Applied Econometrics Time Series Analysis pdf download read full text online, request Baidu.com cloud resources
Applied Econometrics (Time Series Analysis Original Book 4th Edition)/Economic Textbook Translation Series (Walter Enders) ebook netbook download free online reading
Title:Applied Econometrics (Time Series Analysis Original Book 4th Edition)/Economic Textbook Translation Series
Translated by Du Jiang
Publisher:Mechanical Industry Publishing House
Publication year: 2017-9-1
This book is a classic textbook in the field of econometrics, which is consistently organized throughout the book in a shallow-to-deep learning process, using real data examples to illustrate key concepts, which are not only complete and concise, but also very much application-oriented. The book illustrates the practical application of econometric methods through case studies, with few complex mathematical formulas. Topics throughout the book cover differential equations, smooth time series models, volatility modeling, models incorporating trends, multiequation time series models, cointegration and error correction models, and nonlinear time series models.
Walter Enders is a professor of economics at Alabama State University in the U.S. He received his Ph.D. in economics from Columbia University in New York in 1975. Dr. Enders’ recent research has focused on the development and use of time series models in economics and finance. He has published several papers in a number of journals, including ReviewofEconomyandStatistics,QuarterlyJournalofEconomics,JournalofInternationalEconomics. AmericanEconomicReview (sponsored by the American Economic Association), Journal of Business and Economic Statistics (sponsored by the American Statistical Association), and TheAmericanPoliticalScienceReview (sponsored by the American Political Science Association). He currently serves as an official editor of three journals in the field of international economics and as a policy advisor to the Ukrainian government. He also shared the National Academy of Sciences’ : ESTES Award with Todd Sandler for behavioral science research in the prevention of nuclear war. The award’s recognition states that “…basic research in the cognitive and behavioral sciences, using normative analysis or empirical methods, or a good combination of both, has deepened our understanding of crises related to nuclear war.” The National Academy of Sciences awarded them the prize for their “…common research on transnational terrorist activities, namely, the use of game theory and time-series analysis to demonstrate the cyclical and volatile nature of terrorist attacks in response to defensive countermeasures
Seek the pdf version of Mr. He Shuyuan’s “Applied Time Series Analysis”
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Applied Time Series Analysis
Author He Shuyuan edited 2003.09
Peking University Press 328 pages
Advanced Econometrics and Stata Applications, Second Edition pdf download read online, seek Baidu.com cloud resources
Advanced Econometrics and Stata Applications (Chen Qiang) ebook netbook download free online reading
Book Name:Advanced Econometrics and Stata Applications
Publisher:Higher Education Publishing House
Year of Publication:2014-4-1
《Economics, Management Postgraduate Teaching Book:Advanced Econometrics and Stata Applications (Second Edition)》draws more on the latest development of modern econometrics, and is comprehensive in its content, which, in addition to introducing traditional cross-sectional data, provides a comprehensive introduction to panel data (including long panel, dynamic panel, and nonlinear panel), time series (including VAR, unit root, cointegration), natural experiments, repeated cross-section data, GMM, self-help method, Monte Carlo method, quantile regression, threshold regression, nonparametric estimation, treatment effects, spatial measurement, duration analysis, Bayesian estimation, etc. are all analyzed in greater depth. This book tries to explain the measurement methods visually with vivid language, more illustrations and economic significance without losing the mathematical rigor. At the same time, combined with the most popular Stata measurement software in Europe and the United States, timely introduction of the corresponding Stata commands and examples, to provide readers with a “one-stop” service. This book is suitable for master’s and doctoral students and researchers in economics, management or social sciences in general higher education institutions. In order to facilitate the reader to learn advanced econometrics, “economics, management graduate teaching book: advanced econometrics and Stata applications (Second Edition)” in the content arrangement, assuming that the reader has learned calculus, linear algebra and probability statistics, but do not seek to learn undergraduate econometrics (learn to discuss a hundred good).
What are the several methods of applying time series analysis?
Time series analysis (Timeseriesanalysis) is a statistical method for dynamic data processing. The method is based on the theory of stochastic processes and methods of mathematical statistics, the study of random data series obeyed by the statistical laws, in order to be used to solve practical problems.
It includes general statistical analysis (e.g., autocorrelation analysis, spectral analysis, etc.), statistical modeling and inference, as well as optimal prediction, control and filtering of time series. Classical statistical analysis assumes that data series are independent, while time series analysis focuses on the interdependence of data series. The latter is actually a statistical analysis of the stochastic process of discrete indicators, so it can also be regarded as a component of stochastic process statistics. For example, the rainfall of the first month, the second month,……, and the Nth month of a certain region is recorded, and the rainfall of the future months can be forecasted by using the time series analysis method.
