Abstract
This edited volume consists of 30 original contributions in the two closely related research areas of empirical economic research and empirical financial research. Empirical economic research, also called empirical economics, is an important traditional sub-discipline of economics. The research activities in this area are particularly reflected by the journal “Empirical Economics” published by Springer-Verlag since 1976, and by the parallel series “Studies in Empirical Economics,” which consists of 21 volumes published from 1989 to 2009 on different topics in this area. In recent years research in empirical economics has experienced another booming phase due to easy availability of very large data sets and the fast increase of computer power.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39, 885–905.
[Part 1:] Common References
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307–327.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1996). The econometrics of financial markets. Princeton: Princeton University Press.
Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom. Econometrica, 50, 987–1007.
Engle, R. F., Ghysels, E., & Sohn, B. (2008). On the economic sources of stock market volatility. AFA 2008 New Orleans Meetings Paper.
Speth, H.-Th. (2006). The BV4.1 procedure for decomposing and seasonally adjusting economic time series. Wiesbaden: German Federal Statistical Office.
Stevenson, B., & Wolfers, J. (2012). Business is booming in empirical economics. Opinion in Bloomberg, August 6, 2012.
Taylor, S. J. (1986). Modelling financial time series. Chichester: Wiley.
[Part 2:] Selected Publications of Prof. Heiler Cited in this Chapter
Abberger, K., Feng, Y., & Heiler, S. (1998). Nonparametric smoothing and quantile estimation in time series. In G. Bol, G. Nakhaeizadeh, & K.-H. Vollmer (Eds.), Risk measurement, econometrics and neural networks (pp. 1–16). Heidelberg: Physica.
Abberger, K., & Heiler, S. (2001). Nonparametric quantile regression with application to stock returns. In J. Kunert & G. Trenkler (Eds.), Mathematical statistics and biometrical applications. Festschrift für Siegfried Schach. Lohmar: Josef Eul.
Abberger, K., & Heiler, S. (2002). Nonparametric quantile regression with applications to financial time series. In Y. Dodge (Ed.), Statistical data analysis based on the L 1 norm and related methods (pp. 149–160). Basel: Birkhäuser.
Allende, H., & Heiler, S. (1992). Recursive generalized M-estimates for ARMA-models. Journal of Time Series Analysis, 13, 1–18.
Beran, J., Feng, Y., & Heiler, S. (2009). Modifying the double smoothing bandwidth selector in nonparametric regression. Statistical Methodology, 6, 447–465.
Feng, Y., & Heiler, S. (1998). Locally weighted autoregression. In R. Galata & H. Küchenhoff (Eds.), Econometrics in theory and practice. Festschrift für Hans Schneeweiß (pp. 101–117). Heidelberg: Physica
Feng, Y., & Heiler, S. (2000, October). Eine datengesteuerte robuste Version des Berliner Verfahrens. Wirtschaft und Statistik, 10/2000, 786–795.
Feng, Y., & Heiler, S. (2009). A simple bootstrap bandwidth selector for local polynomial fitting. Journal of Statistical Computation and Simulation, 79, 1425–1439.
Ghosh, S., Beran, J., Heiler, S., Percival, D., & Tinner, W. (2007). Memory, non-stationarity and trend: analysis of environmental time series. In F. Kienast, O. Wildi, & S. Ghosh (Eds.), A changing world: Challenges for landscape research (pp. 223–247). Netherlands: Springer.
Hebbel, H., & Heiler, S. (1978). Die Verwendung von Spline-Funktionen bei der Schätzung-von Spektraldichten. In K.-A. Schäffer (Ed.), Splinefunktionen in der Statistik. Allgemeines Statistisches Archiv, Special Issue, 14, 66–85.
Hebbel, H., & Heiler, S. (1985). Zeitreihenglättung in einem Fehler-in-den-Variablen-Modell. In G. Buttler, et al. (Eds), Statistik zwischen Theorie und Praxis. Festschrift in Honour of K.-A. Schäffer (pp. 105–117). Göttingen: Vandenhoeck & Ruprecht.
