Without no doubts, a researcher should several nice tools to handle their data, and testify their results. EXCEL, SPSS, and R represent different stages for doing a research.

1. EXCEL
Most data retrieved from a database has a excel format like xls or xlsx. We probably spend lots time on cleaning missing values and doing some simple valuable transformations with EXCEL.

2. SPSS
What is great about SPSS is that it provides the most direct interface for storing your raw data. Consider SPSS as a extension of EXCEL. It allows the definition of a variable could be stored directly in SPSS format dataset. This will be extremely useful if you are collecting data from surveys.

3. R
The preceding tools provide you with the basic method for analysing your data. On the other hand, R is a powerful statistical language,also suggested by an article from Nature, for analysing more difficult problems. Once you know how to write a function in R, it will save lots of time through avoiding clicking the buttons in the above tools. Data visualization for your data is also a great advantage of R especially using its package, ggplot2. See the examples from Rstudio.


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These concepts will involve the definition of technology, functional form, and other matters especially some important theories in micro-economics. These are the fundamental assumptions when developing the methodology of efficiency measurement. My advice is that read Intermediate Microeconomics_A Modern Approach (8th ed), (Ch. 18, 19, 20, 21) or Microeconomic Analysis (3rd) (Ch. 1~6, 12, 26, 27) and then study very carefully about how to estimate these functional forms especially considering their error sources (these are detailed in the book chapter, A Survey of Functional Forms in the Economic Analysis of Production.) If you want to know how to estimate these in practice, the lecture note, Introduction to Econometric Production Analysis with R, is really worth reading since it combines all R codes and data.

Other suggestions are also listed as follows. Carefully read these references in the following order (words in the parentheses indicate the suggested sections).

Production, cost, profit function
Intermediate Microeconomics_A Modern Approach (8th ed), (Ch. 18, 19, 20, 21)
Microeconomic Analysis (3rd) (Ch. 1~6, 12, 26, 27)
Microeconomic Theory (Ch. 5)
Applied production analysis: a dual approach (All)

Distance function
Multi-Output Production and Duality: Theory and Applications

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As a economist or a decision maker, we cannot avoid decision making, from whether expand the scale of a company to judge which firm is the most efficient one. The question is "is there any useful information for making these tough decisions?"

• Efficiency

See my previous post here.

• Scale

Whether a firm should expand its size or not usually depends on the possible cost induced by the further expansion. Consider a firm producing only one type of product. If the its single-product cost function could be depicted as follows, it is encouraged to expand its size since the unit cost is decreasing with the level of output due the spreading  the fixed cost over a large number of units of a type of product.       

• Scope

When a firm consider to produce one more type of output, economies of scope could be present if the total cost of joint production is less than the separate production.

this webpage perfectly explain the differences between economies of scale and scope.

• Cost-effectiveness ratio

This index is wildly used in the filed of health economics to help decide whether certain treatment is effective and cheaper. See O'Hagan's work for the framework.

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If you agree on the statement that science is based on the previous evidence and that evidence could affect the researchers' thoughts toward the next researches, it is not surprising that every study will involve some subjective judgement while conducting the researches. This is not the terrible part. The worse part of the judgement is that this could affect the estimation implicitly. In other words, we did not consider this judgement during building the model. Bayes theorem is what we need to include all the possible information including researcher's judgement into the model and then use the data we collected to update the model. This theorem is powerful but not valued as a essential tool for researches especially in the field of social science. The massive usage of probability and mathematics could be the causes. In this post, I would like to introduce some references that are extremely useful for the stepping stone of learning Bayes theorem. You don't have to read every book in the section. Instead, I encourage you to find every book in each section and try to find one  that best fits you.  For a reading list for programming in Bayesian estimation, read my another post here.

• Mathematics

1. Calculus and its applications
2. Fundamental Methods of Mathematical Economics

• Probability theory

1. Introduction to probability (by Blitzstein) 
2. Introduction to probability

• Bayes' Theory 

Bayes' theory would be a game changer for statistical estimations. It is not just a method for estimations but also a philosophy which fits our common sense toward the estimation.  Sooner or latter, most statistics textbook could be revised because of statistical concepts from Bayes' theory. Other estimation could be just a special case of Bayes' theory and this makes Bayes' Theory demand more backgrounds of math and probability. Therefore, before I provide the book list of Bayes' theory. I have to warn you that you would not even survive the chapter one if you do not have any concepts of calculus and probability. Thus, you should read the references I mentioned above, and then you can have fun and feel the power of Bayes' Theory through the following books I suggest.

