Dodatna literatura za predavanje prof. dr. Igorja Mastena (izbor po izbiri postavljalcev strani):
Adolfson, M., Laseen, S., Linde, J., & Villani, M., (2007). Bayesian estimation of an open economy DSGE model with incomplete pass-through. Journal of International Economics, 72, 481–511.
Amengual, D., & Watson, M. W., (2007). Consistent estimation of the number of dynamic factors in a large N and T panel. Journal of Business and Economic Statistics, 25, 9–96.
Anderson, T. W. (1951). Estimating linear restrictions on regression coefficients for multivariate normal distributions. Annals of Mathematical Statistics, 22, 327–351.
Bai, J. (2004). Estimating cross-section common stochastic trends in nonstationary panel data. Journal of Econometrics, 122, 137–183.
Bai, J., & Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70, 191–221.
Bai, J., & Ng, S. (2004). A PANIC attack on unit roots and cointegration. Econometrica, 72, 1127–1177.
Bai, J., & Ng, S. (2006). Confidence intervals for diffusion index forecasts with a large number of predictors and inference for factor-augmented regressions. Econometrica, 74, 1133–1150.
Bai, J., & Ng, S. (2007). Determining the number of primitive shocks in factor models. Journal of Business and Economic Statistics, 25, 52–60.
Bai, J., Kao, C., & Ng, S. (2009). Panel cointegration with global stochastic trends. Journal of Econometrics, 149, 82–99.
Banerjee, A., & Marcellino, M. (2009). Factor-augmented error correctionmodels. In J. L. Castle&N. Shephard (Eds.), The methodology and practice of econometrics: A Festschrift for David Hendry (pp. 227–254). Oxford, UK: Oxford University Press.
Banerjee, A.,Marcellino, M., & Masten, I. (2014a). Forecasting with factor-augmented error correction models. International Journal of Forecasting, 30, 589–612.
Banerjee, A., Marcellino, M., & Masten, I. (2014b). Structural FECM: Cointegration in large-scale structural FAVAR models (CEPR Discussion Papers Series No. 9858).
Barigozzi, M., Lippi, M., & Luciani, M. (2016a). Dynamic factor models, cointegration, and error correction mechanisms (Finance and Economics Discussion Series 2016-018). Washington, DC: Board of Governors of the Federal Reserve System.
Barigozzi,M., Lippi,M., & Luciani,M. (2016b). Non-stationary dynamic factor models for large datasets (Finance and Economics Discussion Series 2016-024). Washington, DC: Board of Governors of the Federal Reserve System.
Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79, 655–673.
Choi, I. (2011). Efficient estimation of nonstationary factor models (RIME Working Paper No. 2011-13).
Eickmeier, S. (2009). Comovements and heterogeneity in the euro area analyzed in a non-stationary dynamic factor model. Journal of Applied Econometrics, 24, 933–959.
Forni, M., & Gambetti, L. (2010). The dynamic effects of monetary policy: A structural factor model approach. Journal of Monetary Economics, 57, 203–216.
Forni, M., Gambetti, L., & Sala, L. (2014). No news in business cycles. Economic Journal, 124, 1168–1191.
Forni, M., Giannone, D., Lippi, M., & Reichlin, L. (2009). Opening the black box: Structural factors models with large cross sections. Econometric Theory, 25, 1319–1347.
Forni, M., Hallin, M., Lippi, M., & Reichlin, L. (2000). The generalized dynamic-factor model: Identification and estimation. Review of Economics and Statistics, 82, 540–554.
Gengenbach, C., Urbain, J.-P., & Westerlund, J. (2016). Error correction testing in panels with common stochastic trends. Journal of Applied Econometrics, 31, 982–1004.
Kilian, L. (1998). Small-sample confidence intervals for impulse response function. Review of Economics and Statistics, 80/2, 218–230.
King, R. G., Plosser, C. I., Stock, J. H., & Watson, M. W. (1991). Stochastic trends and economic fluctuations. American Economic Review, 81, 819–840.
Lütkepohl, H. (2014). Structural vector autoregressive analysis in a data-rich environment: A survey (DIW Discussion paper 1351). Berlin, Germany: German Institute for Economic Research.
