[Applied Econometric Time Series 3rd Pdf 125
Facunda Ganesh
MECO 4352 Applied Econometrics and Time Series Analysis (3 semester credit hours) This course introduces students to the use of econometric and time series methods for the analysis of business data, paying particular attention to their uses in business forecasting. Prerequisite: STAT 3360 or OPRE 3360. (3-0) R
I'm a student and I'm interested in focusing on the application of econometric theory to "practical" situations. It would be great if you can suggest freely available books along with those that are not. The best thing would be books with explained examples of applications to real data. Also, a good and solid theoric book would be appreciated (maybe with a practical approach to the theory). All the above, whether it would be possible, may consider econometrics in general and macroeconometrics (in this area, along with the general topics, also some specific suggestion on forecasting would be great).
Summing up, the books I would like you to suggest me has to refer to these arguments:
- econometric theory
- applied econometrics
- macroeconometric theory
- applied macroeconometrics
- forecasting
- applied forecasting
I know that's a huge question and every suggestion would be strongly appreciated.
As properly suggested I add that for the applications any of Stata, Eviews and R is ok. About my background I think I can say to have a good graduate preparation, but it is fair for you to suggest books that starts from the basics concepts.
Experts can provide better suggestions if you give more information about your background, preferred programming language, etc. An absolute beginner myself, I am trying to learn Stats and Econometrics, and their applications using R language, the lingua franca among stats community. So far I have consulted primarily Springer's Use R series and free resources available on CRAN. I found the following particularly useful:
A good (and free) introduction to applied macroeconometrics is the textbook by David Hendry: Introductory Macro-econometrics: A New Approach which walks through several useful examples and explains things fairly intuitively.
It also touches some on forecasting but I would suggest Forecasting Economic Time Series by Mike Clements and David Hendry or Applied Econometric Time Series by Walter Enders or Time series models for business and economic forecasting by Philip H Franses as non-free alternatives that are more advanced.
Studies time series modeling and its applications. Covers estimation, inference and forecasting in univariate and multivariate models for times series data. The emphasis is on real data applications to finance markets, economic growth, and detecting recessions using Stata. Effective Fall 2023, this course fulfills a single unit in each of the following BU Hub areas: Writing-Intensive Course, Quantitative Reasoning II, Research and Information Literacy.
Unique in that it covers modern time series analysis from the sole prerequisite of an introductory course in multiple regression analysis. Describes the theory of difference equations, demonstrating that they are the foundation of all time-series models with emphasis on the Box-Jenkins methodology. Considers many recent developments in time series analysis including unit root tests, ARCH models, cointegration/error-correction models, vector autoregressions and more. There are numerous examples to illustrate various techniques, many of which concern econometric models of transnational terrorism. The accompanying disk provides data for students to work with.
Walter Enders' Applied Econometric Time Series text provides a lucid introduction to and discussion of most of the key topics in modern time series econometrics, including stationarity and unit roots, ARIMA models, volatility (ARCH/GARCH) models, cointegration models, and more. It is 460 pages long, hardbound, and is primarily geared towards those taking Masters and PhD courses in time series analysis or advanced econometrics, or for professionals who wish to learn more about time series analysis techniques.
Many economic variables are observed over time on a regular frequency (e.g. the quarterly growth rate of GDP, the monthly CPI inflation rate, daily interest rates, daily returns of the DAX stock market index). This type of data is known as time series data and often features correlation over time that can be exploited for forecasting. In this course econometric models for univariate time series data are introduced. Estimation and model specification as well as their use in forecasting is discussed in the lecture. Students learn how to apply these methods in practice during the computer session using Python.
An introduction to the key statistical techniques required to examine economic models with data, enabling students to follow large parts of the empirical literature and to carry out such analyses themselves.
To develop skills needed to apply econometric techniques in the following contexts: (i) the implementation of instrumental variable methods when regressors are endogenous; (ii) the use of binary choice models to model probabilities in applied economics; (iii) the estimation and interpretation of models designed for panel data; (iv) forecasting using stationary ARMA models and evaluating forecast performance; (v) the investigation of the time series properties of economic data and the implications of these properties for least squares analysis; and (vi) cointegration analyses when a single equation model is under scrutiny and the derivation of associated error correction schemes when variables are cointegrated.
To develop skills needed to interpret applied econometric results in the following contexts: (i) the analysis of regression models in the presence of omitted variables; (ii) the application of linear probability, logit and probit models; (iii) relationships estimated using linear panel data models; (iv) the outcomes of a battery of diagnostic checks after estimation; (v) testing for unit roots in economic variables by means of Dickey-Fuller tests; and (vi) empirical analyses based upon either the Granger-Engle two-step method or the Autoregressive Distributed Lag model.
Read and understand more of the econometric evidence published in academic journals and books. Understanding is extended beyond the second year Econometrics for Economists module by covering new topics such as: instrumental variable methods; binary choice models; and panel data (in which there are both cross-section and time series dimensions); forecasting using stationary dynamic ARMA models and evaluating forecast performance; nonstationary time series variables in regression; integration and cointegration (which are very important in modern applied macroeconomics).
This paper uses time-series data from nineteen Latin American countries and the U.S. to test for income convergence using two existing definitions of convergence and a new testable definition of β-convergence. Only Dominican Republic and Paraguay were found to pair-wise converge according to the Bernard and Durlauf (1995) definition. More evidence of stochastic convergence exists when allowing for structural breaks using the two-break minimum LM unit root of Lee and Strazicich (2003). The results show greater evidence of convergence within Central America than within South America. Dominican Republic is the only country that complies with the neoclassical conditions of income convergence.
To develop skills needed to apply econometric techniques in the following contexts: (i) the implementation of instrumental variable methods when regressors are endogenous; (ii) the use of binary choice models to model probabilities in applied economics; (iii) the estimation and interpretation of models designed for panel data; (iv) forecasting using stationary ARMA models and evaluating forecast performance; (v) the investigation of the time series properties of economic data and the implications of these properties for least squares analysis; and (vi) cointegration analyses when a single equation model is under scrutiny and the derivation of associated error correction schemes when variables are cointegrated.
To develop skills needed to interpret applied econometric results in the following contexts: (i) the analysis of regression models in the presence of omitted variables; (ii) the application of linear probability, logit and probit models; (iii) relationships estimated using linear panel data models; (iv) the outcomes of a battery of diagnostic checks after estimation; (v) testing for unit roots in economic variables by means of Dickey-Fuller tests; and (vi) empirical analyses based upon either the Granger-Engle two-step method or the Autoregressive Distributed Lag model.
Re-assessment (see also below): the arrangements described below refer to students who resit without mitigating circumstances. For students who resit with mitigating circumstances, the arrangements are instead as follows:
This course will examine modern applied research on macroeconomic and financial issues. The main objective is to allow students to understand, critically appraise and replicate applied work on macroeconomic and financial topics.
Essential Textbook: Enders, W. Applied Econometric Time Series, Wiley, 2014. This book can be purchased from the bookstore on campus. The students can also obtain a copy of this book for short term loan from the Chifley library. An online version of the book is also available via ANU library.
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