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1 |
Introduction to time series, basic concepts, and applications.
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2 |
Characteristics of time series, trend, seasonality, stationarity, and their analyses.
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3 |
Basic statistical tools: Autocorrelation and partial autocorrelation functions.
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4 |
Autocorrelation analyses and graphical tests in time series.
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5 |
Portmanteau tests in time series (Box-Pierce, Ljung-Box) and other diagnostic tests.
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6 |
Time series models: Autoregressive (AR) processes.
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7 |
Time series models: Moving Average (MA) processes.
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8 |
MIDTERM EXAM
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9 |
ARMA (Autoregressive Moving Average) processes.
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10 |
Seasonal Box-Jenkins ARIMA models.
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11 |
Model selection, parameter estimation, and diagnostic checks.
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12 |
Forecasting methods and applications in time series.
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13 |
Cointegration concept, cointegration tests (Engle-Granger, Johansen).
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14 |
Error correction models, seasonal integration, and cointegration applications.
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15 |
VAR (Vector Autoregressive) models and causality tests.
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16 |
FINAL EXAM
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17 |
FINAL EXAM
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18 |
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19 |
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20 |
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