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2023 | OriginalPaper | Buchkapitel

3. Schätzung

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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.
Zurück zum Zitat IPCC (2012) Renewable energy sources and climate change mitigation – special report of the intergovernmental panel on climate change. Cambridge University Press, New York IPCC (2012) Renewable energy sources and climate change mitigation – special report of the intergovernmental panel on climate change. Cambridge University Press, New York
2.
Zurück zum Zitat Duchin F, Steenge AE (2007) Mathematical models in input–output economics. Rensselaer Polytechnic Institute, working papers in economics, 0703 Duchin F, Steenge AE (2007) Mathematical models in input–output economics. Rensselaer Polytechnic Institute, working papers in economics, 0703
4.
Zurück zum Zitat Rey SJ (2000) Integrated regional econometric + input–output modeling: issues and opportunities. Pap Reg Sci 79:271–292CrossRef Rey SJ (2000) Integrated regional econometric + input–output modeling: issues and opportunities. Pap Reg Sci 79:271–292CrossRef
5.
Zurück zum Zitat Eurostat (2008) Eurostat manual of supply, use and input–output tables Eurostat (2008) Eurostat manual of supply, use and input–output tables
6.
Zurück zum Zitat Gillingham K, Kotchen M, Rapson D, Wagner G (2013) The rebound effect is over-played. Nature 493:475–476CrossRef Gillingham K, Kotchen M, Rapson D, Wagner G (2013) The rebound effect is over-played. Nature 493:475–476CrossRef
7.
Zurück zum Zitat 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. 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.
8.
Zurück zum Zitat Chan, N. H., & Palma, W. (1998). State space modeling of long-memory processes. Annals of Statistics, 26(2), 719–740.CrossRef Chan, N. H., & Palma, W. (1998). State space modeling of long-memory processes. Annals of Statistics, 26(2), 719–740.CrossRef
9.
Zurück zum Zitat Grassi, S., & Magistris, P. S. (2014). When long memory meets the Kalman filter: A comparative study. Computational Statistics & Data Analysis, 76, 301–319.CrossRef Grassi, S., & Magistris, P. S. (2014). When long memory meets the Kalman filter: A comparative study. Computational Statistics & Data Analysis, 76, 301–319.CrossRef
10.
Zurück zum Zitat 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. 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.
11.
Zurück zum Zitat Brooks, S. P. (1998). Markov Chain Monte Carlo method and its application. The Statistician, 47(1), 69–100. Brooks, S. P. (1998). Markov Chain Monte Carlo method and its application. The Statistician, 47(1), 69–100.
12.
Zurück zum Zitat 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.CrossRef 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.CrossRef
13.
Zurück zum Zitat 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.CrossRef 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.CrossRef
14.
Zurück zum Zitat Stein, C. 1973. Estimation of the mean of a multivariate normal distribution. In Proceedings of Prague Symposium of Asymptotic Statistics (pp. 345–381). Stein, C. 1973. Estimation of the mean of a multivariate normal distribution. In Proceedings of Prague Symposium of Asymptotic Statistics (pp. 345–381).
15.
Zurück zum Zitat Chaturvedi, A., and S. Mishra. 2019. Generalized Bayes estimation of spatial autoregressive models. Statistics in Transition New Series 20 (2): 15–32.CrossRef Chaturvedi, A., and S. Mishra. 2019. Generalized Bayes estimation of spatial autoregressive models. Statistics in Transition New Series 20 (2): 15–32.CrossRef
16.
Zurück zum Zitat Diebold, F. X., & Li, C. (2006). Forecasting the term structure of government bond yields. Journal of Econometrics, 130(2), 337–364.CrossRef Diebold, F. X., & Li, C. (2006). Forecasting the term structure of government bond yields. Journal of Econometrics, 130(2), 337–364.CrossRef
17.
Zurück zum Zitat 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.CrossRef 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.CrossRef
18.
Zurück zum Zitat Ludvigson, S. C., & Ng, S. (2007). The empirical risk–return relation: A factor analysis approach. Journal of Financial Economics, 83(1), 171–222.CrossRef Ludvigson, S. C., & Ng, S. (2007). The empirical risk–return relation: A factor analysis approach. Journal of Financial Economics, 83(1), 171–222.CrossRef
19.