1. Bayesian Analysis for the Social Sciences: (PPT of this book could be found in author's website) This book provides many useful plots to give you the most direct learning of Bayes' theory.  The author also offers clear proofs in the appendix. For readers who only want to realize the basics, you should at least read introduction and chapter one of this book. 
2. Bayesian Ideas and Data Analysis: This book has the goodness of explanation of the reasons for using different priors. Best of all, it provides detailed Winbugs and R codes in their website. 
3. Bayesian Econometrics: This book is very popular in the field of economics. I especially like that the author introduces the Bayes' theory by closely relating it to linear models which we are familiar with most. Since the author is one of the leading researcher in Bayesian efficiency estimation (see my another post), you will also benefit a lot from reading this book before you jump in that field. This author's another book,Bayesian Econometric Methods, is also worth reading. It also focus on the linear model but provides more detailed proofs and clearer math notation. Other short introduction of Bayesian methods in econometrics could be found in chapter 13, Microeconometrics Methods and Applications.    
4. Bayesian Data Analysis: Very well-known and wildly used for an Bayesian analysis. The first two edition uses Winbugs for demonstrating their Bayesian results while the latest edition use Stan instead. This book is not appropriate for beginners but it provides wide topics. Therefore, you should consider it as a very good model source. The best of it, the math notation is very close to the first book I recommended, so you save lots time when you finish reading the first one.   
5. Bayesian Inference: with ecological applications

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Efficiency estimation is to measure how efficient the decision making units are. The methodologies probably has been dominated by Stochastic frontier analysis (SFA, for parametric analysis) and Data envelopment analysis (for non-parametric analysis). For SFA, it is a branch of micro-economics and therefore you cannot avoid using some theories from micro-economics when measuring the efficiency. If you want to have relevant backgrounds regarding SFA, see my another post. In this article, I will try to recommend a reference list for efficiency estimation. This list will be appropriate for any researches regardless your research field.

Basic concepts
Performance Benchmarking_Measuring and Managing Performance
Introduction to Econometric Production Analysis with R
Benchmarking with DEA, SFA, and R

SFA
A Practitioner's Guide to Stochastic Frontier Analysis Using Stata

DEA
Data Envelopment Analysis: Modeling Operational Processes And Measuring Productivity 

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Yes, you can Google the key words to find the BUGS ( including WinBUGS and OpenBUGS) codes for your model but usually they will not work or you just can not understand what they are writing. You should firstly understand basic codes skill about BUGS:

1. Process for Building Bayesian Model
You could refer to the following picture.

Process for Building Bayesian Model 

see the example in the book,Applied Bayesian Statistics, pp.147

2. BUGS code structure
  "Data analysis using regression and multilevel/hierarchical models" (section 16.8, p.366)  gives excellent explanations about the BUGS code structure.  

3. How to perform the codes
"Bayesian Modeling Using WinBUGS" (chapter 3 and 4) gives clear pictures to teach you how to run your codes. 

4. Run BUGS through R
"Simple linear regression using R2OpenBUGS"
It is not easy to import your data in BUGS if your dataset is too big. Try to use R as a tool to manage data and deal with the MCMC results. This author offers a simple example (linear model) to teach you how to use these. If you want to totally include your BUGS code in R console, you should use sink and cat function , see this blog for a very vivid example. Other useful tricks for running BUGS within R could also found in book, Introduction to WinBUGS for Ecologists, (Appendix 1 - A List of WinBUGS Tricks).

5. Other special BUGS functions
Useful functions could be found here. More examples about using these should refer to The BUGS Book.


6. Bayes' Theory
I have gave a reading list for understanding this part in my another post. You should at least fully understand the first chapter of Bayesian Analysis for the Social Sciences.

Now you understand the basics, and you could try to search the codes about your interested models. The best and workable sources come from the books and journal articles.  Most authors below are very generous to provide the codes in their website. I will not provide these codes here but will suggest the references for you.

1. Efficiency estimation
(1)Stochastic frontier model
"Bayesian stochastic frontier analysis using WinBUGS"
The author (Professor Jim Griffin) provides the WinBUGS code in his website.
I depict their data structure as follows and you should also read their paper on page 165.

2. Panel data analysis
"Introduction to applied Bayesian statistics and estimation for social scientists" (ch.9)
Relevant theory of Bayesian panel data model could refer to Bayesian Econometrics (ch.7), Panel Data Modeling and Inference: A Bayesian Primer, Introduction to Modern Bayesian Econometrics (ch.7)
For specific panel data analysis demonstration:

• random coefficient model or varying-coefficients model: Bayesian Econometric Methods (ch. 12)

3. Multilevel/hierarchical models
"Data analysis using regression and multilevel/hierarchical models" especially chapter 17, p.305
"Applied Bayesian Hierarchical Methods"
"Scope economies in Australian distance education"

4. Econometrics
"An introduction to modern Bayesian Econometrics"

5. Rasch (IRT) Model
"Bayesian Estimation for the Rasch Model using WinBUGS"

6. Random Coefficient Dynamic Factor Models
"Bayesian Estimation of Random Coefficient Dynamic Factor Models"

7. Categorical models
Bayesian Models for Categorical Data, codes

8. Latent Growth Model
latent basis growth curve model with varying residual variances
latent basis growth curve model

9. Prediction
Linear regression: Bayesian Ideas and Data Analysis p. 241
Other models
"The BUGS Book"
"Bayesian modeling using WinBUGS"





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