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Writing and estimation of a dynamic factors model, Markov switching VAR and latent factor model.



I have been developing an investigation that seeks to explain the behavior of the default ratio of 3 types of loans: consumer, mortgage and business based on macroeconomic indicators (GDP, exchange rate, employment, inflation, income, etc.). The project consists of writing the theoretical part and estimating econometric models: Dynamic factor model, Markov Siwtching VAR and lantent factor model. Below I explain in detail what should be done:

1. Dynamic factor model:

The codes are available in STATA ([login to view URL]) and a challenge must be made between the different specifications of a factorial model. In this sense, a static specification must be evaluated to then introduce the independent variables through the dynamic specification. The criteria for choosing the right model are entirely statistical. All the results must be shown in summary tables and the codes must allow to export to an excel file.

2. Bayesian Markov Siwtching VAR

Evaluate different specifications of the Markovian model and choose according to statistical criteria which model to use. All the results must be shown in summary tables and the codes must allow to export to an excel file.

There are a codes and example of estimations: [login to view URL]

3. SUR model

Evaluate different specifications of the model and choose according to statistical criteria which model to use. Reference paper: [login to view URL] (practically involves replicating the paper). All the results must be shown in summary tables and the codes must allow to export to an excel file.

Stress scenarios:

The first two models must allow obtaining the variance and covariance matrix, with the aim of carrying out stress exercises at the default ratio through Monte Carlo simulations in scenarios, for example, of a fall in the annual growth of gross domestic product. An example of what was previously commented is the paper: [login to view URL] It describes the criteria used for stress tests.

For the third model in the reference paper the stress exercise is mentioned.

The data:

The data is in the attached Excel file (Sheet1). The description of the variables is on the “description” sheet. It is monthly data and the dependent variables are: npl_consumer, npl_mortgages and npl_business. The independent variables are:

gdp_growth_12, employment_growth_12, consumer_price_index_growth_12, real_active_interest_rate_12, exchange_rate_growth_12, debt_growth_12_consumer, debt_growth_12_mortgages, debt_growth_12_business

debt_growth_12_system, income_growth_12

The codes:

The codes could be in Stata and / or R and / or Python and / or Eviews or a combination of them, and should have as many comments as possible, to avoid additional queries. You could use markdown notebooks being very detailed with well structured codes or dofiles.

Finally, the deliverable structure will be:

1. Empirical Strategy

1.1. Data

Graphical analysis of dependent versus independent variables.

a. Unit root test

Diferents unit root test have been reproted. Some examples are presented on pages 21-23 of this document:

[login to view URL]

1.2. Econometric models: In this part it is necessary to make a description of the model and the theoretical support of the model.

a. Dynamic factor models

b. Markov Siwtching VAR model

c. SUR model

d. Montecarlos simulation

Theoretical description of the methodology.

2. Results

2.1. Dynamic factor models:

2.2. Markov siwtching VAR:

2.3. Dynamic factor models:

2.4. Monte Carlo simulation under stress scenarios

For all models the stress scenarios are:

GDP growth rate drop

exchange rate increase

fall in the growth rate of the employment index

3. Conclusions:

Skills: Statistics, Statistical Analysis, R Programming Language, Econometrics, Economics

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