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4. Ausblick

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Einleitung des Herausgebers

Prognosen helfen zu wissen, was in der Zukunft passieren wird. Ökonometrische Prognosen unterscheiden sich von den üblichen Prognosemodellen. Ökonometrische Prognosemodelle sind Systeme von Beziehungen zwischen Variablen wie BSP, Inflation, Wechselkursen usw. Dieses Kapitel befasst sich mit verschiedenen Arbeiten zur Schätzung eines Wirtschaftsmodells. Es behandelt einige Theorien sowie die reale Anwendung von ökonometrischen Prognosemodellen.

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Literatur
1.
Zurück zum Zitat Kauppi H, Saikkonen P (2008) Predicting U.S. recessions with dynamic binary response models. Rev Econ Stat 90(4):777–791CrossRef Kauppi H, Saikkonen P (2008) Predicting U.S. recessions with dynamic binary response models. Rev Econ Stat 90(4):777–791CrossRef
2.
Zurück zum Zitat Estrella A, Mishkin FS (1995) Predicting U.S. recessions: financial variables as leading indicators. Working paper 5379, National Bureau of Economic Research Estrella A, Mishkin FS (1995) Predicting U.S. recessions: financial variables as leading indicators. Working paper 5379, National Bureau of Economic Research
3.
Zurück zum Zitat Estrella A, Mishkin FS (1998) Predicting U.S. recessions: financial variables as leading indicators. Rev Econ Stat 80(1):45–61CrossRef Estrella A, Mishkin FS (1998) Predicting U.S. recessions: financial variables as leading indicators. Rev Econ Stat 80(1):45–61CrossRef
4.
Zurück zum Zitat Dueker M (1997) Strengthening the case for the yield curve as a predictor of U.S. recessions. Rev Fed Reserve Bank St Louis 79(2):41–51 Dueker M (1997) Strengthening the case for the yield curve as a predictor of U.S. recessions. Rev Fed Reserve Bank St Louis 79(2):41–51
5.
Zurück zum Zitat Park BU, Simar L, Zelenyuk V (2017) Nonparametric estimation of dynamic discrete choice models for time series data. Comput Stat Data Anal 108:97–120CrossRef Park BU, Simar L, Zelenyuk V (2017) Nonparametric estimation of dynamic discrete choice models for time series data. Comput Stat Data Anal 108:97–120CrossRef
6.
Zurück zum Zitat Fan J, Heckman NE, Wand MP (1995) Local polynomial kernel regression for generalized linear models and quasi-likelihood functions. J Am Stat Assoc 90:141–150CrossRef Fan J, Heckman NE, Wand MP (1995) Local polynomial kernel regression for generalized linear models and quasi-likelihood functions. J Am Stat Assoc 90:141–150CrossRef
7.
Zurück zum Zitat Florio A (2004) The Asymmetric Effects of Monetary Policy. J Econ Surv 18:409–426CrossRef Florio A (2004) The Asymmetric Effects of Monetary Policy. J Econ Surv 18:409–426CrossRef
8.
Zurück zum Zitat Christensen JHE, Diebold FX, Rudebusch GD (2009) An arbitrage-free generalized Nelson-Siegel term structure model. Econ J 12:33–64 Christensen JHE, Diebold FX, Rudebusch GD (2009) An arbitrage-free generalized Nelson-Siegel term structure model. Econ J 12:33–64
9.
Zurück zum Zitat Christensen JHE, Diebold FX, Rudebusch GD (2011) The affine arbitrage-free class of Nelson-Siegel term structure models. J Econom 164:4–20 Christensen JHE, Diebold FX, Rudebusch GD (2011) The affine arbitrage-free class of Nelson-Siegel term structure models. J Econom 164:4–20
10.
Zurück zum Zitat Ullah W, Matsuda Y, Tsukuda Y (2015) Generalized Nelson-Siegel term structure model: do the second slope and curvature factors improve the in-sample fit and out-of-sample forecast? J Appl Stat 42(4):876–904 Ullah W, Matsuda Y, Tsukuda Y (2015) Generalized Nelson-Siegel term structure model: do the second slope and curvature factors improve the in-sample fit and out-of-sample forecast? J Appl Stat 42(4):876–904
11.
Zurück zum Zitat Nelson CR, Siegel AF (1987) Parsimonious modeling of yield curves. J Bus 60:473–489CrossRef Nelson CR, Siegel AF (1987) Parsimonious modeling of yield curves. J Bus 60:473–489CrossRef
12.
Zurück zum Zitat Gribisch B, Hartkopf JP, Liesenfeld R (2020) Factor state-space models for high-dimensional realized covariance matrices of asset returns. J Empir Financ 55(1):1–20CrossRef Gribisch B, Hartkopf JP, Liesenfeld R (2020) Factor state-space models for high-dimensional realized covariance matrices of asset returns. J Empir Financ 55(1):1–20CrossRef
13.
Zurück zum Zitat Philipov A, Glickman ME (2006) Factor stochastic volatility via Wishart processes. Econom Rev 25(2–3):311–334CrossRef Philipov A, Glickman ME (2006) Factor stochastic volatility via Wishart processes. Econom Rev 25(2–3):311–334CrossRef
14.
Zurück zum Zitat Golosnoy V, Gribisch B, Liesenfeld R (2012) The conditional autoregressive Wishart model for multivariate stock market volatility. J Econom 167(1):211–223CrossRef Golosnoy V, Gribisch B, Liesenfeld R (2012) The conditional autoregressive Wishart model for multivariate stock market volatility. J Econom 167(1):211–223CrossRef
15.
Zurück zum Zitat Noureldin D, Shephard N, Sheppard K (2012) Multivariate high-frequency-based volatility (HEAVY) models. J Appl Econom 27(6):907–933CrossRef Noureldin D, Shephard N, Sheppard K (2012) Multivariate high-frequency-based volatility (HEAVY) models. J Appl Econom 27(6):907–933CrossRef
16.