McCracken, M.W., & Ng, S. (2015). FRED-MD: A monthly database for macroeconomic research (Working Papers 2015-12). St. Louis, MO, Federal Reserve Bank of St. Louis.
Olivei, G., & Tenreyro, S. (2010). Wage-setting patterns and monetary policy: International evidence. Journal of Monetary Economics, 57, 785–802.
Stock, J. H., & Watson, M.W. (2002a). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97, 1167–1179.
Stock, J. H., & Watson, M.W. (2002b). Macroeconomic forecasting using diffusion indexes. Journal of Business and Economic Statistics, 20, 147–162.
Stock, J. H., & Watson, M. W. (2005). Implications of dynamic factor models for VAR analysis (NBER Working Paper No. 11467).
Vlaar, P. J. G. (2004). On the asymptotic distribution of impulse response functions with long-run restrictions. Econometric Theory, 20, 891–903.
Warne, A. (1993). A common trends model: identification, estimation and inference University of Stockholm (IIES Seminar Paper No. 555).
Westerlund, J., & Larsson, R. (2009). A note on the pooling of individual PANIC unit root tests. Econometric Theory, 25, 1851–1868.
Wu, J. C., & Xia, F. D. (2016). Measuring the macroeconomic impact of monetary policy at the zero lower bound. Journal of Money, Credit, and Banking, 48, 253–291.
Adolfson, M., Laseen, S., Linde, J., & Villani, M., (2007). Bayesian estimation of an open economy DSGE model with incomplete pass-through. Journal of International Economics, 72, 481–511.
Amengual, D., & Watson, M. W., (2007). Consistent estimation of the number of dynamic factors in a large N and T panel. Journal of Business and Economic Statistics, 25, 9–96.
Anderson, T. W. (1951). Estimating linear restrictions on regression coefficients for multivariate normal distributions. Annals of Mathematical Statistics, 22, 327–351.
Bai, J. (2004). Estimating cross-section common stochastic trends in nonstationary panel data. Journal of Econometrics, 122, 137–183.
Bai, J., & Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70, 191–221.
Bai, J., & Ng, S. (2004). A PANIC attack on unit roots and cointegration. Econometrica, 72, 1127–1177.
Bai, J., & Ng, S. (2006). Confidence intervals for diffusion index forecasts with a large number of predictors and inference for factor-augmented regressions. Econometrica, 74, 1133–1150.
Bai, J., & Ng, S. (2007). Determining the number of primitive shocks in factor models. Journal of Business and Economic Statistics, 25, 52–60.
Bai, J., Kao, C., & Ng, S. (2009). Panel cointegration with global stochastic trends. Journal of Econometrics, 149, 82–99.
Banerjee, A., & Marcellino, M. (2009). Factor-augmented error correctionmodels. In J. L. Castle&N. Shephard (Eds.), The methodology and practice of econometrics: A Festschrift for David Hendry (pp. 227–254). Oxford, UK: Oxford University Press.
Banerjee, A.,Marcellino, M., & Masten, I. (2014a). Forecasting with factor-augmented error correction models. International Journal of Forecasting, 30, 589–612.
Banerjee, A., Marcellino, M., & Masten, I. (2014b). Structural FECM: Cointegration in large-scale structural FAVAR models (CEPR Discussion Papers Series No. 9858).
Barigozzi, M., Lippi, M., & Luciani, M. (2016a). Dynamic factor models, cointegration, and error correction mechanisms (Finance and Economics Discussion Series 2016-018). Washington, DC: Board of Governors of the Federal Reserve System.
Barigozzi,M., Lippi,M., & Luciani,M. (2016b). Non-stationary dynamic factor models for large datasets (Finance and Economics Discussion Series 2016-024). Washington, DC: Board of Governors of the Federal Reserve System.
Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79, 655–673.
Choi, I. (2011). Efficient estimation of nonstationary factor models (RIME Working Paper No. 2011-13).
Eickmeier, S. (2009). Comovements and heterogeneity in the euro area analyzed in a non-stationary dynamic factor model. Journal of Applied Econometrics, 24, 933–959.
