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INTRODUCTION TO TIME SERIES

STAT 349
과목 설명

Autocorrelation; stationarity and non-stationarity; heteroscedasticity; dynamic models; auto-regressive and moving average models; identification and fitting; forecasting; seasonal adjustment; applications for financial time series, social sciences and environmental studies.

선수과목

STAT 333 , STAT 340 , graduate/professional standing, or declared in Statistics VISP

충족 요건

This course does not satisfy any prerequisites.

학점

미보고

개설 시기

미보고

평점
3.31

0.13% 과거 데이터 대비

수료율
95.92%

-0.82% 과거 데이터 대비

A 비율
42.86%

9.48% 과거 데이터 대비

학급 규모
49

12.83% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

APPLIED TIME SERIES ANALYSIS, FORECASTING AND CONTROL I

STAT 701

Theory and application of discrete time series models illustrated with forecasting problems. Principles of iterative model building. Representation of dynamic relations by difference equations. Autoregressive integrated Moving Average models. Identification, fitting, diagnostic checking of models. Seasonal model application to forecasting in business, economics, ecology, and engineering used at each stage, which the student analyzes using computer programs which have been specially written and extensively tested.

TOPICS IN APPLIED ECONOMETRICS

ECON 718

Applied time series methods.

ADVANCED FINANCIAL STATISTICS

STAT 801

Statistical theory and methodology for modern financial data. Topics include financial stochastic models based on time series and stochastic calculus, modern statistical inference, and statistical learning for financial data as well as their applications to financial problems.

FINANCIAL ECONOMETRICS

ECON 724

Introduction to econometric analysis of financial time series. Covers linear econometric time series models, multivariate time series, volatility estimation, and estimating and testing financial econometric models.

TIME SERIES ANALYSIS

POLISCI 917

This course introduces students to time series methods and applications, including ARMA models, error corrections models and reduced form specifications. Course also discusses co-integration and fractional integration.