Titlebook: Quantitative Economics with R; A Data Science Appro Vikram Dayal Textbook 2020 Springer Nature Singapore Pte Ltd. 2020 R.Time Series Data.C

內(nèi)疚

IntroductionThe reasons for a data science approach are briefly discussed. An overview of the book is presented, followed by a brief explanation of key elements of R code.

Cognizance

Carminative

Wrangling and Graphing DataFirst, we see how graphs can reveal with the Anscombe data. Then the relationship between carbon and livelihoods is explored. Last, we use the WDI package to access data.

coltish

FunctionsTo begin we see how we can make our own simple functions in R. We then plot functions quickly with R’s curve function. We consider supply and demand and the Cobb–Douglas production function.

提名

Difference EquationsWe examine how a variable changes over time with a simple example of a difference equation. We see how we can simulate the values of the variable over time and plot the values. We see difference equations related to carbon stocks, fishing and stock pollutants.

輕浮思想

Cabinet

Statistical InferenceWe use box models to illustrate statistical significance. We use simulation to understand sampling distributions and confidence intervals. We then look at simulation-based methods for statistical inference—the bootstrap and permutation tests.

CEDE

Causal InferenceSimulation is used to illuminate causal inference. We begin with a short look at causal graphs and potential outcomes. We then aim to understand and see examples of experiments, regression adjustment, matching and sensitivity analysis, regression discontinuity, difference-in-difference, Manski bounds and instrumental variables.

膽小鬼

Cultivate

Growth CausesWe replicate an important paper regarding the causal effect of institutions on growth. We then consider the relationship between geography and growth. We briefly consider the issue of testing the exclusion restriction.