Zurück zum Zitat Bauwens L, Braione M, Storti G (2016) Multiplicative conditional correlation models for realized covariance matrices. CORE working paper (2016/41) Bauwens L, Braione M, Storti G (2016) Multiplicative conditional correlation models for realized covariance matrices. CORE working paper (2016/41)
17.
Zurück zum Zitat Windle J, Carvalho M (2014) A tractable state-space model for symmetric positive-definite matrices. Bayesian Anal 9(4):759–792CrossRef Windle J, Carvalho M (2014) A tractable state-space model for symmetric positive-definite matrices. Bayesian Anal 9(4):759–792CrossRef
18.
Zurück zum Zitat Moura GV, Noriller MR (2019) Maximum likelihood estimation of a TVP-VAR. Econ Lett 174:78–83CrossRef Moura GV, Noriller MR (2019) Maximum likelihood estimation of a TVP-VAR. Econ Lett 174:78–83CrossRef
19.
Zurück zum Zitat Fan J, Furger A, Xiu D (2016) Incorporating global industrial classification standard into portfolio allocation: a simple factor-based large covariance matrix estimator with high-frequency data. J Bus Econ Stat 34(4):489–503CrossRef Fan J, Furger A, Xiu D (2016) Incorporating global industrial classification standard into portfolio allocation: a simple factor-based large covariance matrix estimator with high-frequency data. J Bus Econ Stat 34(4):489–503CrossRef
20.
Zurück zum Zitat Aït-Sahalia Y, Xiu D (2017) Using principal component analysis to estimate a high-dimensional factor model with high-frequency data. J Econom 201(2):384–399CrossRef Aït-Sahalia Y, Xiu D (2017) Using principal component analysis to estimate a high-dimensional factor model with high-frequency data. J Econom 201(2):384–399CrossRef
21.
Zurück zum Zitat Brito D, Medeiros MC, Ribeiro R (2018) Forecasting large realized covariance matrices: The benefits of factor models and shrinkage. SSRN Working Paper Brito D, Medeiros MC, Ribeiro R (2018) Forecasting large realized covariance matrices: The benefits of factor models and shrinkage. SSRN Working Paper
22.
Zurück zum Zitat Sheppard K, Xu W (2019) Factor high-frequency based volatility (HEAVY) models. J Financ Econom 17(1):33–65 Sheppard K, Xu W (2019) Factor high-frequency based volatility (HEAVY) models. J Financ Econom 17(1):33–65
23.
Zurück zum Zitat Morgan JP (1996) Riskmetrics, 4th edn. techreport, J. P. Morgan, New York Morgan JP (1996) Riskmetrics, 4th edn. techreport, J. P. Morgan, New York
24.
Zurück zum Zitat Lunde A, Shephard N, Sheppard K (2016) Econometric analysis of vast covariance matrices using composite realized kernels and their application to portfolio choice. J Bus Econ Stat 34(4):504–518CrossRef Lunde A, Shephard N, Sheppard K (2016) Econometric analysis of vast covariance matrices using composite realized kernels and their application to portfolio choice. J Bus Econ Stat 34(4):504–518CrossRef
25.
Zurück zum Zitat Moura GV, Santos A, Ruiz E (2020) Comparing high-dimensional conditional covariance matrices: implications for portfolio selection. J Bank Finance 118:1–13CrossRef Moura GV, Santos A, Ruiz E (2020) Comparing high-dimensional conditional covariance matrices: implications for portfolio selection. J Bank Finance 118:1–13CrossRef
26.
Zurück zum Zitat Da Z, Engelberg J, Gao P (2011) In search of attention. J Finance 66:1461–1499 Da Z, Engelberg J, Gao P (2011) In search of attention. J Finance 66:1461–1499
27.
Zurück zum Zitat Bank M, Larch M, Peter G (2011) Google search volume and its influence on liquidity and returns of German stocks. Financ Mark Portf Manag 25:239CrossRef Bank M, Larch M, Peter G (2011) Google search volume and its influence on liquidity and returns of German stocks. Financ Mark Portf Manag 25:239CrossRef
28.
Zurück zum Zitat Han L, Xu Y, Yin L (2018) Does investor attention matter? The attention-return relationship in FX markets. Econ Model 68:660–664CrossRef Han L, Xu Y, Yin L (2018) Does investor attention matter? The attention-return relationship in FX markets. Econ Model 68:660–664CrossRef
29.
Zurück zum Zitat Curme C, Preis T, Stanley HE, Moat HS (2014) Quantifying the semantics of search behavior before stock market moves. PNAS 111:11600–11605CrossRef Curme C, Preis T, Stanley HE, Moat HS (2014) Quantifying the semantics of search behavior before stock market moves. PNAS 111:11600–11605CrossRef
30.
Zurück zum Zitat Wheelock D, Wohar M (2009) Can the term spread predict output growth and recessions? A survey of the literature. Fed Reserve Bank St. Louis Rev 91(5):419–440 Wheelock D, Wohar M (2009) Can the term spread predict output growth and recessions? A survey of the literature. Fed Reserve Bank St. Louis Rev 91(5):419–440
31.
Zurück zum Zitat Chinn M, Kucko K (2015) The predictive power of the yield curve across countries and time. Int Finance 18(2):129–156CrossRef Chinn M, Kucko K (2015) The predictive power of the yield curve across countries and time. Int Finance 18(2):129–156CrossRef
33.
Zurück zum Zitat Andrés J, López-Salido JD, Nelson E (2004) Tobin’s imperfect asset substitution in optimizing general equilibrium. J Money Credit Bank 36(4):665–690CrossRef Andrés J, López-Salido JD, Nelson E (2004) Tobin’s imperfect asset substitution in optimizing general equilibrium. J Money Credit Bank 36(4):665–690CrossRef
34.
Zurück zum Zitat De Graeve F, Emiris M, Wouters R (2009) A structural decomposition of the US yield curve. J Monet Econ 56(4):545–559CrossRef De Graeve F, Emiris M, Wouters R (2009) A structural decomposition of the US yield curve. J Monet Econ 56(4):545–559CrossRef
35.