Zurück zum Zitat 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. 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.
20.
Zurück zum Zitat 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.CrossRef 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.CrossRef
21.
Zurück zum Zitat 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.CrossRef 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.CrossRef
22.
Zurück zum Zitat Eickmeier, S. (2007). Business cycle transmission from the us to Germany – A structural factor approach. European Economic Review, 51(3), 521–551.CrossRef Eickmeier, S. (2007). Business cycle transmission from the us to Germany – A structural factor approach. European Economic Review, 51(3), 521–551.CrossRef
23.
Zurück zum Zitat 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.CrossRef 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.CrossRef
24.
Zurück zum Zitat 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.CrossRef 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.CrossRef
25.
Zurück zum Zitat Stock, J. H., & Watson, M. W. (2002b). Macroeconomic forecasting using diffusion indexes. Journal of Business and Economic Statistics, 20(2), 147–162.CrossRef Stock, J. H., & Watson, M. W. (2002b). Macroeconomic forecasting using diffusion indexes. Journal of Business and Economic Statistics, 20(2), 147–162.CrossRef
26.
Zurück zum Zitat 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. 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.
27.
Zurück zum Zitat 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.CrossRef 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.CrossRef
28.
Zurück zum Zitat IHS Global Inc. (2015a). EViews 9 command and programming reference. Irvine: IHS Global Inc. IHS Global Inc. (2015a). EViews 9 command and programming reference. Irvine: IHS Global Inc.
29.
Zurück zum Zitat IHS Global Inc. (2015b). EViews 9 object reference. Irvine: IHS Global Inc. IHS Global Inc. (2015b). EViews 9 object reference. Irvine: IHS Global Inc.
30.
Zurück zum Zitat IHS Global Inc. (2015c). EViews 9 user’s guide I. Irvine: IHS Global Inc. IHS Global Inc. (2015c). EViews 9 user’s guide I. Irvine: IHS Global Inc.
31.
Zurück zum Zitat IHS Global Inc. (2015d). EViews 9 user’s guide II. Irvine: IHS Global Inc. IHS Global Inc. (2015d). EViews 9 user’s guide II. Irvine: IHS Global Inc.
32.
Zurück zum Zitat Stoica, P., & Jansson, M. (2009). On maximum likelihood estimation in factor analysis – An algebraic derivation. Signal Processing, 89(6), 1260–1262.CrossRef Stoica, P., & Jansson, M. (2009). On maximum likelihood estimation in factor analysis – An algebraic derivation. Signal Processing, 89(6), 1260–1262.CrossRef
33.
Zurück zum Zitat 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.CrossRef 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.CrossRef
34.
Zurück zum Zitat Bai, J., & Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70(1), 191–221.CrossRef Bai, J., & Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70(1), 191–221.CrossRef
35.
Zurück zum Zitat Onatski, A. (2010). Determining the number of factors from empirical distribution of eigenvalues. The Review of Economics and Statistics, 92(4), 1004–1016.CrossRef Onatski, A. (2010). Determining the number of factors from empirical distribution of eigenvalues. The Review of Economics and Statistics, 92(4), 1004–1016.CrossRef
36.
Zurück zum Zitat Ahn, S. C., & Horenstein, A. R. (2013). Eigenvalue ratio test for the number of factors. Econometrica, 81(3), 1203–1227.CrossRef Ahn, S. C., & Horenstein, A. R. (2013). Eigenvalue ratio test for the number of factors. Econometrica, 81(3), 1203–1227.CrossRef
37.
Zurück zum Zitat Hamilton, J. D. (1994). Time series analysis. New Jersey: Princeton University Press.CrossRef Hamilton, J. D. (1994). Time series analysis. New Jersey: Princeton University Press.CrossRef
38.
Zurück zum Zitat Banbura, M., Giannone, D., & Reichlin, L. (2011). Nowcasting. In M. Clements & D. Hendry (Eds.), The Oxford handbook of economic forecasting. Oxford: Oxford University Press. Banbura, M., Giannone, D., & Reichlin, L. (2011). Nowcasting. In M. Clements & D. Hendry (Eds.), The Oxford handbook of economic forecasting. Oxford: Oxford University Press.
39.
