models. Communications in statistics – theory and methods 1:189.205

    Google Scholar 

  98. LeSage JP (1997) Bayesian estimation of spatial autoregressive models. Int Reg Sci Rev 1–2:113.129

    Google Scholar 

  99. Fischer MM, LeSage JP (2020) Network dependence in multi-indexed data on international trade flows. J Spat Econ 1:4

    Google Scholar 

  100. Guastella G, van Oort F (2015) Regional heterogeneity and interregional research Spillovers in European innovation: modelling and policy implications. Reg Stud 49(11):1–16

    Article  Google Scholar 

  101. Audretsch DB, Feldman MP (1996) R&D spillovers and the geography of innovation and production. Am Econ Rev 86:630–640

    Google Scholar 

  102. Furková A (2016) The Innovative clusters in the EU: the sensitivity analysis of the spatial weight matrix construction. In: Quantitative methods in economics: multiple criteria decision making XVIII. International scientific conference Vrátna, pp 106–113

    Google Scholar 

  103. Geniaux G, Martinetti D (2018) A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models. Reg Sci Urban Econ 72:74–85. https://doi.org/10.1016/j.regsciurbeco.2017.04.001

    Article  Google Scholar 

  104. Anselin L, Rey SJ (2014) Modern spatial econometrics in practice. GeoDa Press, Chicago

    Google Scholar 

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  1. Nashik, Indien

    Vaibhavi Aher

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    Vaibhavi Aher

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Aher, V. (2023). Schätzung. In: Aher, V. (eds) Statistische und mathematische Methoden in der Wirtschaft. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-39275-8_3

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