Forni, M., & Gambetti, L. (2010). The dynamic effects of monetary policy: A structural factor model approach. Journal of Monetary Economics, 57, 203–216.
Forni, M., Gambetti, L., & Sala, L. (2014). No news in business cycles. Economic Journal, 124, 1168–1191.
Forni, M., Giannone, D., Lippi, M., & Reichlin, L. (2009). Opening the black box: Structural factors models with large cross sections. Econometric Theory, 25, 1319–1347.
Forni, M., Hallin, M., Lippi, M., & Reichlin, L. (2000). The generalized dynamic-factor model: Identification and estimation. Review of Economics and Statistics, 82, 540–554.
Gengenbach, C., Urbain, J.-P., & Westerlund, J. (2016). Error correction testing in panels with common stochastic trends. Journal of Applied Econometrics, 31, 982–1004.
Kilian, L. (1998). Small-sample confidence intervals for impulse response function. Review of Economics and Statistics, 80/2, 218–230.
King, R. G., Plosser, C. I., Stock, J. H., & Watson, M. W. (1991). Stochastic trends and economic fluctuations. American Economic Review, 81, 819–840.
Lütkepohl, H. (2014). Structural vector autoregressive analysis in a data-rich environment: A survey (DIW Discussion paper 1351). Berlin, Germany: German Institute for Economic Research.
McCracken, M.W., & Ng, S. (2015). FRED-MD: A monthly database for macroeconomic research (Working Papers 2015-12). St. Louis, MO, Federal Reserve Bank of St. Louis.
Olivei, G., & Tenreyro, S. (2010). Wage-setting patterns and monetary policy: International evidence. Journal of Monetary Economics, 57, 785–802.
Stock, J. H., & Watson, M.W. (2002a). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97, 1167–1179.
Stock, J. H., & Watson, M.W. (2002b). Macroeconomic forecasting using diffusion indexes. Journal of Business and Economic Statistics, 20, 147–162.
Stock, J. H., & Watson, M. W. (2005). Implications of dynamic factor models for VAR analysis (NBER Working Paper No. 11467).
Vlaar, P. J. G. (2004). On the asymptotic distribution of impulse response functions with long-run restrictions. Econometric Theory, 20, 891–903.
Warne, A. (1993). A common trends model: identification, estimation and inference University of Stockholm (IIES Seminar Paper No. 555).
Westerlund, J., & Larsson, R. (2009). A note on the pooling of individual PANIC unit root tests. Econometric Theory, 25, 1851–1868.
Wu, J. C., & Xia, F. D. (2016). Measuring the macroeconomic impact of monetary policy at the zero lower bound. Journal of Money, Credit, and Banking, 48, 253–291.
additional_bibliography_factormodels-referencelist.pdf | |
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Na spodnjih povezavah najdete nekaj monografij oz. daljših besedil, ki povezujejo faktorske modele in ekonometrijo časovnih vrst:
Lütkepohl, H. (2005). New introduction to multiple time series analysis. New York, NY: Springer.
Mergner, S. (2009). Applications of State Space Models in Finance. An Empirical Analysis of the Time-varying Relationship between Macroeconomics, Fundamentals and Pan-European Industry Portfolios. Göttingen: Universitätsverlag Göttingen.
Shumway, R. and D. Stoffer (2000). Time Series Analysis and its Applications. New York: Springer.
Stock, J. and M. Watson (2005). Implications of Dynamic Factor Models for VAR Analysis. NBER Working Paper No. 11467, July 2005.
Lütkepohl, H. (2005). New introduction to multiple time series analysis. New York, NY: Springer.
Mergner, S. (2009). Applications of State Space Models in Finance. An Empirical Analysis of the Time-varying Relationship between Macroeconomics, Fundamentals and Pan-European Industry Portfolios. Göttingen: Universitätsverlag Göttingen.
Shumway, R. and D. Stoffer (2000). Time Series Analysis and its Applications. New York: Springer.
Stock, J. and M. Watson (2005). Implications of Dynamic Factor Models for VAR Analysis. NBER Working Paper No. 11467, July 2005.