Zurück zum Zitat Giannone, D., Reichlin, L., & Small, D. (2008). Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55(4), 665–676.CrossRef Giannone, D., Reichlin, L., & Small, D. (2008). Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55(4), 665–676.CrossRef
40.
Zurück zum Zitat Hardy, M. R. (2001). A regime-switching model of long-term stock returns. North American Actuarial Journal, 5(2), 41–53.CrossRef Hardy, M. R. (2001). A regime-switching model of long-term stock returns. North American Actuarial Journal, 5(2), 41–53.CrossRef
41.
Zurück zum Zitat Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2), 223–236.CrossRef Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2), 223–236.CrossRef
42.
Zurück zum Zitat Samanidou E, Zschischang E, Stauffer D, Lux T (2007) Agent-based models of financial markets. Rep Prog Phys 70:409–450CrossRef Samanidou E, Zschischang E, Stauffer D, Lux T (2007) Agent-based models of financial markets. Rep Prog Phys 70:409–450CrossRef
43.
Zurück zum Zitat 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 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
44.
Zurück zum Zitat LeBaron B (2000) Agent-based computational finance: suggested readings and early research. J Econ Dyn Control 24(5):679–702CrossRef LeBaron B (2000) Agent-based computational finance: suggested readings and early research. J Econ Dyn Control 24(5):679–702CrossRef
45.
Zurück zum Zitat Franke R, Westerhoff F (2011) Estimation of a structural stochastic volatility model of asset pricing. Comput Econ 38(1):53–83CrossRef Franke R, Westerhoff F (2011) Estimation of a structural stochastic volatility model of asset pricing. Comput Econ 38(1):53–83CrossRef
46.
Zurück zum Zitat 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 37:1–19CrossRef 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 37:1–19CrossRef
47.
Zurück zum Zitat Franke R, Westerhoff F (2012) Structural stochastic volatility in asset pricing dynamics: estimation and model contest. J Econ Dyn Control 36(8):1193–1211CrossRef Franke R, Westerhoff F (2012) Structural stochastic volatility in asset pricing dynamics: estimation and model contest. J Econ Dyn Control 36(8):1193–1211CrossRef
48.
Zurück zum Zitat 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. 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.
51.
Zurück zum Zitat 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.CrossRef 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.CrossRef
52.
Zurück zum Zitat 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.CrossRef 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.CrossRef
53.
Zurück zum Zitat 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.CrossRef 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.CrossRef
54.
Zurück zum Zitat 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.CrossRef 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.CrossRef
55.
Zurück zum Zitat 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.CrossRef 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.CrossRef
56.
Zurück zum Zitat 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.CrossRef 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.CrossRef
57.
Zurück zum Zitat 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.CrossRef 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.CrossRef
58.
Zurück zum Zitat 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. 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.
59.
Zurück zum Zitat Harris, H. & Sollis, R. (2003). Applied Time Series Modeling and Forecasting. Wiley, West Essex. Harris, H. & Sollis, R. (2003). Applied Time Series Modeling and Forecasting. Wiley, West Essex.
60.
Zurück zum Zitat Hansen PR, Lunde A, Nason JM (2011) The model confidence set. Econometrica 79(2):453–497CrossRef Hansen PR, Lunde A, Nason JM (2011) The model confidence set. Econometrica 79(2):453–497CrossRef
61.
Zurück zum Zitat Lu YK, Perron P (2010) Modeling and forecasting stock return volatility using a random level shift model. J Empir Financ 17:138–156CrossRef Lu YK, Perron P (2010) Modeling and forecasting stock return volatility using a random level shift model. J Empir Financ 17:138–156CrossRef
62.
Zurück zum Zitat Li Y, Perron P, Xu J (2017) Modeling exchange rate volatility with random level shifts. Appl Econ 49:2579–2589CrossRef Li Y, Perron P, Xu J (2017) Modeling exchange rate volatility with random level shifts. Appl Econ 49:2579–2589CrossRef
63.
Zurück zum Zitat Xu J, Perron P (2014) Forecasting return volatility: Level shifts with varying jump probability and mean reversion. Int J Forecast 30:449–463CrossRef Xu J, Perron P (2014) Forecasting return volatility: Level shifts with varying jump probability and mean reversion. Int J Forecast 30:449–463CrossRef
64.
Zurück zum Zitat 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 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
65.
