Merge pull request #22 from QuantEcon/issue18

Issue18

Author: Alim-faraji

lectures/dynamic_programming/smoothing.md

@@ -62,7 +62,7 @@ But for each version of the consumption-smoothing model there is a natural tax-s
 * relabeling consumption as tax collections and nonfinancial income as government expenditures
 * relabeling the consumer's debt as the government's *assets*
 
-For elaborations on this theme, please see {doc}`Optimal Savings II: LQ Techniques <dynamic_programming/perm_income_cons>` and later parts of this lecture.
+For elaborations on this theme, please see {doc}`Optimal Savings II: LQ Techniques <perm_income_cons>` and later parts of this lecture.
 
 We'll consider two closely related alternative assumptions about the consumer's
 exogenous nonfinancial income process (or in the tax-smoothing
@@ -77,7 +77,7 @@ We'll spend most of this lecture studying the finite-state Markov specification,
 
 ### Relationship to Other Lectures
 
-This lecture can be viewed as a followup to {doc}`Optimal Savings II: LQ Techniques <dynamic_programming/perm_income_cons>` and  a warm up for a model of tax smoothing described in {doc}`opt_tax_recur <../dynamic_programming_squared/opt_tax_recur>`.
+This lecture can be viewed as a followup to {doc}`Optimal Savings II: LQ Techniques <perm_income_cons>` and  a warm up for a model of tax smoothing described in {doc}`opt_tax_recur <../dynamic_programming_squared/opt_tax_recur>`.
 
 Linear-quadratic versions of the Lucas-Stokey tax-smoothing model are described in {doc}`lqramsey <../dynamic_programming_squared/lqramsey>`.
 

lectures/more_julia/testing.md

@@ -535,7 +535,7 @@ By default, Travis will compile and test your project (i.e., "build" it) for new
 
 We can see ours by opening it in Atom
 
-```{code-block} julia
+```
 # Documentation: http://docs.travis-ci.com/user/languages/julia/
 language: julia
 os:

lectures/time_series_models/classical_filtering.md

@@ -389,7 +389,7 @@ Thus, we have
 \, X_t
 ```
 
-This formula is useful in solving stochastic versions of problem 1 of lecture {doc}`Classical Control with Linear Algebra <time_series_models/lu_tricks>` in which the randomness emerges because $\{a_t\}$ is a stochastic
+This formula is useful in solving stochastic versions of problem 1 of lecture {doc}`Classical Control with Linear Algebra <lu_tricks>` in which the randomness emerges because $\{a_t\}$ is a stochastic
 process.
 
 The problem is to maximize
@@ -575,7 +575,7 @@ component not in this space.
 
 ### Implementation
 
-Code that computes solutions to  LQ control and filtering problems  using the methods described here and in {doc}`Classical Control with Linear Algebra <time_series_models/lu_tricks>` can be found in the file [control_and_filter.jl](https://github.com/QuantEcon/QuantEcon.lectures.code/blob/master/lu_tricks/control_and_filter.jl).
+Code that computes solutions to  LQ control and filtering problems  using the methods described here and in {doc}`Classical Control with Linear Algebra <lu_tricks>` can be found in the file [control_and_filter.jl](https://github.com/QuantEcon/QuantEcon.lectures.code/blob/master/lu_tricks/control_and_filter.jl).
 
 Here's how it looks
 

lectures/tools_and_techniques/kalman.md

@@ -461,7 +461,7 @@ In this case, for any initial choice of $\Sigma_0$ that is both nonnegative and
 ```{index} single: Kalman Filter; Programming Implementation
 ```
 
-The [QuantEcon.jl]() package is able to implement the Kalman filter by using methods for the type `Kalman`
+The [QuantEcon.jl](http://quantecon.org/quantecon-jl) package is able to implement the Kalman filter by using methods for the type `Kalman`
 
 * Instance data consists of:
     * The parameters $A, G, Q, R$ of a given model

lectures/tools_and_techniques/numerical_linear_algebra.md

@@ -40,7 +40,7 @@ The methods in this section are called direct methods, and they are qualitativel
 The list of specialized packages for these tasks is enormous and growing, but some of the important organizations to
 look at are [JuliaMatrices](https://github.com/JuliaMatrices) , [JuliaSparse](https://github.com/JuliaSparse), and [JuliaMath](https://github.com/JuliaMath)
 
-*NOTE*: As this section uses advanced Julia techniques, you may wish to review multiple-dispatch and generic programming in  {doc}`introduction to types <../getting_starting_julia/introduction_to_types>`, and consider further study on {doc}`generic programming <../more_julia/generic_programming>`.
+*NOTE*: As this section uses advanced Julia techniques, you may wish to review multiple-dispatch and generic programming in  {doc}`introduction to types <../getting_started_julia/introduction_to_types>`, and consider further study on {doc}`generic programming <../more_julia/generic_programming>`.
 
 The theme of this lecture, and numerical linear algebra in general, comes down to three principles:
 
@@ -455,7 +455,7 @@ benchmark_solve(A_dense, b)
 
 ### QR Decomposition
 
-{ref}`Previously <qr_decomposition>`, we learned about applications of the QR decomposition to solving the linear least squares.
+Previously, we learned about applications of the QR decomposition to solving the linear least squares.
 
 While in principle the solution to the least-squares problem
 
@@ -468,7 +468,7 @@ is $x = (A'A)^{-1}A'b$, in practice note that $A'A$ becomes dense and calculatin
 The QR decomposition is a decomposition $A = Q R$ where $Q$ is an orthogonal matrix (i.e., $Q'Q = Q Q' = I$) and $R$ is
 an upper triangular matrix.
 
-Given the  {ref}`previous derivation <qr_decomposition>`, we showed that we can write the least-squares problem as
+Given the  previous derivation, we showed that we can write the least-squares problem as
 the solution to
 
 $$
@@ -567,7 +567,7 @@ Keep in mind that a real matrix may have complex eigenvalues and eigenvectors, s
 
 ## Continuous-Time Markov Chains (CTMCs)
 
-In the previous lecture on {doc}`discrete-time Markov chains <mc>`, we saw that the transition probability
+In the previous lecture on {doc}`discrete-time Markov chains <finite_markov>`, we saw that the transition probability
 between state $x$ and state $y$ was summarized by the matrix $P(x, y) := \mathbb P \{ X_{t+1} = y \,|\, X_t = x \}$.
 
 As a brief introduction to continuous time processes, consider the same state space as in the discrete