Zurück zum Zitat Doh T (2008) Estimating a structural macro finance model of the term structure. Federal Reserve Bank of Kansas City, Kansas Doh T (2008) Estimating a structural macro finance model of the term structure. Federal Reserve Bank of Kansas City, Kansas
36.
Zurück zum Zitat Amisano G, Tristani O (2008) A DSGE model of the term structure with regime shifts. European Central Bank, Germany Amisano G, Tristani O (2008) A DSGE model of the term structure with regime shifts. European Central Bank, Germany
37.
Zurück zum Zitat Zagaglia P (2013) Forecasting long-term interest rates with a general-equilibrium model of the Euro area: What role for liquidity services of bonds? Asia-Pac Financ Mark 20:383–430CrossRef Zagaglia P (2013) Forecasting long-term interest rates with a general-equilibrium model of the Euro area: What role for liquidity services of bonds? Asia-Pac Financ Mark 20:383–430CrossRef
38.
Zurück zum Zitat Balcilar M, Gupta R, Kotze K (2015) Forecasting macroeconomic data for an emerging market with a nonlinear DSGE model. Econ Model 44(1):215–228CrossRef Balcilar M, Gupta R, Kotze K (2015) Forecasting macroeconomic data for an emerging market with a nonlinear DSGE model. Econ Model 44(1):215–228CrossRef
39.
Zurück zum Zitat Balcilar M, Gupta R, Kotze K (2017) Forecasting South African macroeconomic variables with a Markov-switching small open-economy dynamic stochastic general equilibrium model. Empir Econ 53(1):117–135CrossRef Balcilar M, Gupta R, Kotze K (2017) Forecasting South African macroeconomic variables with a Markov-switching small open-economy dynamic stochastic general equilibrium model. Empir Econ 53(1):117–135CrossRef
40.
Zurück zum Zitat Cutler J (2001) Core inflation in the UK. MPC Unit Discussion Paper Cutler J (2001) Core inflation in the UK. MPC Unit Discussion Paper
41.
Zurück zum Zitat Blinder A (1997) Measuring short-run inflation for central bankers: a commentary. Federal Reserve Bank of St. Louis Review, 5/6, 79(3): 157–60 Blinder A (1997) Measuring short-run inflation for central bankers: a commentary. Federal Reserve Bank of St. Louis Review, 5/6, 79(3): 157–60
42.
Zurück zum Zitat Mankikar A, Paisley Jo (2002) What do measures of core inflation really tell us? Bank of England Quarterly Bulletin, winter: 373–83 Mankikar A, Paisley Jo (2002) What do measures of core inflation really tell us? Bank of England Quarterly Bulletin, winter: 373–83
43.
Zurück zum Zitat Bryan M, Cecchetti S (1993) The consumer price index as a measure of inflation. NBER Working Paper No 4505 Bryan M, Cecchetti S (1993) The consumer price index as a measure of inflation. NBER Working Paper No 4505
44.
Zurück zum Zitat Fan Y, Feng W (2005) Measure of core inflation and the effectiveness of macro controls: an empirical analysis of China for 1995–2004. Manage World 5:6–13 Fan Y, Feng W (2005) Measure of core inflation and the effectiveness of macro controls: an empirical analysis of China for 1995–2004. Manage World 5:6–13
45.
Zurück zum Zitat Gadzinski G, Orlandi F (2004) Inflation persistence in the European Union, the Euro area, and the United States. European Central Bank. Working Paper No 414 Gadzinski G, Orlandi F (2004) Inflation persistence in the European Union, the Euro area, and the United States. European Central Bank. Working Paper No 414
46.
Zurück zum Zitat Dias D, Marques C (2005) Using mean reversion as a measure of persistence. European Central Bank. Working Paper Series No 450 Dias D, Marques C (2005) Using mean reversion as a measure of persistence. European Central Bank. Working Paper Series No 450
47.
Zurück zum Zitat Bilke L, Stracca L (2007) A persistence-weighted measure of core inflation in the Euro area. Econ Model 24(6):1032–1047CrossRef Bilke L, Stracca L (2007) A persistence-weighted measure of core inflation in the Euro area. Econ Model 24(6):1032–1047CrossRef
48.
Zurück zum Zitat Zhang C (2011) Inflation persistence, inflation expectations, and monetary policy in China. Econ Model 28(1–2):622–629CrossRef Zhang C (2011) Inflation persistence, inflation expectations, and monetary policy in China. Econ Model 28(1–2):622–629CrossRef
49.
Zurück zum Zitat Taylor J (2000) Low inflation, pass-through, and the pricing power of firms. Eur Econ Rev 44(7):1389–1408CrossRef Taylor J (2000) Low inflation, pass-through, and the pricing power of firms. Eur Econ Rev 44(7):1389–1408CrossRef
50.
Zurück zum Zitat Willis J (2003) Implications of structural changes in the US economy for pricing behavior and inflation dynamics. Econ Rev Federal Reserve Bank of Kansas City Q1:5–27 Willis J (2003) Implications of structural changes in the US economy for pricing behavior and inflation dynamics. Econ Rev Federal Reserve Bank of Kansas City Q1:5–27
51.
Zurück zum Zitat Perron P (1990) Testing for a unit root in a time series with a changing mean. J Bus Econ Stat 8(2):153–162 Perron P (1990) Testing for a unit root in a time series with a changing mean. J Bus Econ Stat 8(2):153–162
52.
Zurück zum Zitat Mariano, R.S., and S. Ozmucur. 2015. High-mixed-frequency dynamic latent factor forecasting models for GDP in the Philippines. Estudios De Economia Aplicada. 33 (2):451–462. Mariano, R.S., and S. Ozmucur. 2015. High-mixed-frequency dynamic latent factor forecasting models for GDP in the Philippines. Estudios De Economia Aplicada. 33 (2):451–462.
53.
Zurück zum Zitat Mariano, R.S., and S. Ozmucur. 2018. High-mixed-frequency forecasting models for GDP and inflation. In Global Economic Modeling – A Volume in Honor of Lawrence Klein, World Scientific Publishing Co, ed. P. Pauly, 2–29. Pte. Ltd. Mariano, R.S., and S. Ozmucur. 2018. High-mixed-frequency forecasting models for GDP and inflation. In Global Economic Modeling – A Volume in Honor of Lawrence Klein, World Scientific Publishing Co, ed. P. Pauly, 2–29. Pte. Ltd.
