Wanna read a Forest Plot ? (Meta Analysis contd...)Applied Econometrics

■ What is a Forest Plot?

Simply, A forest plot is a graph that compares information many individual, clinical or scientific studies studying the same thing.

If you guys are well acquainted with the prior knowledge of Meta Analysis (mine earlier blogs) then lets state:

Say, there are studies such as Ram et al., Sita et al., Siva et al., Abdul et al., Hanuman etal., etc. So in the plot we will also have a Summary measure of all these along with the odds ratio (odds ratio will be discussed later in the Qualitative analysis).

Now you need to break the ice. Here you go,

The Realtive Risk Ratio say, 95% C.I visually displays the study results. The right column of the plot displays the same results expressed numerically.

The horizontal lines in the figure denotes the length of the Confidence Interval. The longer the line, the wider the C.I and as such the less reliable the results are.

If all the horizontal lines crossed the vertical line, it shows that all the studies were in agreement and if the horizontal line doesn't cross the vertical it's an indication that were statistically significant differences betweem the studies.

The boxes on the intervals represent the effect estimates from the single studies. Usually, the bigger the box, the larger the sample size as well as more meaningful story.

Now, an outline of a diamond at the base of the forest plot represents a weighted average for all the studies but may also be in Odds Ratio. The lateral tips of a diamond represnt the Confidence Interval.

If the diamond touches the vertical line then the overall meta analysis results is not statistically significant. If the diamond is left of the (dotted) line, then there is a lapse of two episodes of interest in the treatment group (treatment group discussed in previous blogs - in RCT).

Similarly, if the diamond is to the right, there are more episodes of the outcome in the treatment group.

** Summary**

a) Names to the left: 1st author of the rinary studies

b) Solid Sqaures: RR or OR of the individual studies.

c) Solid Sqaure Size: Weight of the Study

d) Horizontal lines: 95% of C.I

e) Vertical lines: Line of no effect

f) Diamond: Overall treatment effect

g) Tips of Diamond: 95% of C.I

Thank you

P.S Comments and Suggestions are highly lauded.

Aditya Raz Pokhrel

Assistant Director,NRB

MBA, MA Economics, MPA(rng)

Useful MCQ's for banking (Economics of Banking)

 ● MCQ's

1. The widest possible view of money is:

Ans: The Central Bank Approach (C + DD + TD + NBFI - CUA)

2. The function of money is not:

Ans: Stabilization of Price level.

3. What is a Fiat Money ?

Ans: Legal Money

4. BOE is:

Ans: Quasi Money/Near Money

5. When the value of the commodity = Value of the money its called:

Ans: Full boided Money

6. Keyensia concept of the money focuses on:

Ans: Store of the value.

7.  Father of modern quantity theory of money:

Ans: M. Friedman.

8. What is Pigou's cash balance equation ?

Ans: M = KR / P

9. Real Balance Effect is also given by:

Ans: A.C Pigou

10. Degree of the elasticity in respect to the speculative demand for money under liquidity trap is:

Ans: Infinite

11. Deflation benefits mostly:

Ans: Pensioners

12. Is "Security holdings" liability of a commercial bank ?

Ans: No

13. Which is the country that introduced the concept of CRR ?

Ans: USA

14. The deposit creation formula is:

Ans: 1 / CRR

15. What is TOT (Terms of Trade)

Ans: Ratio of, Index of Export Prices to Index of Import prices.

16. In BOP, what is the "Foreign Travel"

Ans: Invisible

17. Nepal's monetray policy is made on the basis of which International system?

Ans: Exchange Rate Anchor

18. What is the elasticity of demand for foreign exchange for financing capital outflow?

Ans: Zero

19. If the elasticity of foreign demand for the country's exports is unity, the supply curve of the foreign excahnge will be:

Ans: Vertical

20. Did "Mercantalists" favor free Trade ? 

Ans: No

Note: NBFI: Non Banking Financial Institutio.

           CUA: Credit from Unrecognised Agents

Plz share this...

Thank you

To be contd...

Aditya Pokhrel

Assistant Director, NRB

MBA,MA Economics,MPA

What's Vector Auto Regression (VAR)

 o VAR

Generally, VAR is considered as an extension of a single equation (ARDL) model which we will discuss later (in upcoming blogs).

VAR can be considered as the hybrid of the ARDL and the structured model (simultaneous equation models) and VAR is used profusely in Econometrics.

Its known to all that most of the econometric model are structural in nature (relationship between variable are determined by the economic theory).

There are many conditions that in which the economic theory won't be sufficient to determine the right model, i.e. the specification of the model should be based on the Econometric approach too. Now, there's a confusion about the correctness of the theory.

Say, the data, rather than the Econometricians specify the dynamic structure of the data. Let, the data determine what is the correct model. So, with this spirit, the VAR model was developed.

We will discuss clearly about the Simultaneous Equation Models (in upcoming blogs), now in the Simultaneous Equation Models, the classification of the endogenous and exogenous variables was arbitrary and identification is achieving by imposing "0" restrictions on the coefficients of the variables on the equation. These two decisions were subjective and this was criticized by "Sims".

According to Sims,. if there's two Simultaneity  between the variables they should be treated equally. There should not be distinction between endogenous and exogenous i.e. all the variables should be treated as endogenous and this spirit led "Sims" to develop a VAR  model.

In VAR thus, there's no distinction between the endogenous and exogenous variables (if included the model will will be biased).

A variable which is exogenous in one model is endogenous in the other:

In VAR model we have to specify two things (2 dimensions):

i) We have to select the variables supposed to be interacting with each other (m).

ii) Determine the largest number of the lags (to ensure that the entre relationships between these variables are captured into the model (p).

Say, that we consider the two variables, money supply and interest rates (Ms and r).

Now, in the VAR model Ms is related not only on the past values of the Ms but also on the past values of r. Similar case is with the "r" as well.

Now, we specify the VAR with 2 variables as :

Mst = alpha + Sum(j to k; j=1)beta j * Mt-j + Sum(j to k; j=1) gamma j * R t-j + U1t _________ eq (a)

Rt = alphaprime + Sum(j to k; j=1) theta j * Mt-j + Sum(j to k; j=1) lamda j * R t-j +U2t______ eq (b)

These two system of the equations are used in the context of the Granger Causality (will discuss later in coming blogs).

U1t and U2t are assumed to be uncorrelated and U1t and U2t are the impulses/innovations/shocks.

TO BE CONTD...

Aditya Pokhrel

MBA, MA Economics, MPA.