Zurück zum Zitat 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 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
66.
Zurück zum Zitat Klaassen F (2002) Improving GARCH volatility forecasts with regime-switching GARCH. Empirical Economics 27(2):363–394CrossRef Klaassen F (2002) Improving GARCH volatility forecasts with regime-switching GARCH. Empirical Economics 27(2):363–394CrossRef
67.
Zurück zum Zitat Morana C, Beltratti A (2004) Structural change and long-range dependence in volatility of exchange rates: either, neither or both. J Empir Financ 11:629–658CrossRef Morana C, Beltratti A (2004) Structural change and long-range dependence in volatility of exchange rates: either, neither or both. J Empir Financ 11:629–658CrossRef
69.
Zurück zum Zitat He, K., X. Zhang, S. Ren and J. Sun. 2015. Deep residual learning for image recognition. arXiv:abs/1512.03385 [CoRR]. He, K., X. Zhang, S. Ren and J. Sun. 2015. Deep residual learning for image recognition. arXiv:abs/1512.03385 [CoRR].
70.
Zurück zum Zitat 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.CrossRef 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.CrossRef
71.
Zurück zum Zitat Bergstra, J., R. Bardenet, Y. Bengio, and B. Kégl. 2011. Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems 24: 2546–2554. Bergstra, J., R. Bardenet, Y. Bengio, and B. Kégl. 2011. Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems 24: 2546–2554.
72.
Zurück zum Zitat 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. 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.
73.
Zurück zum Zitat 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. 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.
74.
Zurück zum Zitat Tsay, R.S. 2005. Analysis of financial time series, 3rd ed. Hoboken: Wiley.CrossRef Tsay, R.S. 2005. Analysis of financial time series, 3rd ed. Hoboken: Wiley.CrossRef
75.
Zurück zum Zitat Campbell, J.Y., A.W. Lo, and C.A. MacKinlay. 1996. The econometrics of financial markets, 2nd ed. Princeton: Princeton University Press. Campbell, J.Y., A.W. Lo, and C.A. MacKinlay. 1996. The econometrics of financial markets, 2nd ed. Princeton: Princeton University Press.
76.
Zurück zum Zitat Sharpe, W.F. 1964. Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance 19 (3): 425. Sharpe, W.F. 1964. Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance 19 (3): 425.
77.
Zurück zum Zitat 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.CrossRef 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.CrossRef
78.
Zurück zum Zitat Berkowitz, J., Christoffersen, P., & Pelletier, D. (2011). Evaluating value-at-risk models with desk-level data. Management Science, 57(12), 2213–2227CrossRef Berkowitz, J., Christoffersen, P., & Pelletier, D. (2011). Evaluating value-at-risk models with desk-level data. Management Science, 57(12), 2213–2227CrossRef
79.
Zurück zum Zitat Angelidis, T., Benos, A., & Degiannakis, S. (2004). The use of GARCH models in VaR estimation. In Statistical methodology (vol. 1, pp. 105–128). Angelidis, T., Benos, A., & Degiannakis, S. (2004). The use of GARCH models in VaR estimation. In Statistical methodology (vol. 1, pp. 105–128).
80.
Zurück zum Zitat 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), 436–452CrossRef 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), 436–452CrossRef
81.
Zurück zum Zitat Engle, R. (2004). Risk and volatility: Econometric models and financial practice. The American Economic Review, 94(3), 405–420CrossRef Engle, R. (2004). Risk and volatility: Econometric models and financial practice. The American Economic Review, 94(3), 405–420CrossRef
82.
Zurück zum Zitat Billio, M., & Pelizzon, L. (2000). Value-at-risk: a multivariate switching regime approach. Journal of Empirical Finance, 7(5), 531–554CrossRef Billio, M., & Pelizzon, L. (2000). Value-at-risk: a multivariate switching regime approach. Journal of Empirical Finance, 7(5), 531–554CrossRef
84.
Zurück zum Zitat 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. 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.
85.
Zurück zum Zitat Lundbergh, S., & Teräsvirta, T. (2002). Evaluating GARCH models. Journal of Econometrics, 110(2), 417–435CrossRef Lundbergh, S., & Teräsvirta, T. (2002). Evaluating GARCH models. Journal of Econometrics, 110(2), 417–435CrossRef
86.