54.
Zurück zum Zitat Mariano, R.S., and S. Ozmucur. 2020b. High-mixed-frequency forecasting Methods in R -with Applications to Philippine GDP and Inflation. In Handbook of Statistics, Vol 42–Financial Macro and Micro Econometrics Using R, ed. C.R. Rao and H.D. Vinod, 185–227. Elsevier. Mariano, R.S., and S. Ozmucur. 2020b. High-mixed-frequency forecasting Methods in R -with Applications to Philippine GDP and Inflation. In Handbook of Statistics, Vol 42–Financial Macro and Micro Econometrics Using R, ed. C.R. Rao and H.D. Vinod, 185–227. Elsevier.
55.
Zurück zum Zitat Mariano, R.S., and D. Preve. 2012. Statistical tests for multiple forecast comparison. Journal of Econometrics. 169 (1):123–130. Mariano, R.S., and D. Preve. 2012. Statistical tests for multiple forecast comparison. Journal of Econometrics. 169 (1):123–130.
56.
Zurück zum Zitat Mariano, R. S., and Ozmucur, S. (2020b). Predictive Performance of Mixed-Frequency Nowcasting and Forecasting Models (with Application to Philippine Inflation and GDP Growth). Penn Institute of Economic Research Working Paper. 20–029 PIER Paper Submission R8_16_20.pdf (upenn.edu). Mariano, R. S., and Ozmucur, S. (2020b). Predictive Performance of Mixed-Frequency Nowcasting and Forecasting Models (with Application to Philippine Inflation and GDP Growth). Penn Institute of Economic Research Working Paper. 20–029 PIER Paper Submission R8_16_20.pdf (upenn.edu).
57.
Zurück zum Zitat Draghi M (2015) Introductory statement to the press conference (with Q&A). Frankfurt am Main Draghi M (2015) Introductory statement to the press conference (with Q&A). Frankfurt am Main
58.
Zurück zum Zitat Ciccarelli M, Osbat C (2017) Low inflation in the euro area: causes and consequences. ECB occasional paper series 181 Ciccarelli M, Osbat C (2017) Low inflation in the euro area: causes and consequences. ECB occasional paper series 181
59.
Zurück zum Zitat Benalal N, Diaz del Hoyo JL, Landau B, Roma M, Skudelny F (2004) To aggregate or not to aggregate? Euro area inflation forecasting. ECB working paper 374 Benalal N, Diaz del Hoyo JL, Landau B, Roma M, Skudelny F (2004) To aggregate or not to aggregate? Euro area inflation forecasting. ECB working paper 374
60.
Zurück zum Zitat Dées S, Güntner J (2017) Forecasting inflation across Euro area countries and sectors: a panel VAR approach. J Forecast 36(4):431–453 Dées S, Güntner J (2017) Forecasting inflation across Euro area countries and sectors: a panel VAR approach. J Forecast 36(4):431–453
61.
Zurück zum Zitat Giannone D, Lenza M, Reichlin L (2019) Money, credit, monetary policy, and the business cycle in the euro area: What has changed since the crisis? Int J Central Bank 15(5):137–173 Giannone D, Lenza M, Reichlin L (2019) Money, credit, monetary policy, and the business cycle in the euro area: What has changed since the crisis? Int J Central Bank 15(5):137–173
62.
Zurück zum Zitat Angeletos GM, Collard F, Dellas H (2020) Business cycle anatomy. Am Econ Rev 110:3030–3070CrossRef Angeletos GM, Collard F, Dellas H (2020) Business cycle anatomy. Am Econ Rev 110:3030–3070CrossRef
64.
Zurück zum Zitat Schumacher C (2007) Forecasting German GDP using alternative factor models based on large datasets. J Forecast 26(4):271–302CrossRef Schumacher C (2007) Forecasting German GDP using alternative factor models based on large datasets. J Forecast 26(4):271–302CrossRef
65.
Zurück zum Zitat Eickmeier S, Ziegler C (2008) How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach. J Forecast 27(3):237–265 Eickmeier S, Ziegler C (2008) How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach. J Forecast 27(3):237–265
66.
Zurück zum Zitat D’Agostino A, Giannone D (2012) Comparing alternative predictors based on large-panel factor models. Oxf Bull Econ Stat 74(2):306–326CrossRef D’Agostino A, Giannone D (2012) Comparing alternative predictors based on large-panel factor models. Oxf Bull Econ Stat 74(2):306–326CrossRef
67.
Zurück zum Zitat Lam C, Yao Q (2012) Factor modeling for high-dimensional time series: inference for the number of factors. Ann Stat 40(2):694–726CrossRef Lam C, Yao Q (2012) Factor modeling for high-dimensional time series: inference for the number of factors. Ann Stat 40(2):694–726CrossRef
68.
Zurück zum Zitat Moench E (2008) Forecasting the yield curve in a data-rich environment: a no-arbitrage factor-augmented VAR approach. J Econ 146(1):26–43CrossRef Moench E (2008) Forecasting the yield curve in a data-rich environment: a no-arbitrage factor-augmented VAR approach. J Econ 146(1):26–43CrossRef
69.
Zurück zum Zitat Gupta R, Kabundi A (2010) The effect of monetary policy on house price inflation: a factor augmented vector autoregression (FAVAR) approach. J Econ Stud 37(6):616–626CrossRef Gupta R, Kabundi A (2010) The effect of monetary policy on house price inflation: a factor augmented vector autoregression (FAVAR) approach. J Econ Stud 37(6):616–626CrossRef
70.
Zurück zum Zitat Zagaglia P (2010) Macroeconomic factors and oil futures prices: a data-rich model. Energy Econ 32(2):409–417CrossRef Zagaglia P (2010) Macroeconomic factors and oil futures prices: a data-rich model. Energy Econ 32(2):409–417CrossRef
71.
