TAILIEUCHUNG - Lecture Undergraduate econometrics, 2nd edition - Chapter 16: Regression with time series data

The analysis of time series data is of vital interest to many groups, such as macroeconomists studying the behavior of national and international economies, finance economists who study the stock market, agricultural economists who want to predict supplies and demands for agricultural products. We introduced the problem of autocorrelated errors when using time series data in chapter 12. In chapter 15 we considered distributed lag models. In both of these chapters we made implicit stationary assumptions about the time series data. | Chapter 16 Regression with Time Series Data The analysis of time series data is of vital interest to many groups such as macroeconomists studying the behavior of national and international economies finance economists who study the stock market agricultural economists who want to predict supplies and demands for agricultural products. We introduced the problem of autocorrelated errors when using time series data in chapter 12. In chapter 15 we considered distributed lag models. In both of these chapters we made implicit stationary assumptions about the time series data. In the context of the AR 1 model of autocorrelation et pet _1 vt we assumed that pl 1. In the infinite geometric lag model yt a ỵp .xt_ et where p. PỘ we assumed 1. Slide Undergraduate Econometrics 2 Edition-Chapter 16 These assumptions ensure that the time series variables in question are stationary time series. However many of the variables studied in macroeconomics monetary economics and finance are nonstationary time series. The econometric consequences of nonstationarity can be quite severe leading to least squares estimators test statistics and predictors that are unreliable. Moreover the study of nonstationary time series is one of the fascinating recent developments in econometrics. In this chapter we examine these and related issues. Slide Undergraduate Econometrics 2 Edition-Chapter 16 Stationary Time Series Let yt be an economic variable that we observe over time. Examples of such variables are interest rates the inflation rate the gross domestic product disposable income etc. The variable yt is random since we can not perfectly predict it. We never know the values of these variables until they are observed. The economic model generating yt is called a stochastic or random process. We observe a sample of yt values which is called a particular realization of the stochastic process. It is one of many possible paths that the stochastic process could have taken. The usual .

• Quản trị kinh doanh
• Undergraduate econometrics
• Lecture Undergraduate econometrics
• Kinh tế lượng
• Time series data
• Stationary time series
• Spurious regressions
• Econometric model
• Experimental data
• Statistical inference
• Random variables
• Probability concepts
• Probability density
• Linear regression model
• Economic model
• The least squares estimators
• Least squares estimators
• Simple regression model
• Interval estimation
• Hypothesis testing
• Simple linear regression model
• Coefficient of determination
• Regression analysis
• Multiple regression model
• Model specification
• The least squares estimator
• Hypothesis tests
• Collinear economic variables
• Intercept dummy variables
• Slope dummy variables
• Dummy variables
• Nonlinear models
• Interaction variables
• Logistic growth curve
• Nature of heteroskedasticity
• Proportional heteroskedasticity
• Distributed lag models
• Finite distributed lag models
• Geometric lag