Question

Topic: Time Series Finite Distributed Lag Model(FDL) Use the fdl_model_data located in the Final Module of the Canvas to answer the following question: Suppose we are studying the impact of a certain policy (e.g., a marketing campaign) on sales over time. We are interested in how the implementation of this policy in a given period affects sales not only in the same period but also in subsequent periods. We will model this effect up to the third lag (t-1, t-2, t-3). The dataset will contain the following variables: \begin{itemize} \item Year: The year of observation. \item Policy: A binary variable indicating whether the policy was implemented in that year (1 if implemented, 0 otherwise). \item Sales: The amount of sales in that year. \end{itemize} The Finite Distributed Lag model will be specified as follows: Sales$_t = \beta_0 + \beta_1 Policy_t + \beta_2 Policy_{t-1} + \beta_3 Policy_{t-2} + \beta_4 Policy_{t-3} + u_t$ where: \begin{itemize} \item Sales$_t$ is the sales in year t. \item Policy$_t$, Policy$_{t-1}$, Policy$_{t-2}$, and Policy$_{t-3}$ are the current and lagged values of the policy variable. \item $\beta_0$, $\beta_1$, $\beta_2$, $\beta_3$, and $\beta_4$ are the coefficients to be estimated. \item $u_t$ is the error term. \end{itemize} This dataset includes the original Policy variable and its lags up to three years (Policy_Lag1, Policy_Lag2, Policy_Lag3). The Sales column represents the sales for each year. A. Perform the FDL regression analysis on this dataset and interpret the results B. Provide your Python Codes.

          Topic: Time Series Finite Distributed Lag Model(FDL)
Use the fdl_model_data located in the Final Module of the Canvas to answer the following
question:
Suppose we are studying the impact of a certain policy (e.g., a marketing campaign) on sales
over time. We are interested in how the implementation of this policy in a given period affects
sales not only in the same period but also in subsequent periods. We will model this effect up to
the third lag (t-1, t-2, t-3).
The dataset will contain the following variables:
\begin{itemize}
    \item Year: The year of observation.
    \item Policy: A binary variable indicating whether the policy was implemented in that year (1 if
    implemented, 0 otherwise).
    \item Sales: The amount of sales in that year.
\end{itemize}
The Finite Distributed Lag model will be specified as follows:
Sales$_t = \beta_0 + \beta_1 Policy_t + \beta_2 Policy_{t-1} + \beta_3 Policy_{t-2} + \beta_4 Policy_{t-3} + u_t$
where:
\begin{itemize}
    \item Sales$_t$ is the sales in year t.
    \item Policy$_t$, Policy$_{t-1}$, Policy$_{t-2}$, and Policy$_{t-3}$ are the current and lagged values of the policy
    variable.
    \item $\beta_0$, $\beta_1$, $\beta_2$, $\beta_3$, and $\beta_4$ are the coefficients to be estimated.
    \item $u_t$ is the error term.
\end{itemize}
This dataset includes the original Policy variable and its lags up to three years (Policy_Lag1,
Policy_Lag2, Policy_Lag3). The Sales column represents the sales for each year.
A. Perform the FDL regression analysis on this dataset and interpret the results
B. Provide your Python Codes.

Topic: Time Series Finite Distributed Lag Model(FDL)
Use the fdlmodeldata located in the Final Module of the Canvas to answer the following
question:
Suppose we are studying the impact of a certain policy (e.g., a marketing campaign) on sales
over time. We are interested in how the implementation of this policy in a given period affects
sales not only in the same period but also in subsequent periods. We will model this effect up to
the third lag (t-1, t-2, t-3).
The dataset will contain the following variables:

* Year: The year of observation.

* Policy: A binary variable indicating whether the policy was implemented in that year (1 if
implemented, 0 otherwise).

* Sales: The amount of sales in that year.

The Finite Distributed Lag model will be specified as follows:
Salest = β0 + β1 Policyt + β2 Policyt-1 + β3 Policyt-2 + β4 Policyt-3 + ut
where:

* Salest is the sales in year t.

* Policyt, Policyt-1, Policyt-2, and Policyt-3 are the current and lagged values of the policy
variable.

* β0, β1, β2, β3, and β4 are the coefficients to be estimated.

* ut is the error term.

This dataset includes the original Policy variable and its lags up to three years (PolicyLag1,
PolicyLag2, PolicyLag3). The Sales column represents the sales for each year.
A. Perform the FDL regression analysis on this dataset and interpret the results
B. Provide your Python Codes.

Added by Aaron L.

Question

Please give Ace some feedback