Zurück zum Zitat Morana C (2013) Oil price dynamics, macro-finance interactions and the role of financial speculation. J Bank Finance 37(1):206–226CrossRef Morana C (2013) Oil price dynamics, macro-finance interactions and the role of financial speculation. J Bank Finance 37(1):206–226CrossRef
72.
Zurück zum Zitat Ledoit O, Wolf M (2003) Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. J Empir Financ 10:603–621CrossRef Ledoit O, Wolf M (2003) Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. J Empir Financ 10:603–621CrossRef
73.
Zurück zum Zitat Duangnate K (2015) Essays on the dynamics of and forecasting ability within the U.S.A energy sector. Ph.D. Dissertation, Texas A&M University, College Station, TX Duangnate K (2015) Essays on the dynamics of and forecasting ability within the U.S.A energy sector. Ph.D. Dissertation, Texas A&M University, College Station, TX
74.
Zurück zum Zitat Sadorsky P (2006) Modeling and forecasting petroleum futures volatility. Energy Econ 28(4):467–488CrossRef Sadorsky P (2006) Modeling and forecasting petroleum futures volatility. Energy Econ 28(4):467–488CrossRef
76.
Zurück zum Zitat Mishra V, Smyth R (2016) Are natural gas spot and futures prices predictable? Econ Model 54:178–186CrossRef Mishra V, Smyth R (2016) Are natural gas spot and futures prices predictable? Econ Model 54:178–186CrossRef
77.
Zurück zum Zitat Zhu B, Shi X, Chevallier J, Wang P, Wei YM (2016) An adaptive multiscale ensemble learning paradigm for nonstationary and nonlinear energy price time series forecasting. J Forecast 35:633–651CrossRef Zhu B, Shi X, Chevallier J, Wang P, Wei YM (2016) An adaptive multiscale ensemble learning paradigm for nonstationary and nonlinear energy price time series forecasting. J Forecast 35:633–651CrossRef
78.
79.
Zurück zum Zitat García-Martos C, Rodríguez J, Sánchez MJ (2013) Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities. Appl Energy 101:363–375CrossRef García-Martos C, Rodríguez J, Sánchez MJ (2013) Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities. Appl Energy 101:363–375CrossRef
80.
Zurück zum Zitat Ergen I, Rizvanoghlub I (2016) Asymmetric impacts of fundamentals on the natural gas futures volatility: an augmented GARCH approach. Energy Econ 56:64–74CrossRef Ergen I, Rizvanoghlub I (2016) Asymmetric impacts of fundamentals on the natural gas futures volatility: an augmented GARCH approach. Energy Econ 56:64–74CrossRef
81.
Zurück zum Zitat Batten A, Ciner C, Lucey BM (2017) The dynamic linkages between crude oil and natural gas markets. Energy Econ 62:155–170CrossRef Batten A, Ciner C, Lucey BM (2017) The dynamic linkages between crude oil and natural gas markets. Energy Econ 62:155–170CrossRef
82.
Zurück zum Zitat Hansen H, Johansen S (1999) Some tests for parameter constancy in cointegrated VAR-models. Econom J 2(2):306–333CrossRef Hansen H, Johansen S (1999) Some tests for parameter constancy in cointegrated VAR-models. Econom J 2(2):306–333CrossRef
83.
Zurück zum Zitat Dawid AP (1984) Statistical theory: the prequential approach. J R Stat Soc 147(2):278–292 Dawid AP (1984) Statistical theory: the prequential approach. J R Stat Soc 147(2):278–292
84.
Zurück zum Zitat Brier GW (1950) Verification of forecasts expressed in terms of probability. Mon Weather Rev 78:1–3CrossRef Brier GW (1950) Verification of forecasts expressed in terms of probability. Mon Weather Rev 78:1–3CrossRef
86.
Zurück zum Zitat Bloomberg LP (2015) North America natural gas spot prices. Retrieved May 15, 2015 from Bloomberg Professional Service Bloomberg LP (2015) North America natural gas spot prices. Retrieved May 15, 2015 from Bloomberg Professional Service
87.
Zurück zum Zitat Farmer D, Siderowich J (1987) Predicting chaotic time series. Phys Rev Lett 59:845–848CrossRef Farmer D, Siderowich J (1987) Predicting chaotic time series. Phys Rev Lett 59:845–848CrossRef
88.
Zurück zum Zitat Abdel-Aal RE (2008) Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks. Comput Ind Eng 54:903–917CrossRef Abdel-Aal RE (2008) Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks. Comput Ind Eng 54:903–917CrossRef
89.
Zurück zum Zitat Tularam GA, Saeed T (2016) Oil-price forecasting based on various univariate time-series models. Am J Oper Res 6:226–235 Tularam GA, Saeed T (2016) Oil-price forecasting based on various univariate time-series models. Am J Oper Res 6:226–235
90.
Zurück zum Zitat Diebold FX, Mariano RS (1995) Comparing predictive accuracy. J Bus Econ Stat 3:253–263 Diebold FX, Mariano RS (1995) Comparing predictive accuracy. J Bus Econ Stat 3:253–263
91.
Zurück zum Zitat Elstner S, Grimme C, Haskamp U (2013) Das ifo Exportklima – ein Frühindikator für die deutsche Exportprognose. ifo Schnelld 66(4):36–43 Elstner S, Grimme C, Haskamp U (2013) Das ifo Exportklima – ein Frühindikator für die deutsche Exportprognose. ifo Schnelld 66(4):36–43
92.
Zurück zum Zitat Grimme C, Lehmann R (2019) The ifo Export Climate–a leading indicator to forecast German export growth. CESifo Forum 20(4):36–42 Grimme C, Lehmann R (2019) The ifo Export Climate–a leading indicator to forecast German export growth. CESifo Forum 20(4):36–42
93.
Zurück zum Zitat Frale C, Marcellino M, Mazzi GL, Proietti T (2010) Survey data as coincident or leading indicators. J Forecast 29(1–2):109–131CrossRef Frale C, Marcellino M, Mazzi GL, Proietti T (2010) Survey data as coincident or leading indicators. J Forecast 29(1–2):109–131CrossRef
94.
