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|Topic||What we offer|
|Left out variables||We provide answers on how important variables can be missed. We provide solutions for a correct mode with Xk+1t and solutions for a model without Xk+1t.|
|Irrelevant variables||Irrelevant variables are variables that should not have been in the model. We cover correct models and models with an irrelevant variable.|
Tests for model misspecification
Model misspecification tests are carried out using software. The two tests are:
- Davidson and Mackinnon J tests – This model misspecification test is carried out on non-nested model specification.
- The Ramsey regression specification error test – a misspecification test for linear regression models
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Unit Root process
A unit root is a stochastic trend in a time series. A time series with a root shows a systematic pattern that is unpredictable. If a variable has a unit root, the shock to the variable does not die. A unit process unit is written as a sum of past shocks plus a / (0) unit root tests are stationary tests in a time series. Time series have a stationary if time shift does not cause a change in the distribution shape. Unit roots are causes of non-stationary. Unit root tests include:
- Dickey-Fuller test
- Elliot-tothenberg-stock test
- Schmidt-Philips test
- Phillips-perron test
If variables are cointegrated, they are individually non-stationary, and they move together in a long-run relationship. Cointegration is the existence of a long-run relationship between economic theory and variables. Therefore it can be described as a long-run property of variables.
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Even though convergence is broad and challenging, we have an experienced team ready to help with econometrics assignments in this field. Some of the topics we will help you with include:
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Our tutors are available 24/7 to ensure that your work is completed before the deadline. We cover all ARMAX model related topics, including but not limited to:
- Exponential smoothing – This is a technique that uses the exponential window function is smoothing time series data. It is a popular function used to smooth data in signal processing.
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- Finite impulse response – FIR is a filter whose impulse response is of finite duration. This is because it settles to zero within a finite time.
- Infinite impulse response – Infinite impulse response applies to many linear time-invariant systems that have an impulse response that does not become zero within a specific period, therefore, continuing indefinitely.
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