With the development of computer-related software, mathematical knowledge is no longer an empty theory, time series analysis is mainly based on mathematical statistics and other knowledge, the application of relevant mathematical and scientific knowledge in the relevant aspects of the application and so on.
A time series is usually composed of four elements: trend, seasonal variation, cyclical fluctuations and irregular fluctuations.
Trend: is a time series over a long period of time to show a continuous upward or continuous downward movement.
Seasonal variations: are cyclical fluctuations in the time series that are repeated within a year. It is the result of the influence of various factors such as climatic conditions, production conditions, holidays or people’s customs.
Cyclical fluctuations: is the time series show a non-fixed length of the cycle. Cyclic fluctuations of the cycle may last for a period of time, but unlike the trend, it is not a continuous change in a single direction, but the same rise and fall of the alternating fluctuations.
Irregular fluctuations: are random fluctuations in a time series after the trend, seasonal variations and cyclical fluctuations are removed. Irregular fluctuations are usually always mixed in the time series, resulting in a time series to produce a wavy or oscillating movements. A series that contains only random fluctuations is also called a smooth series.
The basic steps of time series modeling are:
①Observation, survey, statistics, sampling and other methods to obtain the observed system time series dynamic data.
② According to the dynamic data for the correlation diagram, correlation analysis, autocorrelation function. Correlation charts can show the trend of change and cycle, and can find the jump point and inflection point. A jump point is an observation that is inconsistent with other data. If the jump points are correct observations, they should be taken into account in modeling, and if they are anomalies, the jump points should be adjusted to the desired values. An inflection point, on the other hand, is a point at which a time series suddenly changes from an upward trend to a downward trend. If there is an inflection point, the time series must be modeled with a different model to fit the time series, such as the use of threshold regression model.
3) Identify a suitable stochastic model for curve fitting, i.e., use a generalized stochastic model to fit the observed data of the time series. For short or simple time series, trend models and seasonal models with errors can be used for fitting. For smooth time series, the generalized ARMA model (autoregressive sliding average model) and its special case autoregressive model, sliding average model or combined-ARMA model can be used to fit. The ARMA model is generally used when there are more than 50 observations. For non-stationary time series, the observed time series should be first differentiated into a stationary time series, and then the appropriate model should be used to fit this differentiated series.
An objective description of a system based on time series data from observations of the system using curve fitting.
When observations are taken from more than two variables, the changes in one time series can be used to explain the changes in the other time series, thus providing insight into the mechanism of a given time series.
Forecasting the Future
The ARMA model is generally used to fit a time series and predict the future values of that time series.
Decision making and control
Based on the time series model, the input variables can be adjusted to keep the system development process on target, i.e., necessary controls can be made when the process is predicted to deviate from the target.
Steps in Time Series Analysis
Time series analysis is a statistical technique used to predict future values, mainly by observing and studying the trends and patterns of data over time. The steps in time series analysis include data collection, data visualization and correlation analysis, model selection and fitting.
1, data collection:
First, the time series dynamic data of the observed system is obtained through observation, survey, statistics and sampling. This is the basis of the whole analysis process, the quality and accuracy of the data has a direct impact on the analysis results.
2, data visualization and correlation analysis:
Collected dynamic data will be plotted into a correlation diagram, correlation analysis, and derive the autocorrelation function. The correlation graph can visualize the trend and cycle of data changes, and also can find the jump point and inflection point.
The jump point is an observation that is inconsistent with other data. If the jump point is a correct observation, it should be taken into account in modeling, and if it is an anomaly, the jump point should be adjusted to the desired value. The inflection point, on the other hand, is the point at which the time series suddenly changes from an upward trend to a downward trend. If there is an inflection point, a different model must be used to fit the time series in segments when modeling.
3. Model Selection and Fitting:
Based on the second step, a suitable stochastic model is selected for curve fitting, i.e., a generalized stochastic model is used to fit the observed data of the time series.
For short or simple time series, trend and seasonal models plus errors can be used for fitting. For smooth time series, a generalized ARMA model (autoregressive sliding average model) and its special cases such as autoregressive model, sliding average model, or combined ARMA model can be used for fitting.
The ARMA model is generally used when there are more than 50 observations. For non-stationary time series, it is necessary to carry out the difference operation first, to be transformed into a stationary time series, and then use the appropriate model to fit this difference series.
The above are the basic steps of time series analysis, each step has its own unique role, one without the other. Through these steps, we can effectively analyze and forecast time series data to provide strong support for decision-making.