privacy policy

Privacy Policy

Last Updated: 21 Sept 2023

Introduction

Welcome to dimasmukhlas.com. We are committed to protecting your privacy and providing a safe online experience for all our users. This Privacy Policy outlines the information we may collect from you when you visit our website and how we use, disclose, and safeguard that information. You consent to the practices described in this Privacy Policy by accessing or using our website.

Information We Collect

Personal Information

We may collect personal information you voluntarily provide us when you use our website. This may include:

Your name
Email address
Postal address
Phone number
Any other information you choose to provide

Non-Personal Information

We may also collect non-personal information automatically when you access our website, such as:

IP address
Browser type and version
Operating system
Date and time of access
Pages you visit
Links you click

How We Use Your Information

We use the information we collect for the following purposes:

To Improve Our Website: We may use your information to analyze how our website is used and make improvements to enhance your experience.
To Respond to Your Requests: If you contact us with inquiries or requests, we may use your information to respond to you.
To Send Emails: If you provide your email address, we may send you periodic updates, newsletters, or other communications related to our website and the more than finance world. You can opt out of these emails at any time.
To Protect Our Rights: We may use your information to protect our rights, privacy, safety, or property, or that of others.

Disclosure of Your Information

Without your consent, we do not sell, trade, or otherwise transfer your personal information to third parties. However, we may disclose your information in the following circumstances:

Legal Compliance: We may disclose your information when required by law or in response to lawful requests by public authorities.
Business Transfers: If we are involved in a merger, acquisition, or sale of all or a portion of our assets, your information may be transferred as part of that transaction.

Security

We take reasonable measures to protect your personal information from unauthorized access, disclosure, alteration, and destruction. However, please be aware that no data transmission over the Internet or method of electronic storage is entirely secure. While we strive to use commercially acceptable means to protect your information, we cannot guarantee security.

Your Choices

You have the following choices regarding your information:

Access and Update: You may access and update your personal information by contacting us.
Opt-Out: You can opt out of receiving promotional emails from us by following the email instructions or contacting us directly.

Changes to This Privacy Policy

We may update this Privacy Policy to reflect changes to our information practices. We encourage you to review this page periodically for the latest information on our privacy practices.

Contact Us

If you have any questions or concerns about this Privacy Policy or our data practices, please contact us at [email protected].

News

Some notes about python and Zen of Python

Recent news

Explore Python syntax

Python is a flexible programming language used in a wide range of fields, including software development, machine learning, and data analysis. Python is one of the most popular programming languages for data professionals, so getting familiar with its fundamental syntax and semantics will be useful for your future career. In this reading, you will learn about Python’s syntax and semantics, as well as where to find resources to further your learning.

Understanding Tier 1 Capital, common equeity tier 1 Capital, and risk weighted asset through asking the right question

Recent news

Week 1 to week 6 in learning VAR

Recent news

Week 1: Introduction to Time Series Analysis

Overview of Time Series Data:

python

# Load necessary libraries import pandas as pd import matplotlib.pyplot as plt # Load and visualize time series data data = pd.read_csv('your_time_series_data.csv') plt.figure(figsize=(10, 6)) plt.plot(data['Date'], data['Value']) plt.title('Time Series Data') plt.xlabel('Date') plt.ylabel('Value') plt.show()

Time Series Components:

python

# Decomposition of time series data from statsmodels.tsa.seasonal import seasonal_decompose result = seasonal_decompose(data['Value'], model='additive', period=12) result.plot() plt.show()

Statistical Properties of Time Series:

python

# Stationarity check using Augmented Dickey-Fuller test from statsmodels.tsa.stattools import adfuller result = adfuller(data['Value']) print('ADF Statistic:', result[0]) print('p-value:', result[1]) print('Critical Values:', result[4])

Week 2: Fundamentals of Vector Autoregression (VAR)

Introduction to VAR Model:

python

# Import VAR model from statsmodels from statsmodels.tsa.vector_ar.var_model import VAR # Create VAR model model = VAR(data)

Estimation and Interpretation:

python

# Fit the VAR model results = model.fit() # Summary of the VAR model print(results.summary())

Granger Causality and Lag Selection:

python

# Granger causality test from statsmodels.tsa.stattools import grangercausalitytests max_lag = 4 # maximum lag to test causality granger_test_result = grangercausalitytests(data, max_lag)

Week 3: Implementing VAR in Python

Building a VAR Model in Python:

python

# Implementing VAR model using statsmodels library model = VAR(data) results = model.fit(maxlags=4) # fitting the model with selected maximum lag

Visualization and Forecasting with VAR:

python

# Plotting results and visualizing time series forecasts results.plot_forecast(10)

Implementing Impulse Response Analysis:

python

# Impulse Response Analysis irf = results.irf(10) irf.plot(orth=False)

This breakdown provides code snippets for key concepts covered in the weekly plan. For the complete course, you would expand upon these snippets, incorporate explanations, provide datasets, and encourage students to apply these techniques to various time series datasets and financial data, ensuring they understand the theory and practical implementation of VAR models in Python.

privacy policy

Privacy Policy

Introduction

Information We Collect

Personal Information

Non-Personal Information

How We Use Your Information

Disclosure of Your Information

Security

Your Choices

Changes to This Privacy Policy

Contact Us

Explore Python syntax

3 days, 22 hours ago

2 weeks, 5 days ago

Week 1: Introduction to Time Series Analysis

Overview of Time Series Data:

Time Series Components:

Statistical Properties of Time Series:

Week 2: Fundamentals of Vector Autoregression (VAR)

Introduction to VAR Model:

Estimation and Interpretation:

Granger Causality and Lag Selection:

Week 3: Implementing VAR in Python

Building a VAR Model in Python:

Visualization and Forecasting with VAR:

Implementing Impulse Response Analysis:

3 weeks, 3 days ago

3 weeks, 3 days ago

3 weeks, 5 days ago

3 weeks, 5 days ago

3 weeks, 5 days ago

3 weeks, 5 days ago