The oil prices are measured in Candian dollars per cubic meter. So, the price data that we'll use are weekly (end-of-week), for the 4 January 2000 to 16 July 2013, inclusive.
Although crude oil prices are recorded daily, the gasoline prices are available only weekly. More specifically, the crude oil price is for Canadian Par at Edmonton and the gasoline price is that for the Canadian city of Vancouver. Here, I'll take you through another example of ARDL modelling - this one involves the relationship between the retail price of gasoline, and the price of crude oil. What's now available is a full-blown ARDL estimation option, together with bounds testing and an analysis of the long-run relationship between the variables being modelled. It certainly deserves a post, so here goes!įirst, it's important to note that although there was previously an EViews "add-in" for ARDL models (see here and here), this was quite limited in its capabilities. This is a great feature, and I just know that it's going to be a "winner" for EViews. So, it's great to see that EViews 9 (now in Beta release - see the details here) incorporates an ARDL modelling option, together with the associated "bounds testing". From here, select FRED Database from the Database/File Type dropdown, and hit OK.My previous posts relating to ARDL models ( here and here) have drawn a lot of hits. Next, fetch the unemployment rate data from the FRED database by clicking on File/Open/Database. Under Frequency select Monthly, and set the Start date to 2006M12 and the End date to 2013M12, and hit OK. Turning to data analysis, in EViews, create a new monthly workfile.
Usually, you will not have to touch this setting since EViews populates this field by searching your system for the install directory.įinally, please note that as of writing, the analysis that follows was tested with Python version 3.6.8 and PyFlux version 0.4.15. and on the left tree select External program interface and ensure that Home Path is correctly pointing to the directory where Python is installed. Specifically, in EViews, go to Options/General Options. Next, make sure that the path to Python is specified in your EViews options. From there, issue the following commands: One (certainly not the only) way to install said packages, is to open up a command prompt on your system and navigate to the directory where Python was installed this is usually C:\Users\USER_NAME\AppData\Local\Programs\Python\Python36_64 if you have a 64-bit version. We will then compare our findings.īefore getting started, please make sure that you have Python 3 installed from on your system, and that you also have the following Python packages installed: Accordingly, we will fit a GARCH model in EViews, transfer our data over to Python, and estimate a GAS model using the Python package PyFlux. Note here that while EViews can estimate numerous (G)ARCH models, it cannot yet natively estimate GAS models. Since the GAS model above reduces to the GARCH model when the conditional distribution $ p(\cdot) $ is Gaussian and the time varying parameter is the volatility of the process, we would like to compare the estimates from the GAS model to those generated by EViews' internal GARCH estimation. In particular, the model assumes an input vector of random variables at time $ t $, say $ \pmb $, at each point in time, effectively tracing the evolution of unemployment volatility for the period under consideration. GAS models are agnostic as to the type of data under consideration as long as the score function and the Hessian are well defined.
The family has now come to be known as the generalized autoregressive score (GAS) family or model. In this regard, Creal, Koopman, and Lucas (2013) and Harvey (2013) proposed a novel family of time-varying parametric models estimated using the familiar maximum likelihood framework with the score of the conditional density function driving the updating mechanism. Nevertheless, many of these specifications are often difficult to estimate, such as the family of stochastic volatility models, among which GARCH is a canonical example. Historically, time varying parameters have received an enormous amount of attention and the literature is saturated with numerous specifications and estimation techniques. macroeconomic data on the unemployment rate to fit a GARCH model in EViews, transfer the data over and estimate a GAS model equivalent of the GARCH model in Python, transfer the data back to EViews, and compare the results. To demonstrate this feature, we will use U.S. This means that workflow can begin in EViews, switch over to Python, and be brought back into EViews seamlessly. Starting with EViews 11, users can take advantage of communication between EViews and Python.