# Distributed lag time series model

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*2020-02-20 14:48*

time series variables. In the event of resolving this problem most cointegration techniques are wrongly applied, estimated, and interpreted. One of these techniques is the. Autoregressive Distributed Lag (ARDL) cointegration technique or bound cointegration technique. Hence, this study reviews the issues surrounding the wayThe challenge i am facing is predicting my predictor for future. For example, i used daily data for 2 year for model building. For forecasting into future, i also need values of lag variable, which i do not know. If i use 2 lags of daily data in the model, then in order to predict for future i will also need value of those lag variables as well. distributed lag time series model

Chapter 3: DistributedLag Models 37 To see the interpretation of the lag weights, consider two special cases: a temporary we change in x and a permanent change in x. Suppose that x increases temporarily by one unit in period t, then returns to its original lower level for periods 1 and all future periods. t For the temporary change, the time path of the changes in x looks like Figure 32: the