Data filtering and Calibration Stata tutorial

Data filtering and Calibration Stata DSGE tutorial. In this tutorial, I will be talking about data filtering and calibration in Stata for DSGE models. This video is part of the Free DSGE course (Video 6).
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Filtering the data will decompose the Real GDP into trends and cycles. Recall we are studying business cycles. There are several filters (Kalman, Butterworth), but we will start with the HP Filter.

The Hodrick-Prescott (HP) filter is a data-smoothing technique commonly applied to remove short-term fluctuations associated with the business cycle. Removal of these short-term fluctuations reveals long-term trends. Hodrick-Prescott introduced the filter in the early '80s and has been very popular since the '90s. Despite its popularity, there are some drawbacks with the HP filter (Read: "Why you should never use the Hodrick-Prescott filter" Hamilton(2018)).

The HP filter allows us to decompose a time series into a trend and cycle. We can obtain the cyclical (GAP) and trend component when applied to Real GDP. The HP-Filter smoothing parameter is commonly set to 1600 for quarterly data, 100 for yearly data and 14,400 for monthly data.

On the other hand, we will review calibrating a DSGE model. Typically, dynamic stochastic general equilibrium models count with many parameters which need to be calibrated. Calibration implies assigning values to the model parameters to match real data. The parameter values are set to target a specific value in the model's steady state (example: Calibrate leisure share parameter so, in the steady-state, the families rest 20% of their time). Next, data from National Accounts is helpful and allows us to calibrate the model to match information from the real economy. Finally, other research papers. There is consensus on many parameter values, and we will often use those values.

Are you ready? Let's get into it!

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*** IMPORTANT: The present video is class number 6 of my free DSGE course. In this course I teach you how to solve a real business model step by step and write the dynamic equations in the software. Finally, we compute the steady state, impulse response functions and out of sample forecast**
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🕘 Timestamps:
🎬 In this video the following analysis is performed:
👋 Introduction 0:00
📊 HP Filter Overview 1:12
📊 HP Filter Example 3:17
📊 HP Filter in Stata 4:47
📊 DSGE Model Calibration 9:30
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