Unbiased Predictor

Unbiased Predictor is based on the Market’s forward rate of that particular commodity. By market price, we mean the currency or cash price of a commodity.

In simple words, the theory of an unbiased predictor helps to spot prices at a future date and are predicted to be equal to the forward rates of the present day. Like anything else that depends on interest rate projections, the theory of an unbiased predictor can change significantly each time the Economic Conditions change.

However, the hypothesis of an unbiased predictor can work only in the absence of a risk premium. According to critics, unbiased expectations don’t occur in actual trading as per evidence.

Understanding Unbiased Predictor

Statistically speaking, “bias” refers to the variance between the actual Income and a prediction. In daily life, the term “bias” means there is a tendency to believe that some ideas, people, etc., are better than others. This often leads to the tendency to treat some individuals unfairly. In statistics, too, the words “bias” and “unbiased” carry similar meanings.

Therefore, we can safely say that an unbiased predictor can closely forecast the future behavior of a particular variable. If your statistic is not an overestimate or underestimate of a specific population parameter, then the concerned static is referred to as unbiased.

Unbiased Predictor Formula

For observations X = X1, X2, X3,……,Xn; on the Basis of distribution having the parameter value of Θ, for D(x) implying the estimator for h Θ, the bias serves to be the mean of the difference D(x) – h(Θ).

For instance, if we consider a futures contract as an unbiased predictor of the prices of oil, then after the contract expires, the price should match the anticipated price.

The theory of unbiased estimation is crucial to the point estimation theory, which refers to the use of sample data while calculating an approximate, single value to serve the “best estimate” or “best guess” of an undefined population parameter. The population mean can be a good example in this regard.

Unbiased estimation is essential in point estimation since, in the real-world, the unbiased predictor must have no systematical errors.

There are numerous ways to ensure that your statistics are unbiased and are reflecting the population parameter accurately. For example, you can take your sample data according to the standard and verified statistical practices. Also, try to avoid any measurement errors. Try to double-check and cross-check at every step to ensure that the data you collected is unbiased. Finally, avoid unrepresentative samples and make sure that you didn’t miss out on certain population members, including people working two jobs or the minorities.

What is an Unbiased Estimator?

It is a measure that helps to approximate a population parameter. If the estimator, or the sample mean is equal to the parameter, which is the population mean, then it is an unbiased estimator. The sample mean refers to a random variable that is used to estimate the population mean.

An unbiased estimator estimates the value of those parameters that are approximately correct. The expected value of a parameter in an unbiased estimator equals its true value.

For example, the sample mean can be called an unbiased estimator of the population mean. This is because the expected value of the population mean and the sample mean are equal. In brief, the sample mean refers to a random variable, which estimates the population mean.