How do I efficiently get "the most recent corresponding row"?
It very much depends on circumstances and exact requirements. Consider my comment.
Simple solution
With DISTINCT ON in Postgres:
SELECT DISTINCT ON (i.good, i.the_date)
       i.the_date, p.the_date AS pricing_date, i.good, p.price
FROM   inventory  i
LEFT   JOIN price p ON i.good = p.good AND i.the_date &gt;= p.the_date
ORDER  BY i.good, i.the_date, p.the_date DESC;

Returned rows are ordered. See:

Select first row in each GROUP BY group?

Or with NOT EXISTS in standard SQL (works with every RDBMS I know):
SELECT i.the_date, p.the_date AS pricing_date, i.good, i.quantity, p.price
FROM   inventory  i
LEFT   JOIN price p ON p.good = i.good AND p.the_date &lt;= i.the_date
WHERE  NOT EXISTS (
   SELECT FROM price p1
   WHERE  p1.good = p.good
   AND    p1.the_date &lt;= i.the_date
   AND    p1.the_date &gt;  p.the_date
   );

Same result, but with arbitrary sort order - unless you add ORDER BY.
Depending on data distribution, exact requirements and indices, either one of these may be faster. See:

How do I (or can I) SELECT DISTINCT on multiple columns?

With only few rows per good, DISTINCT ON is typically faster and you get a sorted result on top of it. But for certain cases other query techniques are (much) faster, yet. See below.
Solutions with subqueries to compute max / min values are typically slower. Variants with CTEs are generally slower, yet. (CTEs improved with Postgres 12.)
Plain views (like proposed by another answer) do not help performance at all in Postgres.
db&lt;&gt;fiddle here
Old sqlfiddle
Proper solution
Strings and collation
First of all, your table layout is a sub-optimal. It may seem trivial, but normalizing your schema can go a long way.
Sorting by character types (text, varchar, ...) is done according to current COLLATION. Typically, your DB would use some local set of rules, like in my case: de_AT.UTF-8. Find out with:
SHOW lc_collate;

This makes sorting and index look-ups slower. The longer your strings (names of goods) the worse. If you do not actually care for collation rules in your output (or the sort order), this can be faster with COLLATE &quot;C&quot;:
SELECT DISTINCT ON (i.good COLLATE "C", i.the_date)
       i.the_date, p.the_date AS pricing_date, i.good, p.price
FROM   inventory  i
LEFT   JOIN price p ON i.good = p.good AND i.the_date >= p.the_date
ORDER  BY i.good COLLATE "C", i.the_date, p.the_date DESC;
Note the added collation in two places.
Twice as fast in my test with 20k rows each and very basic names ('good123').
Index
If your query is supposed to use an index, columns with character data have to use a matching collation (good in the example):
CREATE INDEX inventory_good_date_desc_collate_c_idx
ON price(good COLLATE "C", the_date DESC);
Read the last two chapters of the related answer I linked above.
You can even have multiple indexes with different collations on the same columns - if you also need goods sorted according to another (or the default) collation in other queries.
Normalize
Redundant strings (name of good) bloat tables and indexes, which makes everything slower. A proper table layout can avoid most of the problem. Could look like this:
CREATE TABLE good (
  good_id serial PRIMARY KEY
, good    text   NOT NULL
);

CREATE TABLE inventory (
  good_id  int  REFERENCES good (good_id)
, the_date date NOT NULL
, quantity int  NOT NULL
, PRIMARY KEY(good_id, the_date)
);

CREATE TABLE price (
  good_id  int     REFERENCES good (good_id)
, the_date date    NOT NULL
, price    numeric NOT NULL
, PRIMARY KEY(good_id, the_date));

The primary keys automatically provide (almost) all indices we need.
Depending on missing details, a multicolumn index on price with descending order on the second column may improve performance:
CREATE INDEX price_good_date_desc_idx ON price(good, the_date DESC);

Again, the collation must match your query (see above).
Since Postgres 9.2 &quot;covering indices&quot; for index-only scans can help some more - especially if tables hold additional columns, making the table substantially bigger than the index.
These resulting queries are much faster:
DISTINCT ON
SELECT DISTINCT ON (i.the_date)
       i.the_date, p.the_date AS pricing_date, g.good, i.quantity, p.price
FROM   inventory  i
JOIN   good       g USING (good_id)
LEFT   JOIN price p ON p.good_id = i.good_id AND p.the_date &lt;= i.the_date
ORDER  BY i.the_date, p.the_date DESC;

NOT EXISTS
SELECT i.the_date, p.the_date AS pricing_date, g.good, i.quantity, p.price
FROM   inventory  i
JOIN   good       g USING (good_id)
LEFT   JOIN price p ON p.good_id = i.good_id AND p.the_date &lt;= i.the_date
AND    NOT EXISTS (
   SELECT 1 FROM price p1
   WHERE  p1.good_id = p.good_id
   AND    p1.the_date &lt;= i.the_date
   AND    p1.the_date &gt;  p.the_date
   );

db&lt;&gt;fiddle here
OLD sqliddle

Faster solutions
If that still is not fast enough, there may be faster solutions.
Recursive CTE / JOIN LATERAL / correlated subquery
Especially for data distributions with many prices per good:

Optimize GROUP BY query to retrieve latest record per user

Materialized view
If you need to run this often and fast, I suggest you create a materialized view. I think it is safe to assume, that prices and inventories for past dates rarely change. Compute the result once and store a snapshot as materialized view.
Postgres 9.3+ has automated support for materialized views. You can easily implement a basic version in older versions.