Applied Time Series Interactive Lab

Interactive simulators, diagnostic plots, and exam visuals for time-series econometrics. Tweak parameters with sliders, see the impact on charts and formulas immediately, and test yourself with chapter quizzes.

26chapters
8parts (Parts I–VIII)
26+interactive labs
100+quiz questions

Part I — Foundations

CHAPTER 1

Why Time Series Matter

Order matters: trend + cycle + noise components shape every series.

Simulator CHAPTER 2

Returns and Financial Data

Prices drift; returns are usually stationary and more model-friendly.

Simulator CHAPTER 3

Probability and Statistics

Same mean can hide very different risk — distributions and tails matter.

Diagnostic CHAPTER 4

Visualizing Time Series

Always plot before modeling — spot trend, seasonality, breaks, outliers.

Exam Lab

Part II — Smoothing & Filters

CHAPTER 5

Smoothing and Trend Estimation

Moving averages reveal trend but introduce lag.

Simulator CHAPTER 6

Trading Indicators as Filters

MA crossover signals — educational illustration of lagged filters.

Simulator

Part III — Dependence, Stationarity, Unit Roots

CHAPTER 7

Randomness and Dependence

A random-looking series can still be strongly autocorrelated.

Simulator CHAPTER 8

Stationarity

Stable mean and variance over time — the foundation of everything.

Diagnostic CHAPTER 9

ACF and PACF

Pattern recognition: AR cuts PACF, MA cuts ACF.

Exam Lab CHAPTER 10

Unit Roots and Differencing

Differencing turns a unit root into a stationary process.

Diagnostic

Part IV — ARIMA Modeling

CHAPTER 11

AR(p) Models

Persistence via past values — φ controls memory and oscillation.

Simulator CHAPTER 12

MA(q) Models

Shock propagation — finite memory of past errors.

Simulator CHAPTER 13

ARMA Models

Persistence + shock effects combined.

Simulator CHAPTER 14

ARIMA Models

Difference first, then model — Box-Jenkins workflow.

Simulator

Part V — Forecasting

CHAPTER 15

Forecasting Methods

Forecasts decay to mean (stationary) or stay put (RW).

Forecast Lab CHAPTER 16

Forecast Evaluation

Bias, RMSE, MAE, MAPE, Theil U — pick by loss function.

Forecast Lab

Part VI — Time-Series Regression

CHAPTER 17

Spurious Regression

Two unrelated random walks can produce high R² and significant t.

Exam Lab CHAPTER 18

Dynamic Regression & ARDL

Short-run vs long-run effects via lagged terms.

Simulator CHAPTER 19

Granger Causality

Predictive content, not philosophical causality.

Exam Lab CHAPTER 20

Cointegration

I(1) variables with a stationary linear combination.

Diagnostic CHAPTER 21

Error Correction Models

Speed of adjustment back to long-run equilibrium.

Simulator

Part VII — Multivariate Time Series

CHAPTER 22

VAR Models

Variables jointly endogenous — use IRFs and FEVD to interpret.

Simulator CHAPTER 23

Impulse Response Functions

Trace the dynamic ripple of a one-time shock through time.

Simulator CHAPTER 24

VECM

VAR + cointegration → error-correction in a system.

Diagnostic

Part VIII — Volatility Models

CHAPTER 25

ARCH Models

Volatility today depends on yesterday's squared shock.

Simulator CHAPTER 26

GARCH Models

Volatility has memory — α + β controls persistence.

Simulator

How to use this lab

  1. Click any chapter card to open its interactive lab.
  2. Tweak parameters using sliders, dropdowns, and toggles on the left.
  3. Watch charts and live readouts update in real time.
  4. Read the formula box, diagnostic notes, and Pro Tip: What to Avoid card.
  5. Take the mini-quiz at the bottom — your score is saved per chapter.
  6. Use the Key Takeaways table for last-minute revision.
Disclaimer: Educational only. Simulators are textbook-simple implementations. The trading-indicator demo (Ch 6) is illustrative and not investment advice.

Credits: Built for master's-level Applied Time Series Course. Special thanks to Professor Dr. Yong Yoon (Chulalongkorn University), whose teachings and insights inspired the creation of this interactive lab.