Zurück zum Zitat Goodman AC, Thibodeau TG (1998) Housing market segmentation. Journal of Housing Economics 7(2):121–143CrossRef Goodman AC, Thibodeau TG (1998) Housing market segmentation. Journal of Housing Economics 7(2):121–143CrossRef
87.
Zurück zum Zitat Goodman AC, Thibodeau TG (2003) Housing market segmentation and hedonic prediction accuracy. Journal of Housing Economics 12(3):181–201CrossRef Goodman AC, Thibodeau TG (2003) Housing market segmentation and hedonic prediction accuracy. Journal of Housing Economics 12(3):181–201CrossRef
88.
Zurück zum Zitat Brasington D, Haurin DR (2006) Educational outcomes and house values: A test of the value added approach. Journal of Regional Science 46(2):245–268CrossRef Brasington D, Haurin DR (2006) Educational outcomes and house values: A test of the value added approach. Journal of Regional Science 46(2):245–268CrossRef
89.
Zurück zum Zitat 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 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
90.
Zurück zum Zitat Burstein L (1980) The analysis of multilevel data in educational research and evaluation. Review of Research in Education 8(1):158–233CrossRef Burstein L (1980) The analysis of multilevel data in educational research and evaluation. Review of Research in Education 8(1):158–233CrossRef
91.
Zurück zum Zitat 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):65–108CrossRef 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):65–108CrossRef
92.
Zurück zum Zitat Hill PW, Rowe KJ (1996) Multilevel modelling in school effectiveness research. School Effectiveness and School Improvement 7(1):1–34CrossRef Hill PW, Rowe KJ (1996) Multilevel modelling in school effectiveness research. School Effectiveness and School Improvement 7(1):1–34CrossRef
93.
Zurück zum Zitat 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):379–400CrossRef 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):379–400CrossRef
94.
Zurück zum Zitat 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):1081–1099CrossRef 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):1081–1099CrossRef
95.
Zurück zum Zitat LeSage JP, Pace RK (2008) Spatial econometric modeling of origin-destination flows. J Reg Sci 5:941.967 LeSage JP, Pace RK (2008) Spatial econometric modeling of origin-destination flows. J Reg Sci 5:941.967
96.
Zurück zum Zitat LeSage J, Pace RK (2009) Introduction to spatial econometrics. Chapman and Hall/CRC, LondonCrossRef LeSage J, Pace RK (2009) Introduction to spatial econometrics. Chapman and Hall/CRC, LondonCrossRef
97.
Zurück zum Zitat 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 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
98.
Zurück zum Zitat LeSage JP (1997) Bayesian estimation of spatial autoregressive models. Int Reg Sci Rev 1–2:113.129 LeSage JP (1997) Bayesian estimation of spatial autoregressive models. Int Reg Sci Rev 1–2:113.129
99.
Zurück zum Zitat Fischer MM, LeSage JP (2020) Network dependence in multi-indexed data on international trade flows. J Spat Econ 1:4 Fischer MM, LeSage JP (2020) Network dependence in multi-indexed data on international trade flows. J Spat Econ 1:4
100.
Zurück zum Zitat Guastella G, van Oort F (2015) Regional heterogeneity and interregional research Spillovers in European innovation: modelling and policy implications. Reg Stud 49(11):1–16CrossRef Guastella G, van Oort F (2015) Regional heterogeneity and interregional research Spillovers in European innovation: modelling and policy implications. Reg Stud 49(11):1–16CrossRef
101.
Zurück zum Zitat Audretsch DB, Feldman MP (1996) R&D spillovers and the geography of innovation and production. Am Econ Rev 86:630–640 Audretsch DB, Feldman MP (1996) R&D spillovers and the geography of innovation and production. Am Econ Rev 86:630–640
102.
Zurück zum Zitat 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 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
104.
Zurück zum Zitat Anselin L, Rey SJ (2014) Modern spatial econometrics in practice. GeoDa Press, Chicago Anselin L, Rey SJ (2014) Modern spatial econometrics in practice. GeoDa Press, Chicago
Metadaten
Titel
Schätzung
verfasst von
Vaibhavi Aher
Copyright-Jahr
2023
DOI
https://doi.org/10.1007/978-3-658-39275-8_3