Zurück zum Zitat Camacho M, Pérez-Quirós G, Poncela P (2018) Markov-switching dynamic factor models in real time. Int J Forecast 34(4):598–611CrossRef Camacho M, Pérez-Quirós G, Poncela P (2018) Markov-switching dynamic factor models in real time. Int J Forecast 34(4):598–611CrossRef
95.
Zurück zum Zitat Camacho M, Pérez-Quirós G (2010) Introducing the euro-sting: short-term indicator of euro area growth. J Appl Econom 25(4):663–694CrossRef Camacho M, Pérez-Quirós G (2010) Introducing the euro-sting: short-term indicator of euro area growth. J Appl Econom 25(4):663–694CrossRef
96.
Zurück zum Zitat Kuzin V, Marcellino M, Schumacher C (2011) MIDAS vs. mixed-frequency VAR: nowcasting GDP in the euro area. Int J Forecast 27(2):529–542CrossRef Kuzin V, Marcellino M, Schumacher C (2011) MIDAS vs. mixed-frequency VAR: nowcasting GDP in the euro area. Int J Forecast 27(2):529–542CrossRef
97.
Zurück zum Zitat Armesto MT, Hernandez-Murillo R, Owyang MT, Piger J (2009) Measuring the information content of the beige book: a mixed data sampling approach. J Money Credit Bank 41(1):35–55CrossRef Armesto MT, Hernandez-Murillo R, Owyang MT, Piger J (2009) Measuring the information content of the beige book: a mixed data sampling approach. J Money Credit Bank 41(1):35–55CrossRef
98.
Zurück zum Zitat Clements MP, Galvão AB (2008) Macroeconomic forecasting with mixed-frequency data: forecasting output growth in the United States. J Bus Econ Stat 26(4):546–554CrossRef Clements MP, Galvão AB (2008) Macroeconomic forecasting with mixed-frequency data: forecasting output growth in the United States. J Bus Econ Stat 26(4):546–554CrossRef
99.
Zurück zum Zitat Clements MP, Galvão AB (2009) Forecasting US output growth using leading indicators: an appraisal using MIDAS models. J Appl Econ 24(7):1187–1206CrossRef Clements MP, Galvão AB (2009) Forecasting US output growth using leading indicators: an appraisal using MIDAS models. J Appl Econ 24(7):1187–1206CrossRef
100.
Zurück zum Zitat Galvão AB (2013) Changes in predictive ability with mixed frequency data. Int J Forecast 29(3):395–410CrossRef Galvão AB (2013) Changes in predictive ability with mixed frequency data. Int J Forecast 29(3):395–410CrossRef
101.
Zurück zum Zitat Tay AS (2007) Financial variables as predictors of real output growth. Singapore Management University, School of Economics Working Papers (No:7–2007) Tay AS (2007) Financial variables as predictors of real output growth. Singapore Management University, School of Economics Working Papers (No:7–2007)
102.
Zurück zum Zitat Schumacher C, Breitung J (2008) Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data. Int J Forecast 24(3):386–398CrossRef Schumacher C, Breitung J (2008) Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data. Int J Forecast 24(3):386–398CrossRef
103.
Zurück zum Zitat Ghysels E, Wright JH (2009) Forecasting professional forecasters. J Bus Econ Stat 27(4):504–516CrossRef Ghysels E, Wright JH (2009) Forecasting professional forecasters. J Bus Econ Stat 27(4):504–516CrossRef
104.
Zurück zum Zitat Hamilton JD (2008) Daily monetary policy shocks and new home sales. J Monet Econ 55(7):1171–1190CrossRef Hamilton JD (2008) Daily monetary policy shocks and new home sales. J Monet Econ 55(7):1171–1190CrossRef
105.
Zurück zum Zitat Monteforte L, Moretti G (2013) Real-time forecasts of inflation: the role of financial variables. J Forecast 32(1):51–61CrossRef Monteforte L, Moretti G (2013) Real-time forecasts of inflation: the role of financial variables. J Forecast 32(1):51–61CrossRef
106.
Zurück zum Zitat Andreou E, Ghysels E, Kourtellos A (2013) Should macroeconomic forecasters use daily financial data and how? J Bus Econ Stat 31(2):240–251CrossRef Andreou E, Ghysels E, Kourtellos A (2013) Should macroeconomic forecasters use daily financial data and how? J Bus Econ Stat 31(2):240–251CrossRef
107.
Zurück zum Zitat Stock JH, Watson MW (2003) Forecasting output and inflation: the role of asset prices. J Econ Lit 41:788–829CrossRef Stock JH, Watson MW (2003) Forecasting output and inflation: the role of asset prices. J Econ Lit 41:788–829CrossRef
108.
Zurück zum Zitat Rodriguez-Palenzuela D, Dees S (eds) (2016) The saving and investment task force. Savings and investment behaviour in the euro area. ECB occasional paper series no. 167 Rodriguez-Palenzuela D, Dees S (eds) (2016) The saving and investment task force. Savings and investment behaviour in the euro area. ECB occasional paper series no. 167
109.
Zurück zum Zitat Guérin P, Marcellino M (2013) Markov-switching MIDAS models. J Bus Econ Stat 31(1):45–56CrossRef Guérin P, Marcellino M (2013) Markov-switching MIDAS models. J Bus Econ Stat 31(1):45–56CrossRef
110.
Zurück zum Zitat Hansen BE (1996) Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica 64:413–430CrossRef Hansen BE (1996) Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica 64:413–430CrossRef
111.
Zurück zum Zitat Hansen BE (1999) Threshold effects in non-dynamic panels: estimation, testing, and inference. J Econom 93(2):345–368CrossRef Hansen BE (1999) Threshold effects in non-dynamic panels: estimation, testing, and inference. J Econom 93(2):345–368CrossRef
112.
Zurück zum Zitat Hansen BE (2000) Sample splitting and threshold estimation. Econometrica 68(3):575–603CrossRef Hansen BE (2000) Sample splitting and threshold estimation. Econometrica 68(3):575–603CrossRef
113.
