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Books on the topic 'Regression analysis – Econometric models'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Bootstrap tests for regression models. Basingstoke, Hampshire: Palgrave Macmillan, 2009.

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2

Godfrey, L. G. Bootstrap tests for regression models. Basingstoke, Hampshire: Palgrave Macmillan, 2009.

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3

Hylleberg, Svend. Seasonality in regression. Orlando: Academic Press, 1986.

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4

Elbers, Chris. Imputed welfare estimates in regression analysis. Washington, D.C: World Bank, 2004.

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5

Seasonality in regression. Orlando, Fla: Academic Press, 1986.

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6

Bährens, Henning. Partielle und simultane Prüfung auf Autokorrelation und Heteroskedastizität der Störvariablen im linearen Regressionsmodell. Göttingen: Unitext, 1992.

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7

John, DiNardo, ed. Econometric methods. 4th ed. New Yorki: McGraw Hil, 2007.

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8

Short-memory linear processes and econometric applications. Hoboken, N.J: Wiley, 2011.

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9

Empirical vector autoregressive modeling. Berlin: Springer-Verlag, 1994.

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10

Bhatti, M. Ishaq. Non-Nested Regression Models: UK ed. edition. Hauppauge, New York, USA: Nova Science Publishers Inc, 2013.

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11

Fahrmeir, Ludwig. Regression: Models, Methods and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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12

Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.

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Abstract:

The textbook includes topics of modern econometrics, often used in economic research. Some aspects of multiple regression models related to the problem of multicollinearity and models with a discrete dependent variable are considered, including methods for their estimation, analysis, and application. A significant place is given to the analysis of models of one-dimensional and multidimensional time series. Modern ideas about the deterministic and stochastic nature of the trend are considered. Methods of statistical identification of the trend type are studied. Attention is paid to the evaluation, analysis, and practical implementation of Box — Jenkins stationary time series models, as well as multidimensional time series models: vector autoregressive models and vector error correction models. It includes basic econometric models for panel data that have been widely used in recent decades, as well as formal tests for selecting models based on their hierarchical structure. Each section provides examples of evaluating, analyzing, and testing models in the R software environment. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is addressed to master's students studying in the Field of Economics, the curriculum of which includes the disciplines Econometrics (advanced course)", "Econometric modeling", "Econometric research", and graduate students."

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13

Fernández-Villaverde, Jesús. A, B, C's (and D)'s for understanding VARS. Cambridge, MA: National Bureau of Economic Research, 2005.

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14

Fernández-Villaverde, Jesús. A, B, C's, (and D's) for understanding VARS. [Atlanta, Ga.]: Federal Reserve Bank of Atlanta, 2005.

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15

Ley, Eduardo. We just averaged over two trillion cross-country growth regressions. [Washington, D.C.]: International Monetary Fund, Fiscal Affairs Department, 1999.

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16

Altissimo, Filippo. Nonlinear VAR: Some theory and an application to US GNP and unemployment. Rome: Banca d'Italia, 1998.

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17

Altissimo, Filippo. Nonlinear VAR: Some theory and an application to US GNP and unemployment. [Roma]: Banca d'Italia, 1998.

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18

Brooks, Chris. RATS handbook to accompany Introductory econometrics for finance. Cambridge: Cambridge University Press, 2009.

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19

W, Cooper Russell. Exhuming Q: Market power vs. capital market imperfections. Cambridge, MA: National Bureau of Economic Research, 2001.

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20

W, Cooper Russell. Exhuming Q: Market power vs. capital market imperfections. [Minneapolis, MN]: Federal Reserve Bank of Minneapolis, Research Dept., 2001.

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21

Regressionsschätzung skalarwertiger Präferenzfunktionen für ökonometrische Entscheidungsmodelle auf der Grundlage von Befragungsdaten. Frankfurt am Main: P. Lang, 1992.

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22

Lyandres, Evgeny. Investment-based underperformance following seasoned equity offerings. Cambridge, Mass: National Bureau of Economic Research, 2005.

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23

Lyandres, Evgeny. Investment-based underperformance following seasoned equity offerings. Cambridge, MA: National Bureau of Economic Research, 2005.

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24

Tschentscher, Holger. Variable Parameterregressionsmodelle: Anpassungs- und Prognoseverhalten am Beispiel der Entwicklung der Arbeitslosigkeit in der Bundesrepublik Deutschland. Göttingen: Cuvillier, 1994.

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25

Bizer, David S. Testing the positive theory of government finance. Cambridge, MA: National Bureau of Economic Research, 1990.

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26

1946-, Fraser Mark W., ed. Propensity score analysis: Statistical methods and applications. Thousand Oaks, Calif: Sage Publications, 2010.

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27

J, Barro Robert. World interest rates and investment. Cambridge, Mass: National Bureau of Economic Research, 1991.

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28

J, Barro Robert. World real interest rates. Cambridge, MA: National Bureau of Economic Research, 1990.

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29

Blunch, Neils-Hugo. Asymmetries in union relative wage effects in Ghanaian manufacturing: An analysis applying quantile regressions. Washington, D.C: World Bank, Africa Technical Families, Human Development 3, 2001.

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30

R, Nelson Charles. Predictable stock returns: Reality or statistical illusion? Cambridge, MA: National Bureau of Economic Research, 1990.

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31

Bordo, Michael D. The long-run behavior of velocity: The institutional approach revisited. Cambridge, MA: National Bureau of Economic Research, 1989.

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32

Hilbe, Joseph. Logistic regression models. Boca Raton: Chapman & Hall/CRC, 2009.

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33

Kutner, Michael H. Applied linear regression models. 4th ed. New York, NY: McGraw-Hill, 2003.

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34

William, Wasserman, and Kutner Michael H, eds. Applied linear regression models. 2nd ed. Homewood, Ill: Irwin, 1989.

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35

Chris, Nachtsheim, and Neter John, eds. Applied linear regression models. 4th ed. Boston: McGraw-Hill/Irwin, 2004.

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36

Ward, Michael Don. Spatial regression models. Thousand Oaks: Sage Publications, 2008.

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37

Theodore, Bos, ed. Stochastic parameter regression models. Beverly Hills: Sage Publications, 1985.

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38

Theodore, Bos, ed. Stochastic parameter regression models. Beverly Hills: Sage Publications, 1985.

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39

Forecasting with dynamic regression models. New York: Wiley, 1991.

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40

Konstantinos, Fokianos, ed. Regression models for time series analysis. Hoboken, N.J: Wiley-Interscience, 2002.

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41

Kedem, Benjamin, and Konstantinos Fokianos. Regression Models for Time Series Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2002. http://dx.doi.org/10.1002/0471266981.

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42

Fry, John M. (John Michael), 1980-, ed. Regression: Linear models in statistics. London: Springer, 2010.

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43

1947-, KOENKER ROGER. L-estimation for linear models. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1986.

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44

1947-, KOENKER ROGER. L-estimation for linear models. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1986.

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45

KOENKER, ROGER 1947. L-estimation for linear models. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1986.

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46

1947-, KOENKER ROGER. L-estimation for linear models. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1986.

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47

Steckel, Joel H. Cross-validating regression models in marketing research. Fontainebleau: INSEAD, 1990.

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48

Štulajter, František. Predictions in time series using regression models. New York: Springer, 2002.

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49

Regression and linear models. New York: McGraw-Hill, 1990.

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50

Fixed effects regression models. Thousand Oaks, Calif: Sage Publications, 2009.

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