Richard Hardy
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Expectation VS forecast
4 votes

To avoid confusion inherent in colloquial expressions, it is convenient to define and analyze expectations and forecasts using mathematics and statistics. An expectation is then the expected value $\...

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Applying ensemble modelling to VAR models
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4 votes

Emsemble learning is beneficial in forecasting where statistical adequacy of any model in the ensemble is of limited importance. However, when models are used for inference, statistical adequacy is ...

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Many or few variables when testing for Granger causality?
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4 votes

The implication is, the model should be adequately specified, without omitting relevant variables. Once the model is adequately specified, Granger causality will be easier to establish if the model ...

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When do the sign and magnitude of coefficient of variable of interest matter if it is insignificant?
3 votes

They matter whenever you are interested in learning from the data. If you do not have prior knowledge about the effect you are studying, the point estimate alongside its confidence interval and ...

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In VAR models, do variations in the variables come solely from shocks?
3 votes

Zero shocks do not imply zero variations in a VAR model. In a stationary VAR model, if the shocks starting from a time period $t=\tau$ are all permanently zero, the variables will converge to their ...

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References for particular definitions of risk and uncertainty
3 votes

I finally found a reference that defines the terms risk and uncertainty the way I do. Sven Ove Hansson "Decision Theory: A Brief Introduction" (1994) writes on p. 27-28: In one of the most ...

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Misspecified autoregressive models
3 votes

As Neeraj has explained correctly, omitting a variable will lead to inconsistent estimates of the included variables. They will be biased both in a finite sample and asymptotically because the ...

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Structural VAR and Granger Causality
3 votes

Every structural VAR (SVAR) model, e.g. $$ B_0 y_t = B_1 y_{t-1} + u_t $$ has an equivalent reduced form (VAR), e.g. \begin{aligned} y_t &= B_0^{-1} B_1 y_{t-1} + B_0^{-1} u_t \\ &= A_1 y_{...

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Examples of the use of Vector Autoregressive Models
2 votes

VAR models are typically used in macroeconomics, among other fields. Lütkepohl "New Introduction to Multiple Time Series Analysis" (2005) (a textbook) contains an in-depth presentation of ...

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Annual data VS Monthly data VS Quarterly data for a VAR model
2 votes

For example, the response of $y_t$ to a shock in $x_t$ should be expected to be different at monthly, quarterly and annual frequencies, right? Generally (and usually), yes. Or it is that the results ...

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Differences between GMM and MSM?
2 votes

While in GMM one uses theoretical analytical moments, in MSM one uses simulated theoretical moments instead. For GMM, [t]he method requires that a certain number of moment conditions were ...

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Recommended tests do detect breaks in time series
2 votes

Impulse indicator saturation (IIS) (Santos, 2008) and step indicator saturation (SIS) (Doornik et al., 2013) are relatively new and powerful methods for change/break detections. They are used, for ...

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Corporate Finance Book Recommendations
1 votes

I like some bits of Van der Wijst "Finance: A Quantitative Introduction" (2013, Cambridge University Press). I have not read the whole book, but I found some examples in it to be very clear ...

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Relevance of change in Inventory in calculation of Free Cash Flow
1 votes

When you manufacture a product, you incur some production cost. However, you do not account for this production cost in the $\text{Cost}$ line of the income statement until you actually sell the ...

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