Zurück zum Zitat Hansen BE (2017) Regression kink with an unknown threshold. J Bus Econ Stat 35(2):228–240CrossRef Hansen BE (2017) Regression kink with an unknown threshold. J Bus Econ Stat 35(2):228–240CrossRef
114.
Zurück zum Zitat Seo, B. (2007). Asymptotic distribution of the cointegrating vector estimator in error correction models with conditional heteroskedasticity. Journal of Econometrics, 137, 68–111.CrossRef Seo, B. (2007). Asymptotic distribution of the cointegrating vector estimator in error correction models with conditional heteroskedasticity. Journal of Econometrics, 137, 68–111.CrossRef
115.
Zurück zum Zitat Herwartz, H., & Lütkepohl, H. (2011). Generalized least squares estimation for cointegration parameters under conditional heteroskedasticity. Journal of Time Series Analysis, 32(3), 281–291.CrossRef Herwartz, H., & Lütkepohl, H. (2011). Generalized least squares estimation for cointegration parameters under conditional heteroskedasticity. Journal of Time Series Analysis, 32(3), 281–291.CrossRef
116.
Zurück zum Zitat Koop, G., León-González, R., & Strachan, R. W. (2011). Bayesian inference in the time varying cointegration model. Journal of Econometrics, 165, 210–220.CrossRef Koop, G., León-González, R., & Strachan, R. W. (2011). Bayesian inference in the time varying cointegration model. Journal of Econometrics, 165, 210–220.CrossRef
117.
Zurück zum Zitat Osiewalski, K., & Osiewalski, J. (2013). A long-run relationship between daily prices on two markets: The Bayesian VAR(2)-MSF-SBEKK model. Central European Journal of Economic Modelling and Econometrics, 5(1), 65–83. Osiewalski, K., & Osiewalski, J. (2013). A long-run relationship between daily prices on two markets: The Bayesian VAR(2)-MSF-SBEKK model. Central European Journal of Economic Modelling and Econometrics, 5(1), 65–83.
118.
Zurück zum Zitat Osiewalski, J., & Osiewalski, K. (2016). Hybrid MSV-MGARCH models general remarks and the GMSF-SBEKK specification. Central European Journal of Economic Modelling and Econometrics, 8(4), 241–271. Osiewalski, J., & Osiewalski, K. (2016). Hybrid MSV-MGARCH models general remarks and the GMSF-SBEKK specification. Central European Journal of Economic Modelling and Econometrics, 8(4), 241–271.
119.
Zurück zum Zitat Cavaliere, G., Angelis, L. D., Rahbek, A., & Taylor, A. M. R. (2015). A comparison of sequential and information-based methods for determining the co-integration rank in heteroskedastic VAR models. Oxford Bulletin of Economics and Statistics, 77, 106–128.CrossRef Cavaliere, G., Angelis, L. D., Rahbek, A., & Taylor, A. M. R. (2015). A comparison of sequential and information-based methods for determining the co-integration rank in heteroskedastic VAR models. Oxford Bulletin of Economics and Statistics, 77, 106–128.CrossRef
120.
Zurück zum Zitat Pajor, A., & Wróblewska, J. (2017). VEC-MSF models in Bayesian analysis of short- and long-run relationships. Studies in Nonlinear Dynamics and Econometrics, 21(3), 1–22. Pajor, A., & Wróblewska, J. (2017). VEC-MSF models in Bayesian analysis of short- and long-run relationships. Studies in Nonlinear Dynamics and Econometrics, 21(3), 1–22.
121.
Zurück zum Zitat Cavaliere, G., Angelis, L. D., Rahbek, A., & Taylor, A. M. R. (2018). Determining the cointegration rank in heteroskedastic VAR models of unknown order. Econometric Theory, 34(2), 349–82.CrossRef Cavaliere, G., Angelis, L. D., Rahbek, A., & Taylor, A. M. R. (2018). Determining the cointegration rank in heteroskedastic VAR models of unknown order. Econometric Theory, 34(2), 349–82.CrossRef
122.
Zurück zum Zitat Clark, T. E. (2011). Real-time density forecasts from Bayesian vector autoregressions with stochastic volatility. Journal of Business and Economic Statistics, 29(3), 327–341.CrossRef Clark, T. E. (2011). Real-time density forecasts from Bayesian vector autoregressions with stochastic volatility. Journal of Business and Economic Statistics, 29(3), 327–341.CrossRef
123.
Zurück zum Zitat D’Agostino, A., Gambetti, L., & Giannone, D. (2013). Macroeconomic forecasting and structural change. Journal of Applied Econometrics, 28, 82–101.CrossRef D’Agostino, A., Gambetti, L., & Giannone, D. (2013). Macroeconomic forecasting and structural change. Journal of Applied Econometrics, 28, 82–101.CrossRef
124.
Zurück zum Zitat Rossi, B., & Skhposyan, T. (2014). Evaluating predictive densities of us output growth and inflation in a large macroeconomic data set. International Journal of Forecasting, 30, 662–682.CrossRef Rossi, B., & Skhposyan, T. (2014). Evaluating predictive densities of us output growth and inflation in a large macroeconomic data set. International Journal of Forecasting, 30, 662–682.CrossRef
125.
Zurück zum Zitat Clark, T. E., & Ravazzolo, F. (2015). Macroeconomic forecasting performance under alternative specifications of time-varying volatility. Journal of Applied Econometrics, 30(4), 551–575.CrossRef Clark, T. E., & Ravazzolo, F. (2015). Macroeconomic forecasting performance under alternative specifications of time-varying volatility. Journal of Applied Econometrics, 30(4), 551–575.CrossRef
126.
Zurück zum Zitat Berg, T. O. (2017). Forecast accuracy of a BVAR under alternative specifications of the zero lower bound. Studies in Nonlinear Dynamics and Econometrics, 21(2), 1–29. Berg, T. O. (2017). Forecast accuracy of a BVAR under alternative specifications of the zero lower bound. Studies in Nonlinear Dynamics and Econometrics, 21(2), 1–29.
127.
