**Econometrics Beat: Dave Giles' Blog**, and kindly contributed to R-bloggers)

After my previous ~~rant to~~ **discussion with her** about this matter, I’ve tried to stay on the straight and narrow. It’s better for my blood pressure, apart from anything else! Anyway, somehow how we got back this topic, and she urged me to get some related issues off my chest. This *is* therapy, after all!

Right at the outset, let me state quite categorically that lots of people estimate semi-logarithmic regressions for the wrong reasons. I’m not condoning what they do. That’s their problem – I have enough of my own! However, if they’re going to insist on doing this, then I’m going to insist that they be consistent when it comes to using their estimated model for forecasting purposes!

I mean, we wouldn’t use an inconsistent *estimator*, would we? So why should we put up with an inconsistent *modeller*?

Let me tell you all about it………

Suppose that we’re using a regression model of the form

_{t}) = β

_{1}+ β

_{2}x

_{2t}+ β

_{3}x

_{3t}+ ……+ β

_{k}x

_{kt}+ ε

_{t},

*natural*logarithm. One or more of the regressors may also be in logarithmic form, but that’s irrelevant for the purposes of this post. We just need them to be non-random.

_{t})]* = b

_{1}+ b

_{2}x

_{2t}+ ….. + b

_{k}x

_{kt}, where b

_{i}is the OLS estimator for β

_{i}(i = 1, 2, ……, k).

*original data*– that is, in terms of y itself, rather than log(y).

_{t}* = exp[log(y

_{t})]* . Right?

Actually, No!

_{t}, in the regression model. That was bad – those assumptions are really just as important as the specification of the structural part of the equation. So, let’s tidy things up a bit. Specifically, let’s assume that the errors have a zero mean, and are homoskedastic and serially independent. Crucially, let’s also assume that they are

*Normally distributed*.

_{t}is Normally distributed, then log(y

_{t}) is also Normally distributed, assuming that the regressors are non-random. In turn, this implies that y

_{t}itself must follow a

*Log-Normal*distribution.

^{2}], then Y = exp[X] ~ Log-N[(m , v], where “m” and “v” are the mean and variance of the Log-Normal distribution. Its well-known that:

- m = exp[μ + σ
^{2}/ 2] - v = [exp(σ
^{2}) – 1] exp[2μ + σ^{2}]

_{t}] = 0 ; and σ

^{2}= var.[ε

_{t}].

_{t}] = exp[β

_{0}+ β

_{1}x

_{1t}+ β

_{2}x

_{2t}+ ……+ β

_{k}x

_{kt}+ (σ

^{2}/ 2)].

So, when we generate our predictions (“fitted values”) of y_{t}, based on our log-linear model, *really* we should create them as:

y_{t}* = exp{[log(y_{t})]* + ( s^{2} / 2)},

where

[log(y_{t})]* = [ b_{0} + b_{1}x_{1t} + … + b_{k}x_{kt }],

and s^{2} is the usual unbiased estimator of σ^{2}, based on the OLS estimates of the semi-log model.

To be more specific,

s^{2} = Σ[log(y_{t}) – b_{1} – b_{2}x_{2t} – ……. – b_{k}x_{kt}]^{2} / (n – k),

where the range of summation is for t = 1, 2, …., n.

The naive approach of generating the predictions simply as y_{t}* = exp[log(y_{t})]* ignores a term, and this will distort the predictions. In fact, it will distort them in a *downwards* direction, as the term we’re ignoring must be positive in sign. Of course, just how important this distortion is, in practice, will depend on the sale of our data, and “signal-to-noise” ratio for our model.

Let’s take a look at a couple of simple empirical examples, first using R, and then using EViews. (Jane has been encouraging me to be more “open” in my choice of software!)

The R example uses the well-known “Airplane Passengers” (AP) time-series, and is based loosely on the analysis of Cowperwait and Metcalf (2009, pp. 109-118). The R script is available on this blog’s **code page**, and it can be opened with any text editor. The logarithm of AP is regressed against a quadratic time trend and a bunch of Sine and Cosine terms of the form SIN(2πit) and COS(2πit); i = 1, 2, …, 5:

The time series “APF” is the series of naive within-sample predictions, obtained by simply exponentiating the fitted values for log(AP). The time-series “APFAD” incorporates the adjustment term discussed above. In this particular case, s = 0 04811, so there’s not much difference between APF and APFAD:

However, the sum of the squared (within-sample) prediction errors, based on AP and APF is 24936.24, while that based on AP and APFAD is 24840.21. So, there’s a bit of improvement when we take the adjustment into account.

Now let’s repeat this exercise using EViews. The associated workfile is on the **code page** for this blog, and the data are in a csv file on the **data page**. Here are the regression results using EViews:

** series apfad = apf*exp([email protected]^2) **,and here are the results:

There are several interesting questions that I haven’t dealt with here.

For example, what does the result, v = [exp(σ^{2}) – 1] exp[2μ + σ^{2}] , imply for possible modifications to the calculation of forecast *intervals*?

Second, what if the errors are *non-normal*? Can (how should) we modify our naive forecasts in such cases?

So many questions……. so little time!**Reference**Cowperwait, P. S. P. and A. V. Metcalf, 2009.

*Introductory Time Series With R*. Springer, Dordrecht.

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