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Logo: Institute of Statistics/Leibniz Universität Hannover
Logo Leibniz Universität Hannover
Logo: Institute of Statistics/Leibniz Universität Hannover
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Regression Analysis with Time Series Data (EN)

Description: Regression analysis with time series data entails special challenges since the innovations are often heteroscedastic and exhibit serial correlation. Heteroscedasticity and autocorrelation consistent (HAC) estimates of the variance covariance matrix address these issues and provide robust standard errors. (This thesis is supervised in English.)

Literature: Kiefer, N. M. and Vogelsang, T. J. (2005): A new asymptotic theory for heteroskedasticityautocorrelation robust tests, Econometric Theory, 21(6):1130-1164.; Martin, V., Hurn, S., and Harris, D. (2013): Econometric modelling with time series: speci fication, estimation and testing, Cambridge University Press.; Stock, J. H. and Watson, M. W. (2011b): Introduction to econometrics, 3rd edition.

Linear time series models (EN)

Description: Linear time series models, especially ARMA models, are central to modern stationary time series data analysis. They include past observations of the time series and past interference terms and are therefore a simple and economical way of modelling. Possible extensions are ARIMA and ARFIMA models. (This thesis is supervised in English.)

Literature: Enders, W. (2014): Applied Econometric Time Series, 4th Edition, Wiley Series in Probability and Statistics. Wiley.; Martin, V., Hurn, S., and Harris, D. (2013): Econometric modelling with time series: speci cation, estimation and testing, Cambridge University Press.; Hamilton, J. D. (1994): Time series analysis, volume 2, Princeton university press Princeton, NJ.

Unit Root Tests and Structural Breaks (EN)

Description: Since many economic time series are highly persistent, there is an ongoing debate whether they are di fference stationary or trend stationary, which has important implications for the impact of economic shocks. Tests for the null hypothesis of a di fference stationary process can falsely reject if the series is also subject to structural change. Therefore, there are several extensions that allow for the presence of structural breaks. (This thesis is supervised in English.)

Literature: Enders, W. (2014): Applied Econometric Time Series, 4th Edition, Wiley Series in Probability and Statistics. Wiley.; Martin, V., Hurn, S., and Harris, D. (2013): Econometric modelling with time series: speci cation, estimation and testing, Cambridge University Press.; Hamilton, J. D. (1994): Time series analysis, volume 2, Princeton university press Princeton, NJ.

VAR Models (EN)

Description: A vector autoregression model is a natural extension of the univariate autoregressive model to dynamic multivariate time series. Its simple framework enables to capture rich dynamics in multiple time series and apply macroeconomic forecasts. (This thesis is supervised in English.)

Literature: Lütkepohl, H. (2005): New introduction to multiple time series analysis, Springer Science & Business Media.; Martin, V., Hurn, S., and Harris, D. (2013): Econometric modelling with time series: speci cation, estimation and testing, Cambridge University Press.; Stock, J. H. and Watson, M. W. (2001): Vector autoregressions, Journal of Economic perspectives, 15(4):101-115.