Zurück zum Zitat Abbate, A., & Marcellino, M. (2018). Point, interval and density forecast of exchange rates with time varying parameter models. Journal of the Royal Statistical Society, 181(1), 155–179.CrossRef Abbate, A., & Marcellino, M. (2018). Point, interval and density forecast of exchange rates with time varying parameter models. Journal of the Royal Statistical Society, 181(1), 155–179.CrossRef
128.
Zurück zum Zitat Chan, J. C. C., & Eisenstat, E. (2018). Bayesian model comparison for time-varying parameter VARs with stochastic volatility. Journal of Applied Econometrics, 33(4), 509–532.CrossRef Chan, J. C. C., & Eisenstat, E. (2018). Bayesian model comparison for time-varying parameter VARs with stochastic volatility. Journal of Applied Econometrics, 33(4), 509–532.CrossRef
129.
Zurück zum Zitat Vardar, G., & Coskun, Y. (2018). Shock transmission and volatility spillover in stock and commodity markets: Evidence from advanced and emerging markets. Eurasian Economic Review, 8, 231–288.CrossRef Vardar, G., & Coskun, Y. (2018). Shock transmission and volatility spillover in stock and commodity markets: Evidence from advanced and emerging markets. Eurasian Economic Review, 8, 231–288.CrossRef
130.
Zurück zum Zitat Huber, F., Koop, G., & Pfarrhofer, M. (2020). Bayesian inference in high-dimensional time-varying parameter models using integrated rotated Gaussian approximations. Available at: https://arxiv.org/abs/2002.10274 (Accessed on: 11 November, 2021) Huber, F., Koop, G., & Pfarrhofer, M. (2020). Bayesian inference in high-dimensional time-varying parameter models using integrated rotated Gaussian approximations. Available at: https://​arxiv.​org/​abs/​2002.​10274 (Accessed on: 11 November, 2021)
131.
Zurück zum Zitat Kastner, G., & Huber, F. (2021). Sparse Bayesian vector autoregressions in huge dimensions. Journal of Forecasting, 39(7), 1142–1165.CrossRef Kastner, G., & Huber, F. (2021). Sparse Bayesian vector autoregressions in huge dimensions. Journal of Forecasting, 39(7), 1142–1165.CrossRef
132.
Zurück zum Zitat Anderson, R. G., Hoffman, D. L., & Rasche, R. H. (2002). A vector error-correction forecasting model of the U.S. Economy. Journal of Macroeconomics, 24(4), 569–598.CrossRef Anderson, R. G., Hoffman, D. L., & Rasche, R. H. (2002). A vector error-correction forecasting model of the U.S. Economy. Journal of Macroeconomics, 24(4), 569–598.CrossRef
133.
Zurück zum Zitat Swanson, N. R. (2002). Comments on ‘A vector error-correction forecasting model of the US economy’. Journal of Macroeconomics, 24(4), 599–606.CrossRef Swanson, N. R. (2002). Comments on ‘A vector error-correction forecasting model of the US economy’. Journal of Macroeconomics, 24(4), 599–606.CrossRef
134.
Zurück zum Zitat Kuo, C. Y. (2016). Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory. Economic Modelling, 52(B), 772–789.CrossRef Kuo, C. Y. (2016). Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory. Economic Modelling, 52(B), 772–789.CrossRef
135.
Zurück zum Zitat Huber, F., & Zörner, T. O. (2019). Threshold cointegration in international exchange rates: A Bayesian approach. International Journal of Forecasting, 35(2), 458–473.CrossRef Huber, F., & Zörner, T. O. (2019). Threshold cointegration in international exchange rates: A Bayesian approach. International Journal of Forecasting, 35(2), 458–473.CrossRef
136.
Zurück zum Zitat Geweke, J. (2005). Contemporary Bayesian econometrics and statistics. Wiley series in probability and statistics. Wiley-Interscience [John Wiley and Sons]. Geweke, J. (2005). Contemporary Bayesian econometrics and statistics. Wiley series in probability and statistics. Wiley-Interscience [John Wiley and Sons].
137.
Zurück zum Zitat Rosenblatt, M. (1952). Remarks on a multivariate transformation. Annals of Mathematical Statistics, 23(3), 470–472.CrossRef Rosenblatt, M. (1952). Remarks on a multivariate transformation. Annals of Mathematical Statistics, 23(3), 470–472.CrossRef
138.
Zurück zum Zitat Wróblewska, J., & Pajor, A. (2019). One-period joint forecasts of polish inflation, unemployment and interest rate using Bayesian VEC-MSF models. Central European Journal of Economic Modelling and Econometrics, 11(1), 23–45. Wróblewska, J., & Pajor, A. (2019). One-period joint forecasts of polish inflation, unemployment and interest rate using Bayesian VEC-MSF models. Central European Journal of Economic Modelling and Econometrics, 11(1), 23–45.
139.
Zurück zum Zitat Gans N, Koole G, Mandelbaum A (2003) Telephone call centers: a tutorial and literature review. Manuf Serv Oper Manag 5(2):79–141CrossRef Gans N, Koole G, Mandelbaum A (2003) Telephone call centers: a tutorial and literature review. Manuf Serv Oper Manag 5(2):79–141CrossRef
140.
Zurück zum Zitat Jung RC, Tremayne AR (2011) Useful models for time series of counts or simply wrong ones? AStA Adv Stat Anal 95(1):59–91CrossRef Jung RC, Tremayne AR (2011) Useful models for time series of counts or simply wrong ones? AStA Adv Stat Anal 95(1):59–91CrossRef
141.
Zurück zum Zitat Proietti T, Giovannelli A, Ricchi O, Citton A, Tegami C, Tinti C (2021) Nowcasting GDP and its components in a data-rich environment: the merits of the indirect approach. Int J Forecast (in Press) Proietti T, Giovannelli A, Ricchi O, Citton A, Tegami C, Tinti C (2021) Nowcasting GDP and its components in a data-rich environment: the merits of the indirect approach. Int J Forecast (in Press)
Metadaten
Titel
Ausblick
verfasst von
Vaibhavi Aher
Copyright-Jahr
2023
DOI
https://doi.org/10.1007/978-3-658-39275-8_4