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Regression with Time Series Data Judge et al Chapter 15 and 16.

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Presentation on theme: "Regression with Time Series Data Judge et al Chapter 15 and 16."— Presentation transcript:

1 Regression with Time Series Data Judge et al Chapter 15 and 16

2 Distributed Lag

3 Polynomial distributed lag

4 Estimating a polynomial distributed lag

5 Geometric Lag If change in x t is sustained for another period: Impact Multiplier: change in y t when x t changes by one unit: Long-run multiplier:

6 The Koyck Transformation

7 Autoregressive distributed lag ARDL(1,1) Represents an infinite distributed lag with weights: ARDL(p,q) Approximates an infinite lag of any shape when p and q are large.

8 Stationarity The usual properties of the least squares estimator in a regression using time series data depend on the assumption that the variables involved are stationary stochastic processes. A series is stationary if its mean and variance are constant over time, and the covariance between two values depends only on the length of time separating the two values

9 Stationary Processes

10 Non-stationary processes

11 Non-stationary processes with drift

12 AR(1) Random walk Random walk with drift Deterministic trend Summary of time series processes

13 Trends Stochastic trend –Random walk –Series has a unit root –Series is integrated I(1) –Can be made stationary only by first differencing Deterministic trend –Series can be made stationary either by first differencing or by subtracting a deterministic trend.

14 Real data

15 Spurious correlation

16 Spurious regression VariableDFB ValueStd ErrorT ratioApprox prob Intercept114.2040400.542926.1620.0001 RW21-0.5262630.00963-54.6670.0001 R 2 0.7495 D-W 0.0305

17 Checking/testing for stationarity Correlogram –Shows partial correlation observations at increasing intervals. –If stationary these die away. Box-Pierce Ljung-Box Unit root tests

18 Unit root test

19 Dickey Fuller Tests Allow for a number of possible models –Drift –Deterministic trend Account for serial correlation Drift Drift against deterministic trend Adjusting for serial correlation (ADF)

20 Table 16.4 Critical Values for the Dickey-Fuller Test Model1%5%10% 2.56 1.94 1.62 3.43 2.86 2.57 3.96 3.41 3.13 Standard critical values 2.33 1.65 1.28 Critical values

21 Example of a Dickey Fuller Test

22 Cointegration In general non-stationary variables should not be used in regression. In general a linear combination of I(1) series, eg: is I(1). If e t is I(0) x t and y t are cointegrated and the regression is not spurious e t can be interpreted as the error in a long- run equilibrium.

23 Example of a cointegration test Model1%5%10% 3.90 3.34 3.04

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