econometrics of life

Making big decisions in life is sometimes require courage, particularly if it will affect our life in long term (e.g. Choosing education, career). It may be different than short term, daily decisions, which outcome will cointegrate in the long term. If errors exist, we can simply hope that it will back to normal tomorrow, and offset today's bad experience. If dynamic forecasting is too risky, we can simply use static projections, by updating the data and reestimate the model frequently.
In econometrics, short term forecasting is often fit well by simple autoregressive models, that only require historical data of variable projected. But for long term, advanced theory and more complex methods are often required. we also have to use many exogenous and instrumental variables, together with assumptions and scenarios. Therefore, it require much more information.
So what it has to be in the real life? To choose a career, we have to collect large scale of historical information about story of others, including exogenous variables such as particular individual characteristics, and control variables such as social acceptance and support. We have to make assumptions or scenarios to set values of exogenous shocks. Indeed, once assumptions are flawed or scenario we choose is misleading, we will face unexpected future. Nevertheless, information between sets of choices are often unbalanced.
Suppose there are two long term choices, let's consider them equivalent to long maturity assets that have very high transaction costs. One has sufficient number of observations and known exogenous data, while the other has not, but shows higher return in that small number of observations. Which asset should we choose?

Marriage Model

In this paper I estimate empirical marriage model simultaneously considering endogenous regression and model uncertainty. I apply Bayesian methods with love as the prior and marriage as the posterior. The likelihood function assumed to follow binomial distribution (1: married, 0: not-married). The resulting maximum likelihood estimator for the probability of a man getting married to someone that he loves is 73.49%. On the other hand, the probability of a woman getting married to someone that she loves is only 23.12%. (Forthcoming: Journal of Economics of Love, No. x, 201x)

Least Square and WOC

As I posted here many moths ago, WOC roughly states that the best guess to predict a random number is the average of the distribution of this number.

As Ordinary Least Square (OLS) is one of the estimation methods to predict the value of say a dependent variable Y, then it must be correct to state that the OLS is a better method if “it can predicts (explains) the value of Yi better than could be explained by the sample mean (Ybar)”(Studenmund)

In short, we can see this comparison by the formula of the “Explained Sum Square” (ESS) in OLS method which is

So, it is clear now that to evaluate every prediction (forecasting) methods, it is always and should be compared to the WOC’s prediction.

Berapa Nilai Tukar USD-Rp jika Cadangan Devisa 100 Milyar USD - V.2

Regresi Pertama.



Regresi Kedua.



Tabel Prediksi




Demikianlah hasil perhitungan dari penulis. Sangat diharapkan pendapat dan masukkannya. Trims.