Hebbel, H., & Heiler, S. (1987a). Zeitreihenzerlegung über ein Optimalitätskriterium. Allgemeines Statistisches Archiv, 71, 305–318.
Hebbel, H., & Heiler, S. (1987b). Trend and seasonal decomposition in discrete time. Statistical Papers, 28, 133–158.
Hebbel, H., & Heiler, S. (1988). Statistische Analyse von Wassergütedaten. Allgemeines Statistisches Archiv, 72, 24–39.
Heiler, S. (1969). Überlegungen zu einem statistischen Modell einer wirtschaftlichen Zeitreihe und einem daraus resultierenden Analyseverfahren. In B. Nullau, S. Heiler, P. Wäsch, B. Meisner, & D. Filip (Eds.), “Das Berliner Verfahren” - Ein Beitrag zur Zeitreihenanalyse. DIW-Beiträge zur Strukturforschung (Vol. 7, pp. 19–43). Berlin: Duncker & Humblot.
Heiler, S. (1970). Theoretische Grundlagen des “Berliner Verfahrens”. In W. Wetzel (Ed.), Neuere Entwicklungen auf dem Gebiet der Zeitreihenanalyse. Allgemeines Statistisches Archiv, Special Issue 1, 67–93.
Heiler, S. (1976). Entwurf kausaler Filter zur Analyse ökonomischer Zeitreihen bei Vorschriften imFrequenzbereich. In K.-A. Schäffer (Ed.), Beiträge zur Zeitreihenanalyse. Allgemeines Statistisches Archiv, Special Issue 9, 7–33.
Heiler, S. (1977). Some recent developments in the analysis of component models for economic time series. Statistische Hefte, 18, 154–180.
Heiler, S. (1978). Der Geburtenrückgang in der Bundesrepublik und seine Auswirkungen. Research Report. Department of Statistics, University of Dortmund.
Heiler, S. (1982). Die Verwendung von Zeitreihenverfahren und Verzweigungsprozessen zur Bevölkerungsvorhersage. In W. Piesch & W. Förster (Eds.), Angewandte Statistik und Wirtschaftsforschung heute (pp. 66–75). Göttingen: Vandenhoeck & Ruprecht.
Heiler, S. (1990). Robust estimation of ARMA-models. Journal of Computing and Information, 1, 81–90.
Heiler, S. (2001). Nonparametric time series analysis: Nonparametric regression, locally weighted regression, autoregression, and quantile regression. In D. Pe\(\tilde{\mathrm{n}}\) a, G. C. Tiao, & R. S. Tsay (Eds.), A course in time series analysis (pp. 308–347). New York: Wiley.
Heiler, S., & Feng, Y. (1996). Datengesteuerte Zerlegung saisonaler Zeitreihen. IFO Studien: Zeitschrift für empirische Wirtschaftsforschung, 42, 337–369.
Heiler, S., & Feng, Y. (1998). A simple root n bandwidth selector for nonparametric regression. Journal of Nonparametric Statistics, 9, 1–21.
Heiler, S., & Feng, Y. (2000). Data-driven decomposition of seasonal time series. Journal of Statistical Planning and Inference, 91, 351–363.
Heiler, S., & Michels, P. (1986). FLUKON-Programm zur laufenden Kontrolle halbstündiger Meßwerte für Sauerstoffgehalt, pH-Wert, Leitfähigkeit und Wassertemperatur der Leine. Discussion Paper, University of Dortmund.
Heiler, S., & Michels, P. (1989). Die Wasserführung eines Flusses - Statistische Modellierungsversuche. Discussion Paper. University of Konstanz.
Heiler, S., & Michels, P. (1994). Deskriptive und Explorative Datenanalyse. München: Oldenbourg.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Beran, J., Feng, Y., Hebbel, H. (2015). Introduction. In: Beran, J., Feng, Y., Hebbel, H. (eds) Empirical Economic and Financial Research. Advanced Studies in Theoretical and Applied Econometrics, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-319-03122-4_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-03122-4_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03121-7
Online ISBN: 978-3-319-03122-4
eBook Packages: Business and EconomicsEconomics and Finance (R0)