Econometrics By Example/ by Damodar Gujarati
Material type: TextPublication details: New Delhi: Bloomsbury, 2022.Edition: 2nd edDescription: xxx,466pISBN:- 9789354356117
- 330.015195 GUJ/E
Item type | Current library | Home library | Collection | Call number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|---|---|
Book | Dept. of Economics Gift | Dept. of Economics | Gift or donation | 330.01 GUJ/E (Browse shelf(Opens below)) | Available | ECNG211 | ||
Book | Study Centre Alappuzha, University of Kerala | Study Centre Alappuzha, University of Kerala | 330.015195 GUJ/E (Browse shelf(Opens below)) | Available | USCA6192 |
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320.095483 ISA.K Kerala : Another possible world / | 320.095483 ISA.K Kerala : Another possible world / | 330.01 AST/A Applied Econometrics | 330.01 GUJ/E Econometrics By Example/ | 332.1 SID/B Banking and financing: Perspective from alternative economics | 339 XAV/M Macroeconomics: theory and policy | 361.250954 CHR/S Social and solidarity Economy experiments and Experiences |
Part I.Basics of Linear Regression--
1.The Linear regression model:an Overview--
2.Functional Forms of Regression models--
3.Qualitative explanatory variables regression models--
Part II.Regression Diagnostics--
4.Regression diagnostic I:Multicollinearity--
5.Regression diagnostic II:Heteroscedasticity--
6.Regression diagnostic III:Autocorrelation--
7.Regression Diagnostic IV:Model Specification Errors--
Part III:Topics in Cross-Section data--
8.The logit and Probit models--
9.Multinomial regression models--
10.Ordinal regression models--
11.Limited dependent variable regression models--
12.Modeling count data:the Poission and negative binomial regression models--
13.Part IV:Time series econometrics--
13.Stationary and non stationary time series--
14.Cointegration and error correction models--
15.Asset price volatility:the ARCH and GARCH models--
16.Economic forecasting--
Part V:Selected topics in Econometrics--
17.Panel data regression models--
18.Survival analyysis--
19.Stochastic regressors and the method of instrumental variables--
20.Beyond OLS:Quantile regression--
21.Multivariate regression models.
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